Monetary Policy and Financial Stability

The issue of using monetary policy for financial stability purposes is hotly contested. The crisis was a reminder that price stability is not sufficient for financial stability, financial crises are costly, and policy should aim to decrease the likelihood of crises, not only rely on dealing with their repercussions once they occur. It is clear that well-targeted prudential policies (including micro and macroprudential regulation and supervision) should be pursued actively to attenuate the buildup of financial risks. The question is whether monetary policy should be altered to contain financial stability risks. Should it lend a hand by temporarily raising interest rates more than warranted by price and output stability objectives? Keeping rates persistently higher is also possible, but more costly.

Abstract

The issue of using monetary policy for financial stability purposes is hotly contested. The crisis was a reminder that price stability is not sufficient for financial stability, financial crises are costly, and policy should aim to decrease the likelihood of crises, not only rely on dealing with their repercussions once they occur. It is clear that well-targeted prudential policies (including micro and macroprudential regulation and supervision) should be pursued actively to attenuate the buildup of financial risks. The question is whether monetary policy should be altered to contain financial stability risks. Should it lend a hand by temporarily raising interest rates more than warranted by price and output stability objectives? Keeping rates persistently higher is also possible, but more costly.

Introduction and Motivation

1. Before the global financial crisis, a widespread consensus supported a strict division of labor between different policy levers. Price stability was the primary—and sometimes sole—mandate of monetary policy. Financial stability was the realm of prudential regulation and supervision (often managed by agencies separate from the central bank). This framework found an intellectual foundation in New Keynesian models, which implied that—under broad conditions—price stability would keep output around its natural level.

2. As a result, most central banks took a “benign neglect” approach to asset price and credit booms. Monetary policy was to react to movements in asset prices and credit aggregates only to the extent that they affected inflation (and output).1 This was reinforced by a belief that it was too difficult to distinguish fundamental-driven movements from speculative bubbles in real time. And, in any event, the policy rate was too coarse an instrument to address the associated financial risks. If monetary policy had a role, it was to respond to the macroeconomic consequences of financial instability, if and when it materialized. This debate is often summarized by the phrase “lean versus clean.”2

3. Policy makers recognized the dangers associated with financial imbalances. Indeed, central banks tended to follow the consensus framework with some flexibility. In many emerging markets, concerns about financial imbalances (for instance, large foreign-exchange exposures or fast credit growth) weighed significantly on monetary policy decisions. But in most advanced economies (with Australia, Norway, and Sweden as notable exceptions) preserving financial stability was solely the job of prudential policy. Financial regulation and supervision were, however, predominantly focused on the stability of individual institutions, with relatively little attention to the stability of the financial system as whole, thus leaving an important gap in the overall policy framework. Furthermore, regulation and supervision of individual institutions was deficient.

4. In the run-up to the crisis, financial stability risks grew, largely undetected, beneath the surface of seemingly close-to-target inflation and output gaps. There was a sharp increase in the ratio of credit to GDP and in real estate prices—two important measures of financial vulnerabilities (Figure 1, where output gaps are estimated based solely on information available before the crisis). Some have argued that central bank policies during this period raised incentives for risk taking, as rewards could be appropriated by individuals, while costs would be alleviated by swift policy reaction and borne by the public.3

Figure 1.
Figure 1.

Output Gaps, Core Inflation, and Financial Indicators Before the Crisis

Citation: Policy Papers 2015, 045; 10.5089/9781498344265.007.A001

Source: World Economic Outlook (September 2007 vintage for the output gap) and Haver Analytics.

5. Contrary to pre-crisis beliefs, the costs of cleaning up after the crisis proved to be very large, especially for those countries where financial imbalances had grown the most. There was a remarkable correlation between the two measures of financial vulnerabilities just mentioned—pre-crisis credit growth and housing price appreciation—and the drop in GDP and rise in household loan delinquencies in the two years following the global financial crisis (Figure 2). This is consistent with the findings of a large literature outside of mainstream macroeconomics that links financial fragility with poor macroeconomic performance. For instance, there is evidence that financial crises are deeper and more persistent than normal recessions. In advanced and emerging market economies after World War II, financial crises have on average led to negative GDP growth for two years, with a peak loss of GDP per capita of about 1.5 percent. In these crises, GDP has lagged behind its average recovery path after normal recessions by about 4 to 5 percent after five years.4 Moreover, crises typically undermine countries’ fiscal positions, as well as social and political stability and cohesion.

Figure 2.
Figure 2.

Growing Financial Vulnerabilities and Costs of the Crisis

Citation: Policy Papers 2015, 045; 10.5089/9781498344265.007.A001

Sources: IMF International Financial Statistics; IMF staff calculations.Note: Each data point corresponds to a country, indicated by the three-letter abbreviations.Sources: Federal Housing Finance Agency, Mortgage Bankers Association, Bureau of Economic Analysis, U.S. Census Bureau.Note: Each data point corresponds to a U.S. state, indicated by the two-letter abbreviations.

6. The severity of such crises required extraordinary monetary policy accommodation, all the more so when fiscal policy was constrained by high and rising public debt burdens. In many advanced economies, short-term nominal rates quickly ran into the effective lower bound, and large-scale unconventional monetary policies (UMP) were required. Despite their overall effectiveness, UMP were difficult to fine-tune, and their implications for future financial stability and cross-border spillovers are yet to be fully understood.5

7. This has rekindled the debate on “lean versus clean.” Many policy makers now recognize the need to mitigate crisis risk proactively, rather than only relying on cleaning up after a crisis. On the monetary policy front, price stability is no longer believed to be sufficient to ensure macroeconomic stability. And, on the prudential front, the emphasis has shifted to containing systemic risk by complementing traditional microprudential policies aimed at individual institutions with macroprudential policy frameworks, as recommended in Viñals (2013), and IMF (2013f, and 2014c). Examples of the latter include both cyclical instruments (e.g., countercyclical capital buffers, loan-to-value limits, or dynamic loss provisioning) and permanent measures to strengthen the structural resilience of the financial system. But there is still concern that even the stronger emerging combination of micro- and macroprudential policies may not suffice to contain financial stability risks.6

8. If that were the case, should monetary policy lend a hand—by pursing a financial stability objective in addition to its primary mandate of price stability? This question is central to this paper, and the answer potentially affects all countries with developed financial systems (and sufficiently high probabilities of crisis), effective and independent monetary policy, as well as rapidly evolving prudential policies. As such, this paper is more appropriate for advanced and emerging economies with freely floating exchange rates, than low-income countries, or countries with exchange rate pegs or other constraints on monetary policy.7 The question tackled here has implications for the conduct of monetary policy in normal times, not just in rare crisis periods. And by reopening a debate on established monetary policy frameworks, it raises sensitive political and institutional issues.

9. Two approaches are possible to using monetary policy. The first involves responding by keeping nominal interest rates persistently higher than implied by a traditional reaction function focused only on inflation and output stability. In other words, interest rates would be raised a bit more and faster in the upswing, and lowered a bit less and more slowly in the downswing. The second involves responding occasionally by “leaning against the wind” as needed to counter evolving financial risks.8 The latter could be part of a state contingent rule-based approach; for instance, one in which deviations from a traditional inflation targeting framework were guided by previously identified financial indicators (for example, credit growth, leverage, and others). This paper—as most of the policy debate—focuses on the second approach. The first does not seem especially promising. Higher rates would create persistently lower inflation. This would eventually decrease inflation expectations, and in the end leave real rates—and thus financial risks—unchanged, while aggravating risks of hitting the zero lower bound.9 The second approach might be difficult to communicate in the light of long and variable lags. However, it is more consistent with the view that financial risks evolve over time, and that in some cases—as discussed later—price and financial stability will require the same policy reaction.

10. Not surprisingly, given the limited empirical evidence and the lack of an accepted theoretical framework, the question of leaning against the wind is hotly contested. Influential economists and policymakers espouse very different views, some praising the virtues of monetary policy to affect lending and potentially risk-taking behavior in all markets, others underscoring the risks and costs of using one instrument for two targets. John Williams, president of the San Francisco Federal Reserve, represents one side of the debate: “monetary policy is poorly suited for dealing with financial stability concerns, even as a last resort.” Oystein Olsen, Governor of the Norges Bank—Norway’s central bank—epitomizes the other side: “we have been leaning against the wind.” As a result, the Norges bank publishes interest rate forecasts that respond to risks of financial imbalances, in its Monetary Policy Report. Janet Yellen, the Chair of the Board of Governors of the U.S. Federal Reserve Board, sees valid arguments on both sides: “Monetary policy faces significant limitations as a tool to promote financial stability… [However,] it may be appropriate to adjust monetary policy to “get in the cracks” that persist in the macroprudential framework.”10

11. The Bank for International Settlements (BIS) has also been a prominent contributor to this debate, expressing support for a stronger role for monetary policy in maintaining financial stability: “Financial stability is too large a task for prudential […] frameworks alone. Monetary policy strategies also need to […] lean against the build-up of financial imbalances even if near-term inflation remains low and stable.” The BIS argues in favor of higher interest rates for extended periods, as “financial imbalances can build up gradually, over many years […]. If central banks are to counteract such build-ups, they will need longer policy horizons.” These, argues the BIS, grow over a “financial cycle,” lasting longer than the business cycle (Caruana 2011, and Box 1).

Cycles in Financial Variables: Concepts and Measurement1

The term “financial cycle” is widely used. It has regained particular prominence in recent BIS analysis where it is said to denote “self-reinforcing interactions between perceptions of value and risk, attitudes towards risk and financing constraints, which translate into booms followed by busts (Borio 2014).” The term loosely refers to booms and busts in financial markets, whose fluctuations can amplify swings in business cycles. Financial cycles are generally considered to include credit and property cycles, given the relatively high synchronization between the two and their more direct effect on the real economy through wealth effects (Schularick & Taylor, 2009). Recently, another strand of analysis has focused on the common factors that drive global financial cycles, spurred by large cross-border capital flows and financial integration (Adrian & Shin, 2012; Rey, 2015).

The interaction of financial and real shocks and their dynamic properties is being explored. A large literature focuses on the role of credit on growth, including the impact of “credit crunches” or “creditless recoveries” (Abiad, Dell’Ariccia, and Li, 2011). Others focus on the impact of shocks to balance sheets—changes in asset prices or economic prospects, amplified through “financial accelerators”—on macroeconomic fluctuations (Bernanke and Gertler, 1989; Bernanke et al. 1996; Kiyotaki and Moore 1997). Still others focus on endogenous leverage cycles$fluctuations in leverage driven by changes in assets prices, or uncertainly about their revenue distribution—that can produce large changes in collateral requirements and hence credit conditions (Geanakoplos 2010; Adrian and Shin 2012). Overall, there is a sense that shocks may propagate differently to financial indicators versus real variables, leading them to exhibit different cyclical properties. BIS staff in particular argue that peaks in financial cycles—filtered by them at very low frequencies—exhibit a close association with systemic banking crises; thus longer financial cycles offer greater insight into the interaction between financial and business cycles. In this vein, the deviations between financial and business cycles can be large and long-lasting (Borio, 2014). But this notion, much like the definition of “financial cycle” itself, has yet to find consensus.

Measurement. One typical approach to measure financial cycles is to rely on a commonly used algorithm for business cycle identification that locates peaks and troughs (Claessens et al. 2011; Drehmann et al., 2012; BIS, 2014; ECB, 2014), which is used also in this paper. Trends and gaps are then identified as in Borio and Drehmann (2009) and Cervantes and Krznar (2014).2 Credit cycles tend to be highly correlated with GDP, with similar durations of about five years, while property cycles tend to be shorter.

Policy relevance. The key concern that underlies this research agenda is the impact of financial variables on the economy. There is a wide range of views on channels, impacts, and timing. But the definition of “financial cycle” must consider the policy objective and analytical focus—i.e., crisis prediction, propagation channels, or others. The exact empirical features and stability of financial cycles are not yet established. Against such uncertainty, a prudent approach is to consider a range of indicators and be alert to early signs of a pickup in trends of and widening gaps in financial variables.

1 The authors of this box are Sally Chen, Katsiaryna Svirydzenka, Edouard Vidon, and Aleksandra Zdzienicka.2 The turning point algorithm (Harding and Pagan, 2002) is first used to estimate the average length of cycles in financial variables across countries and a band-pass filter (Christiano and Fitzgerald, 1999) is then applied to estimate gaps (deviations from trend).

12. The debate has taken on added urgency in the current economic environment. Tensions between price and financial stability mandates have emerged in several advanced economies (AEs) that still face considerable slack in the economy and low inflation.11 Meanwhile, these countries have seen a mix of rising house or other asset prices, and credit growth (Appendix I provides an in-depth survey of various countries’ macroeconomic context, financial risks, and current policy debates). In emerging market economies (EMs), these tensions between policy objectives are currently not so marked:

  • In Sweden, Switzerland, and the United Kingdom (U.K.), house prices and credit growth are either accelerating or still increasing, sometimes from already elevated levels.

  • In Australia and Canada, house prices are rising despite moderate credit growth.

  • In the Netherlands and Norway, house prices and credit have decreased, but are recently recovering.

  • In contrast, in the United States (U.S.), after a sharp correction in house prices and subdued credit growth, a range of asset markets other than housing show signs of stretched valuations.12

13. This paper aims to bring some clarity to these issues. While it cannot provide final answers, it aims to help policymakers assess the value and implications of using monetary policy to support financial stability. It does so in three ways: by providing a framework to conceptualize and clarify the channels of transmission and policy tradeoffs, advancing initial policy guidance based on the most recent empirical findings, and emphasizing the gaps that still need to be filled before more definitive policy advice can be formulated. Put simply, the paper asks: “what do we know and can be quantified, what should we do based on what we know, and how much do we really know?”

14. The paper is divided into five sections. The first provides the policy context, conceptual underpinnings and definitions. The second reviews the available empirical estimates of the relationship between interest rates and financial variables, and ultimately with the macroeconomy. The third, discusses tradeoffs between price and financial stability. The fourth builds on these findings by evaluating the welfare implications of using monetary policy to support financial stability. The fifth section provides some additional discussion, including of open economy implications. Finally, the sixth section considers the implementation issues, and the last offers some concluding thoughts.

The Policy Context and Definitions

15. The lesson from recent years is that policy should aim to decrease the likelihood of crises, not only rely on dealing with their repercussions; the question is how to do so. In particular, what is the role for monetary policy? Answering this question leads policy makers to contemplate two dimensions of policy: one is cyclical, the other structural. The first adapts to the conjuncture and may target a specific source of risk. The second remains unchanged over time, aiming to support the resilience of the financial system to a wide set of shocks. Such policies rely on capital and liquidity requirements, as well as limits on exposure to foreign exchange, or redemption risks. Recent work suggests that strong policies aimed at structural stability can materially improve systemic resilience. 13 In these cases, the burden on cyclical policies—including on monetary policy—to support financial stability could be lighter. Given this paper’s focus on monetary policy, though, the emphasis here will be on cyclical policies. Setting policy over the cyclical dimension involves answering a series of questions, illustrated in Figure 3 (that offers only a stylized snap-shot of what is actually a repeated decision making process with substantial uncertainty at each node).

Figure 3.
Figure 3.

Dealing With Financial Stability Over the Cyclical Dimension: A Decision Tree

Citation: Policy Papers 2015, 045; 10.5089/9781498344265.007.A001

Source: IMF staff.

16. The first question is: are financial risks excessive? Financial risks capture the likelihood of large disturbances to future macroeconomic conditions originating in financial variables. Variables such as asset prices can be a direct source of shocks, for instance through a large drop in prices. Other variables, such as leverage or debt of financial firms, household and corporates, tend to amplify other shocks through financial distortions. Such distortions include, for instance, the relationship between asset prices and credit growth, whereby higher asset prices allow borrowers to pledge more collateral and thus increase debt, until a shock forces them to deleverage rapidly, with potential externalities on other debtors.14 Estimating financial risks is not easy.15 At the end of the day, determining whether risks are excessive will have to rest on a socially agreed maximum for the probability and severity of large disturbances to macroeconomic conditions. This is similar to defining an inflation target for monetary policy.

17. The second question is whether other policies—in particular macroprudential policies—can address financial risks, when they are excessive. Macroprudential policies offer the hope of targeting specific sources of vulnerabilities, whether they arise from exuberance in a particular sector, or specific financial distortion affecting multiple sectors. And, as discussed in IMF 2013e and Blanchard, Dell’Ariccia, and Mauro, 2010, the policy burden should fall primarily on these measures, should they prove both well targeted and effective. However, empirical evidence as to their effectiveness remains slim and scattered though is growing quickly.16 Other policies should also be considered for their effect on financial stability and their interaction with monetary policy. More expansionary fiscal policy, for instance, can lead to the buildup of sovereign risk and counter the impact of higher interest rates targeted at reducing financial stability risks.

18. The third question, to be answered in parallel with the second, is whether tighter monetary policy warranted by price stability is also sufficient for financial stability. Financial risks commonly grow in periods of economic expansion in which inflation pressures build up, and output growth is sustained. In these periods, interest rates should be tightened for price stability regardless of financial stability concerns. However, higher rates may well, as a byproduct, also stabilize the financial system. Cases in which financial stability risks are sufficiently contained as a result of higher rates are said to induce “no tradeoffs” between price and financial stability objectives.17

19. If there is a tradeoff, and if prudential policy is not sufficiently effective, a final question emerges: should monetary policy lean against the wind? Tradeoffs can arise starkly when there is no economic expansion yet growing financial risks, but also more subtly when financial risks warrant a greater interest rate hike than necessary to tame prices. The question of leaning against the wind while there are tradeoffs is the main focus of the paper, as it provides a clear test of the issues involved than the (more typical) case where rising financial risks are accompanied by a strong economic expansion. The paper does not explore the tradeoffs and interactions between monetary and prudential policies in details. Instead, the paper assumes that prudential policy, including policy aimed at structural resilience, cannot stabilize the financial sector completely, so some financial risks remain. In principle, to the extent structural policies are able to sufficiently increase resilience of the financial system on their own, there could be less of a role for monetary policy as well as cyclical macroprudential policies. These interactions across different policies—and their welfare implications—are an important area for further study. The focus of this paper remains on documenting and quantifying the link between monetary policy and financial stability.

20. Evaluating the policy of leaning against the wind requires three broad steps:

  • First, the transmission channels need to be described. This involves estimating the links between policy interest rates and financial risks.

  • Second, one needs to establish the nature and size of the tradeoff between stabilizing inflation and financial risks. As discussed above, if there is no tradeoff, the argument for leaning against the wind would become trivial, as pursuing price or financial stability would be one and the same.

  • Third, the welfare implications of leaning against the wind need to be assessed in the context of a clear framework, allowing for a cost-benefit analysis.

Step 1: Transmission

21. The transmission between monetary policy and financial stability entails two links. The first is between interest rates and key financial variables. And the second is the relation between these financial variables and the probability of large disturbances to macroeconomic conditions. This is as illustrated in Figure 4.

Figure 4.
Figure 4.

From Interest Rates to Macroeconomic Conditions

Citation: Policy Papers 2015, 045; 10.5089/9781498344265.007.A001

Source: IMF staff.

A. Interest Rates and Financial Variables

22. This paper focuses on five financial variables. The first three measure quantities (leverage of financial firms, household debt, bank risk taking), and the last two prices (of assets—especially real estate—and credit spreads). These variables have received attention in the literature on the relation between monetary policy and financial vulnerabilities, and on crisis prediction. Other variables may also be important, notably loss absorption capacity, liquidity, maturity, and FX balance sheet mismatches, but data are often weak, and the literature not as conclusive.

23. Interest rates can affect each of these financial variables. Effects change—and can even reverse—depending on the time horizon of the analysis, as well as initial conditions.

  • In the short term, before agents are able to adjust their balance sheets, theory suggests that higher interest rates are likely to weaken financial stability. First, by reducing aggregate demand, a monetary tightening reduces household earnings and firms’ profitability. Second, it leads to an increase in the interest rate burden, especially if liabilities are at variable rates and have short maturities. Finally, it tends to reduce asset prices and the value of legacy assets held by financial institutions. These effects weaken the financial conditions of households and firms, possibly leading to a temporary increase in delinquencies and defaults especially if balance sheets are weak to begin with.

  • In the medium term, however, these effects are likely to reverse as households, firms and financial institutions rebalance their balance sheets and adapt their behavior. In particular, higher borrowing costs should induce households and firms to gradually reduce leverage through the conventional intertemporal substitution effect. Tighter monetary conditions are likely to gradually reduce leverage also in the banking sector, as shown in Dell’Ariccia, Laeven and Marquez (2014). The effects on risk taking are instead less clear-cut. By reducing search-for-yield motives, higher rates should reduce risk taking by financial intermediaries with fixed long-term liabilities, such as insurers and pension funds. The response of banks is instead ambiguous: higher funding costs that compress intermediation margins should lower the incentive for monitoring, but lower leverage should induce banks to behave more prudently.

24. Empirical results broadly support these theoretical predictions. The papers discussed below investigate the effects of monetary policy on financial variables. Findings are new and separate from a slightly older literature that investigates the effects of financial variables on the transmission of monetary policy.18

  • Real debt levels generally decrease following a temporary monetary policy tightening of 100 basis points, by up to 0.3 percent and 2 percent, after 4 to 16 quarters, depending on the model.19

  • However, in the short term, real debt to GDP seems to rise.20 The intuition is that nominal GDP responds faster than nominal debt to an interest rate hike, especially with lengthy loan amortization periods.

  • Because real debt and debt servicing costs increase with higher interest rates, default rates rise in the quarters following an interest rate shock (Box 3).21

  • Banks and nonbanks generally respond to higher interest rates by reducing their leverage, though after 1–2 quarters (Box 4).22 In the initial quarters, leverage tends to rise across financial firms.

  • Higher interest rates seem to induce banks to tighten their lending standards, grant fewer loans to risky firms, and extend less risky new loans.23 The economic significance of these effects is difficult to quantify as results are based on survey data. One measure is that Sharpe ratios (a measure of riskiness of assets) of financial firms—both banks and nonbanks—decrease somewhat following protracted rate cuts.24 The implication is that Sharpe ratios would increase (lower financial risks) following interest rate hikes.

  • A very different approach confirms that banks (primarily, but also nonbanks to some extent) are perceived to hold a less risky portfolio after an interest rate hike. However, once again, these are medium-term effects. In the short term, banks become riskier. These results follow from tracking banks’ distance to default, a market-based measure of expected default based on balance sheet data and equity prices. Some papers find that distance to default eventually rises (lower riskiness), as interest rates are increased. However, in the short term, distance to default decreases (higher riskiness) and can reach levels commensurate with past crises. This is especially true when the rate hike occurs in periods of financial stress, underscoring that effects of monetary policy on risk taking behavior are state-contingent. More details are offered in Box 5.25

  • Real estate prices decrease on average following a hike in interest rates. The effect is of the order of 2 percent, following a 100 bps interest rate shock, though after a significant lag, of between 10 to 16 quarters.26

  • Credit spreads tend to increase following an interest rate hike.27 As discussed in detail later, higher spreads are symptomatic of lower risk-taking behavior and correlated to higher future output growth.

The Effect of Monetary Policy on Default Rates1

This box investigates the effects of monetary policy on loan delinquencies. There are two advantages to this approach. Delinquencies capture an alternative measure of households’ debt servicing capacity, but also of the effects of leaning against the wind. Loan delinquencies may represent a direct cost of increasing rates in the short-term, but also longer-term benefits if households curtail their credit and leverage to the point where defaults are less likely in the future. This approach differs from that in the paper, in which the relevance of a change in a financial variable—such as real household credit—only matters indirectly to the extent it contributes to the probability of a crisis.

The main takeaway is that higher interest rates have a negative effect on financial stability in the short run, but may have positive effects in the long run. In the U.S. and Spain, loan delinquencies first increase; this is consistent with lower economic activity, lower house prices and higher interest payments after a monetary contraction. Household leverage ratios eventually improve after several quarters. This reduces default rates (only in the U.S. but not in Spain) and enhances financial stability in the long run.2

Short vs. long-term tradeoffs are evident in the U.S. A standard VAR model is estimated with real GDP, core PCE, the Federal Funds Rate, real house prices, household leverage and delinquency rates, including four lags, for the period 1988Q1 to 2015Q1. The monetary policy shock is identified with a standard Cholesky ordering. After a monetary policy shock, real GDP and real house prices decline and the response of core PCE is not significant. Initially, default rates on residential loans slowly increase, peaking at 0.37 percent after 14 quarters (Figure 3.1). However, they decline over the long-term to 0.31 percent after 36 quarters. The reaction of household leverage (measured from the Flow of Funds data) is not significantly different from zero for the first 25 quarters, and becomes negative afterwards.3

Figure 3.1.
Figure 3.1.

Effects of a Monetary Policy Shock, U.S.

Citation: Policy Papers 2015, 045; 10.5089/9781498344265.007.A001

Sources: BEA, Federal Reserve, OECD, and IMF staff estimates.

In Spain, an increase in the 12-month EURIBOR leads to an increase in residential loan default rates.4 Household leverage first increase and then declines significantly. Residential nonperforming loans (NPLs) increase much faster but the response is smaller than in the U.S., peaking after 6 quarters at 0.08 percent (Figure 3.2). The differences between the two countries could be due to: (i) a larger share of variable-rate mortgages in Spain, such that the transmission of interest rate shocks is faster; and (ii) widespread use of non-recourse mortgages, reducing the incentive to default. A recent paper by González-Aguado and Suárez (2014) finds that the response to interest-rate changes is asymmetric and heterogeneously distributed across firms. Interest-rate rises tend to produce a temporary rise in default rates, while lower interest rates may also lead to higher leverage, making some firms riskier.

Figure 3.2
Figure 3.2

Effect of an Interest Rate Shock, Spain

Citation: Policy Papers 2015, 045; 10.5089/9781498344265.007.A001

Sources: EROSTAT, Banco de España, and IMF staff estimates.
1 The author of this box is Pau Rabanal (RES).2 Loan delinquency rates for the U.S. are obtained from the Federal Financial Institutions Examination Council, FRB Call Report, Board of Governors of the Federal Reserve System. Delinquent loans include those past due 30 days or more and still accruing interest, as well as those on nonaccrual status. Spanish data are doubtful (nonperforming) loans over total loans for house purchase and renovation, from the Banco de España.3 Similar results are obtained when using commercial and industrial loans delinquency rates. These results are robust to different orderings of the variables in the VAR, especially for the response of default rates. The results are robust to estimating the VAR with Bayesian methods and a Minnesota prior. In order to address issues related to the effective lower bound of interest rates and the deployment of unconventional monetary policies, the two-year rate was used instead of the Federal Funds rate in the VAR. The results are robust as well.A standard VAR model is estimated with real GDP, HICP, the 12-month EURIBOR rate, real house prices, household leverage and delinquency rates, including one lag, for the period 1999Q1 to 2014Q4.

Effects of Prolonged Monetary Policy Accommodation on Financial Stability1

This box investigates whether prolonged periods of monetary policy accommodation undermine financial stability. Two measures of financial vulnerability are considered: the leverage (assets to equity; higher leverage implies higher vulnerability) and the Sharpe ratio (returns on equity divided by its volatility; a lower ratio implies higher vulnerability). Data are from 22 countries from 1998 Q1 to 2014 Q2.

This study extends the traditional focus in the literature in two ways. First, it covers nonbanks (insurance companies, investment banks, asset managers, real estates, and others) in addition to banks. Second, it considers multiple measures of prolonged monetary policy accommodation: consecutive quarters of cuts (in the overnight, 2-year and 10-year rates, as well as term spread), and of policy rates below a benchmark rate (the neutral rate, and rates implied by different Taylor rule specifications).

Unconditional correlations suggest a possible link between policy accommodation and financial stability risks. Leverage increased, and the Sharpe ratio decreased, during periods of accommodative monetary policy (Figure 4.1).2

Figure 4.1.
Figure 4.1.

Leverage Ratio in Periods of Monetary Policy Accommodation (Median)

Citation: Policy Papers 2015, 045; 10.5089/9781498344265.007.A001

Source: Bloomberg, Datastream, Worldscope, and IMF staff estimates.

Estimation results reinforce these correlations. The effect of prolonged monetary policy accommodation is estimated using a panel regression analysis.3 Leverage (Sharpe ratio) increases (decreases) the longer monetary policy remains accommodative (Figure 4.2 and 4.3). This is an expected part of the monetary policy transmission mechanism.

Figure 4.2.
Figure 4.2.

Estimated Effects on Leverage

Citation: Policy Papers 2015, 045; 10.5089/9781498344265.007.A001

Sources: Bloomberg, Datastream, WEO, Worldscope, and IMF staff estimates.
Figure 4.3.
Figure 4.3.

Estimated Effects on Sharp Ration

Citation: Policy Papers 2015, 045; 10.5089/9781498344265.007.A001

Sources: Bloomberg, Datastream, WEO, Worldscope, and IMF staff estimates.

Effects are significant for banks, as well as nonbanks. These include insurance companies and investment banks, and to some extent asset managers. Results are consistent with the “searching for yield” hypothesis (Rajan 2005, and Feroli and others 2014). This is somewhat different from Herman and others (2015), which suggests that U.S. interest rate shocks shift intermediation from banks to nonbanks.

Financial vulnerabilities seem most correlated to consecutive cuts in two-year bond yields. Other measures of policy accommodation exhibit less significant results (the two year yield has the additional advantage of capturing policy surprises even at the effective lower bound—see Swanson and Williams, 2014). Cuts in both actual and expected future policy rates seem to be important drivers of risk-taking behavior.

Further research is needed to assess the macroeconomic significance of results. Effects on both leverage and Sharpe ratios are notable, and could have repercussions on the probability of crises.

1 The author of this box is Machiko Narita, with assistance from Zohair Alam (both MCM), based on Cecchetti, Mancini-Griffoli, and Narita (2015, forthcoming).2 Leverage, though, tends to be negatively correlated with real growth. This is true across sectors, and likely due to the fact that equity valuations increase more than debt with higher growth, as in Andres and Gulan (2015), Adrian and Shin (2013), and Christiano and Ikeda (2013).3 ln(Yikt)= α0 + α1 Dkt + βXk,t-1 + Cki + εikt is estimated for each industry (i : firm, k: country, and t: time). Yikt is the financial vulnerability indicator, Dkt is the duration of monetary policy accommodation, Xk,t-1 are controls (real GDP growth, stock price growth, a volatility index, and a sovereign bond rating), Cki is fixed effects, and εikt is the error term.

Non-Linear Interaction Between Monetary Policy on Financial Stress1

This box investigates two questions: the effect of financial stress on the transmission of monetary policy to output, and the effect of monetary policy on financial stress.

The approach allows for non-linear effects. These may vary with the state of the financial system. When stress is high, monetary policy may have a stronger effect on output, as discussed in the paper. Likewise, tighter policy could undermine financial stability in the short-term, especially if the system is already under stress.

Financial stress is measured using a market-based stress indicator. The main variable of interest is Average Distance to Default (ADD) of the largest and most liquid publicly traded banks and insurance companies (in the U.K. and U.S., Canada and Australia, between January 1984 and October 2014). Distance-to-default, as discussed in the paper, is computed on the basis of balance sheet and equity price data, following Merton (1974). A lower ADD signals higher financial stress.

A Bayesian Threshold-VAR model determines two regimes. These are associated with periods of high and low financial stress. The TVAR includes four endogenous variables: output growth, inflation, ADD, and the term spread of government bonds (the difference between the 10-year and the 3-month yields). The term spread captures monetary policy; in most cases a lower spread means tighter policy (except at the effective lower bound; results are robust to excluding the ZLB period).

The effects of financial stress on the transmission of monetary policy to output growth differ across countries. A related literature, surveyed in the paper, instead finds more conclusive evidence of stronger effects in periods of financial stress. In the U.S., for instance, the effects on output growth of a 100 bps decrease in the term spread are not statistically distinguishable between regimes (Figure 5.1). Mean dynamics are similar to those found in other econometric studies of the U.S. economy (such as the IMF’s GIMF model mentioned in the paper).

Figure 5.1.
Figure 5.1.

Effects of a 100 bps Decrease in the Term Spread on GDP Growth

Citation: Policy Papers 2015, 045; 10.5089/9781498344265.007.A001

Source: IMF staff estimates.

A lower term spread increases financial stress in the short-term. This echoes findings mentioned in the paper. Higher rates typically reduce net worth, increase real debt, leverage, and debt servicing costs in the short-term, thereby lowering Distance-to-Default. In the U.S., ADD drops significantly in the 6 months following the monetary policy shock (Figure 5.2).

Figure 5.2.
Figure 5.2.

Effects of a 100 bps Decrease in the Term Spread on ADD

Citation: Policy Papers 2015, 045; 10.5089/9781498344265.007.A001

Source: IMF staff estimates.

These effects are mostly felt in times of high financial stress. This again supports the intuition developed in the paper. When banks are weak, higher rates—and associated increases in real debt and debt servicing costs of both banks and their customers—are more painful. In these circumstances, higher rates bring ADD near levels commensurate with those of past crises, such as the 1987 crash, or the 1998 Russian and Long-Term Capital Management crises, and the 1999 bursting of the dot-com bubble.

1 The author of this box is Martin Saldias, with assistance from Zohair Alam (both MCM), based on Saldias (2015, forthcoming).

25. An important caveat emerges: estimates of the effect of interest rates on financial variables may be biased downwards. Relationships among variables are mostly estimated over periods of relative stability. In those years, higher rates were associated with good times, and thus mostly growing financial vulnerabilities. The relationship between the two variables, despite attempts to carefully isolate monetary policy shocks, will therefore appear weak. Moreover, the analysis to date emphasized costs to unemployment. But other costs also exist, to inflation, public finances, as well as social and political stability and cohesion.

B. Financial Variables and Macroeconomic Conditions

26. Estimating how changes in financial variables affect the risk of future macroeconomic disturbances is tricky. First, two types of disturbances can be distinguished: crises and “setbacks.” Crises imply infrequent, but very substantial drops in output and increases in unemployment. Setbacks affect the economy more frequently, but are smaller, akin to a mild recession with financial roots.28 The emphasis in this paper will initially be on crises, taken to be the primary concern of financial stability policy, though setbacks will also be discussed.

27. The second complication stems from crises being rare events. Taylor (2015) reports on crises in advanced and emerging market economies since 1800, finding that while crises have not occurred uniformly over time (the postwar years, for instance, saw a period of relative calm until the 1970s), they have on average struck every 15 to 20 years. And crises are difficult to predict, with little agreement on how to gauge potentially rising risks.

28. This paper initially focuses on the relationship between bank credit and crises. This particular link seems to be the clearest among the variables discussed above. Using annual data from 1870 to 2008 for 14 advanced economies, Schularick and Taylor (2012) document that faster credit growth over the previous five years is associated with a higher probability of a financial crisis. Staff obtained similar results using a larger set of 35 advanced countries and quarterly data post 1960. While both the probability and severity of crises should be a concern to policy makers, analysis focuses on the first link which seems more robust.29

29. According to the evidence, higher interest rates reduce the probability of crises over the medium term.30 The above reduced form estimates, when taken together, suggest that the probability of crises first increases, then decreases to its trough after 3 to 5 years.31 At that point, the probability is reduced by 0.04 to 0.3 percentage points following a 100 basis point interest rate hike for a year, given the range of effects found in the literature. Panel 1 of Figure 5 illustrates this effect for a middle-of-the-road response, in which real credit growth decreases by one percentage point for one year, from its historical average of 5.4 percent.

Figure 5.
Figure 5.
Figure 5.

Credit Growth and the Probability of Banking Crises

Citation: Policy Papers 2015, 045; 10.5089/9781498344265.007.A001

Source: IMF staff estimates.

30. There are various caveats to these findings:

  • The average reduction in probability of crisis is lower than the maximum effect (at the trough). This is because crisis risk initially spikes following an interest rate hike, due to the stock effects discussed earlier.32

  • The path taken by the probability of crisis depends strongly on the behavior of credit. If the level of credit is assumed to return to steady state, after the temporary interest rate hike, credit growth has to increase substantially after reaching its trough to catch up for periods when credit was growing slowly. In this case, the probability of crisis can overshoot the initial point (Panel 1 of Figure 5 in red). However, if credit is assumed to stabilize at a lower steady state after the interest rate hike, credit growth does not need to overshoot its steady state. As a result, the probability of crisis returns to its initial level in a smoother fashion (Panel 1 of Figure 5 in blue).33

  • An interest rate hike would reduce the probability of crisis more if it occurred during a credit boom. Indeed, Panel 2 of Figure 5 shows that the probability of a crisis increases non-linearly as credit growth reaches very high rates.34

31. Other approaches, including that underpinning vulnerability analysis at the Fund, indicates that other financial variables can also explain crisis risk. Two approaches are used (Box 6). The first is drawn from the literature on early warning indicators. It determines the signal to noise ratio of each variable by measuring its ability to accurately sort the sample into crisis and non-crisis periods minimizing both type I and II errors. The second is the more standard logit regression discussed in the paper. Crises are explained by the growth of financial variables as well as their deviation from trend. The results suggest that a range of indicators, including equity and house prices, credit growth, and even simply the output gap, may be worth monitoring when forming judgments about stability risks and the scope for monetary policy action.

Predicting Crises1

This box reviews alternative approaches to predicting financial crises. The paper focuses on the role of real household debt, in a regression (logit) model. This box instead considers more variables and methods.

Two types of variables are considered. Financial variables include bank credit (real and as a percent of GDP), equity and property prices, and leverage (loan to deposit ratios of banks). Real variables include output and inflation.2

Variables enter in growth rates, or as gaps. Gaps are computed using one and two sided band-pass filters,3 where the smoothing parameter is set to extract cycles of frequencies equal to the observed lag between peak and trough of the original series.4 The distinction is fundamental. A two sided filter uses known future data to compute gaps at any given time. It can therefore extract important dynamics, but does not offer a practical tool to inform policy. In real time, the one-sided filter is more appropriate.

Two broad methods are used to predict crises. The first is drawn from the literature on early warning indicators.5 It determines the signal to noise ratio of each variable by measuring its ability to accurately sort the sample into crisis and non-crisis periods.6 The second is the more standard logit regression discussed in the paper.

Results from the early warning method underscore the difficulty of predicting crises. Two sided gaps emerge as much better predictors of crises than real time indictors. Since no one variable distinguishes itself as a solid predictor with a constant lag across methods, policy makers should consider a range of indicators to assess overheating in the financial system.

Equity price and output (two-sided) gaps predict crises best, up to two years ahead. Property price, inflation and credit gaps are also acceptable predictors one year before the crisis (Figure 6.1)

Figure 6.1
Figure 6.1

Signal to Noise Ratios

Citation: Policy Papers 2015, 045; 10.5089/9781498344265.007.A001

However, predictive power deteriorates significantly in real time. When variables are taken in growth rates, equity prices appear to be the best predictor one–two years ahead. Credits and leverage instead do better than others, one and two years ahead respectively, when applying one-sided filters (Figures 6.2 and 6.3).

Logit regressions confirm these results. Output and inflation gaps are nearly always significant predictors of crises, though lags vary according to specifications. Credit gaps loose significance when output and inflation gaps are also used. However, property price, equity and leverage gaps have predictive power. When taken as two sided gaps, these variables predict crises over relatively short horizons—typically around a year. The horizon extends to four years in some cases, when gaps are computed with one-sided filters. However, lags vary over sub-samples and across variables, making real-time prediction uncertain.

1 The authors of this box are Sally Chen, Katya Svirydzenka, and Aleksandra Zdzienicka (SPR).2 Data cover 34 advanced economies from 1960 to 2014.3 Christiano and Fitzgerald (2003).4Harding and Pagan (2002).5 IMF SM/07/328, 7/17/2007, and Kaminsky, Lizondo, and Reinhart (1998).6 By minimizing the percent of crises missed and misclassified (type 1 and type 2 errors).

Leaning Against the Wind in a Extended Inflation Targeting Framework1

This box evaluates the possible benefits and costs of leaning against the wind, using a VAR model estimated across countries. The model extends the approach of Riksbank (2013) and Svensson (2014, 2015), to estimate the effects of monetary policy shocks on inflation and unemployment, as well as on household debt, in six advanced inflation targeting countries2 using a standard Bayesian VAR method. The effect on unemployment and inflation directly captures the short-term costs of higher rates. Benefits instead stem from the medium term response of household debt, and its effect on the probability of a crisis. This last link is estimated based on results in Schularick and Taylor (2014).

The basic findings are that costs are more immediate than benefits, which appear with considerable lags. On net, costs are larger than benefits in all countries in the years following the monetary policy shock. After 4–5 years, benefits grow slightly larger than costs in some countries. These results hinge on the assumption that real household debt does not return to its pre-shock levels. As discussed in the text, this implies that monetary policy is not neutral, and that credit growth, along with the associated probability of a crisis, return to their original levels without overshooting them. Moreover, the costs of a crisis have to be substantial for the benefits to be large. The initial conditions also need to be close to, and not below, long run sustainable levels. Finally, credit growth needs to be persistently affected by monetary policy.

Detailed results are presented in Figure 7.1. Interest rates are hiked by 100 basis points during one year. The peak positive effect3 occurs after three to five years and implies that the probability of a crisis is around 0.04 percentage points lower per quarter. Although effects seem small, their persistence entails a larger cumulative impact. These help reduce the probability of a crisis that is assumed to be substantial (5 percentage point higher unemployment rate and 2 percentage points lower inflation for four years).

Figure 7.1.
Figure 7.1.

Effects of an Interest Rate Hike

Citation: Policy Papers 2015, 045; 10.5089/9781498344265.007.A001

Sources: IMF, BIS, OECD, national sources through Haver Analytics, and IMF staff estimates.
1 The authors of this box are Stefan Laseen and Aleksandra Zdzienicka, with assistance from Federico Diaz Kalan.2 Australia, Canada, Norway, Sweden, the U.K., and the U.S.3 The cost is expressed as the cumulative mean of the sum of squared deviations of inflation from target and the unemployment rate from long run sustainable level. The benefit is the product of the probability of a crisis in the next period times the cost conditional on a crisis occurring.

32. Indeed, the effect of interest rates on the probability of crises may be stronger if links through other financial variables are also taken into account. The earlier analysis focused on the link between interest rates and crisis probability, as intermediated by real credit growth. The link through other financial variables, though developed at the IMF (see above), has not been quantified as precisely, nor have links through specific variables been sufficiently separated from one another. However, as discussed above, interest rates have significant effects on more than just real household credits: on leverage of financial firms, risk-taking behavior, asset prices, and credit spreads. Changes in these variables are likely to entail financial vulnerabilities through different and, at least partly, independent channels to those related to household credit. To the extent these are relevant, the effect of higher interest rates on lowering crisis probability should likely be revised up, at least somewhat.

Step 2: Tradeoffs

33. Tradeoffs between stabilizing inflation (or output) and financial risks should be evaluated on the basis of magnitude and direction of effects. The first thing to establish is whether the periods when interest rates have to be tightened for price stability purposes often coincide with periods when financial stability concerns also call for higher rates such that rates would be raised for both reasons. The second question regards the size of interest rate changes; should interest rates be hiked by the same degree to ensure that both objectives are met? The earlier analysis suggests this will often not be the case. A 100 basis point rise in interest rates is large for price stability purposes (at least in AEs in tranquil times)—central banks rarely lift rates by more than 25 or 50 basis points at most on any given announcement date (though on a cumulative basis the U.S. Federal Reserve, for instance, raised rates by about 200 basis points per year in the 2004–06 tightening cycle). Meanwhile, the earlier analysis suggested that a 100 basis point increase in rates is associated with a small decrease in the probability of a crisis.

34. However, tradeoffs may not always be severe, at least on the basis of the direction of effects, and with hindsight. Often, financial risks develop in periods of economic expansion, which also warrant higher interest rates for the purpose of price stability. Figure 6 shows inflation and output gaps in the advanced economies that experienced systemic banking crises in 2007–08.35 Panel 1 shows that inflation was running slightly above target prior to the crisis, thus calling for a somewhat tighter monetary stance from a pure inflation-targeting perspective. The deviation from target did remain moderate, but a possible lesson from the crisis is that, given the flattening of the Phillips curve, policy makers may need to react more promptly as inflation deviates from target. Panel 2 considers the evolution of output gaps. Real-time estimates from the April 2007 WEO show no sign of economic overheating in the run-up to the crisis. However, revised estimates from April 2015, based on revised data and the realization of the crisis, reveal large positive output gaps that would have warranted considerable monetary tightening. In these cases, tradeoffs may have been small on the basis of both sign and size of interest rate responses. The analysis underscores a well-known point: policy makers should seek to improve real-time estimates of potential output, possibly using financial variables, as well as external balances—this remains a key and difficult challenge.36 Box 2 offers a similar analysis based on comparing output gaps with financial gaps—a measure of financial stability risks—and also suggests that tradeoffs between stabilizing inflation and financial risks may not always be severe.

Figure 6.
Figure 6.

Economic Dynamics in AEs with Banking Crises in 2007–08

Citation: Policy Papers 2015, 045; 10.5089/9781498344265.007.A001

Sources: WEO October 2015, staff estimates.* Defined as the midpoint of target range.Sources: WEO April 2007 and October 2015, staff estimates.

How Severe are the Tradeoffs Between Economic and Financial Stabilization?1

This box considers the extent to which policy makers may confront tradeoffs between economic and financial stability. Figure 2.1 shows the evolution of economic and financial variables in the advanced economies that, according to Leaven and Valencia (2012), experienced a systemic banking crisis in 2007 and 2008. Data are quarterly and each dot indicates the beginning of the crisis. The vertical axis shows a measure of economic gap, given by the simple average of the output gap and the inflation deviation from target. The horizontal axis shows a measure of financial gap obtained as the simple average of the deviations in credit-to-GDP and real house prices from their historical trends.

Figure 2.1
Figure 2.1

Economic and Financial Gaps Around Banking Crisis

Citation: Policy Papers 2015, 045; 10.5089/9781498344265.007.A001

Sources: BIS, WEO, and IMF staff estimates.

Economic and financial gaps are estimated with the benefit of hindsight. In particular, potential output and the trends of financial variables are constructed with two-sided HP filters, respectively with smoothing parameter of 1.600 and 400.000 as used by the BIS. This perspective is helpful to analyze the true possible tradeoffs between economic and financial stability, even though real-time estimates may convey a different impression because of imprecise estimates of potential output, as for example illustrated in panel 2 of Figure 6.2

The tradeoffs between economic and financial stability may not be particularly strong. The figure shows that in the three years prior to a banking crisis, both economic and financial variables tend on average to grow above normal values. Monetary tightening would thus be warranted on the simple grounds of economic stabilization. Once a crisis begins, economic conditions tend to deteriorate quickly, while financial conditions adjust more gradually. This may lead to a period with negative economic gaps and positive financial gaps. However this constellation does not seem to justify monetary tightening since financial conditions tend to correct over time. The analysis calls for improving the quality of real-time estimates of potential output, possibly using financial variables, to better guide monetary policy.

1 The author of this box is Damiano Sandri (RES) with assistance from Paola Ganum (RES).2 Country data points in Figure 2.1 do not provide an accurate basis for cross-country comparison of financial resilience because the HP filter does not account for country-specific features.

Step 3: Welfare Implications

35. Ideally, implications of leaning against the wind should be taken in a fully specified model. Empirical relationships estimated on past data only go so far. Importantly, they are rooted in a period during which monetary policy did not lean against the wind. Had it done so, agents might have adapted their behavior by taking fewer risks in the first place, or by cutting back on risk more aggressively following interest rate hikes. Only models can account for the endogenous response of households and firms to a structural change in policy, such as the decision to lean against the wind. To do so, though, models need to fully take into account the structural relationships between agents’ risk-taking behavior, and financial and macro variables (the so-called micro-foundations).

36. Indeed, a new class of models suggests that leaning against the wind, absent other tools, can be welfare improving. These models take into account some financial distortions, as well as heterogeneous agents.37 However, these models do not as yet generate crises of major proportions as considered in this paper. The buildup of financial imbalances and the subsequent crises are modeled as small fluctuations around the economy’s steady state growth path. Hence, they exclude large nonlinearities—associated for example with default states—that could meaningfully impact the welfare considerations. It remains for now that reacting systematically to swings in financial variables (asset prices, leverage, and risk premia) reduces inefficient fluctuations in output.38

37. The welfare improvements in many current models are generally fairly small and state (or shock) dependent. Hence, simple rules that react to observable variables may lead to policy mistakes––emphasizing the need for judgment in actual policy decisions.39 Also, welfare gains of leaning against the wind in these models are small relative to complete macroprudential policies that are able to more directly target financial frictions (Figure 7). In these models, though, macroprudential policy remains highly stylized (a reduced form means of affecting lending rates that is completely effective) and cannot be attributed to a specific real-world instrument.

Figure 7.
Figure 7.

Welfare Gains from Leaning Against the Wind Versus Using Macroprudential Policy

Citation: Policy Papers 2015, 045; 10.5089/9781498344265.007.A001

Source: IMF staff estimates.Note: Welfare is measured in terms of consumption relative baseline with no reaction to leverage. X-axis is relative importance of leverage in policy rules.

38. While useful for informing policy intuition, the new models remain very stylized and more work is needed. They have not yet fully captured the foundations of financial distortions and the specific dynamics of risk-taking behavior. It is not clear how much monetary policy can affect the root cause of these distortions, such as incentive structures, or funding liquidity constraints due to asymmetric information or balance sheet mismatches. Also, in the real world, distortions are multiple, interrelated, and time varying. They may include a mix of frictions, each with a different effect on the risk-taking behavior of different actors. Models, for instance, might consider effects of higher rates on the leverage of financial intermediaries, but not on household loan delinquencies. Moreover, crises are mostly modeled as small fluctuations around the economy’s steady state growth path. Multiple equilibria where one pole captures a state of massive defaults, debt overhang and asset price spirals, are mostly excluded. This seems at odds with the observation that crises follow extended departures from steady state followed by sharp deviations. This indicates the need for a richer analysis of non-linearities that could have sizable implications for the role of monetary policy.

39. In the meantime, a simpler framework for cost-benefit analysis can be used as a rough guide for policy deliberations. The framework, inspired by Svensson (2014), is useful to build intuition, highlight interactions among variables, and explore rough magnitudes. Leaning against the wind involves paying a short-term cost—lower output or higher unemployment—in exchange for a medium-term benefit in the form of lower expected costs from a financial crisis.40 There is less of an inter-temporal tradeoff (illustrated in Figure 8) with respect to the role of monetary policy in stabilizing inflation, because of the strong correlation between inflation and output over shorter horizons, where bringing inflation to target typically also implies bringing output towards its target.41

Figure 8.
Figure 8.

To Lean or not to Lean, Such are the Tradeoffs

Citation: Policy Papers 2015, 045; 10.5089/9781498344265.007.A001

Source: IMF staff.

40. One important parameter in the illustrative cost-benefit analysis is the unemployment loss from higher interest rates in the short run. This loss can be approximated by using any standard DSGE model used for policy analysis. The IMF’s GIMF model, which is also used by many central banks, implies that unemployment would rise by somewhat less than ½ percentage point as a result of a 100 basis point increase in short-term interest rates for a year.42 This estimate is broadly consistent with those obtained using vector auto-regression (VAR) models to estimate the transmission of monetary policy.43

41. The costs of leaning against the wind appear greater than benefits, unless a severe crisis is foreseen. The cost-benefit analysis is highly sensitive to the strength of the linkages between the policy rate and crisis risk, and the assumed severity of the crisis. In order to justify leaning against the wind these parameters need to be close to the upper range of existing empirical estimates. Note, however, that such calculations focus on the benefits of avoiding major crises, but do not consider additional benefits of reducing smaller economic setbacks (a topic discussed further below).Three scenarios among many are illustrated in Table 1.44

  • The first is based on the average decrease in crisis probability computed earlier, as a result of a 100 basis point increase in short-term interest rates. In this scenario, the crisis is assumed to increase unemployment by 5 percentage points and last 4 ½ to 6 years.

  • The second scenario assumes the crisis probability can be reduced by 0.3 percentage points—the maximum effect found in the literature surveyed earlier.

  • In the third scenario, the crisis is assumed to be acute (an event further in the tail), with unemployment increasing by 7 percentage points for 6 to 8 years.

  • In the first two scenarios, the costs of leaning against the wind are notably higher than benefits. Only in the third scenario does leaning against the wind seem to pay off.

42. A more detailed alternative approach comes to similar conclusions and provides various refinements.45 The first is that the above illustration draws on earlier results that represent broad averages; tradeoffs will be different depending on specific country circumstances. In some cases, benefits can rise above costs, but only somewhat and in the medium to long term. Costs of leaning against the wind are apparent in the short run, whereas benefits materialize more slowly and with uncertainty. In addition, benefits are sensitive to various assumptions, one of which is the return of real credits to steady state, as discussed earlier. Initial conditions are also important, and the case for leaning against the wind improves as initial unemployment is close to target, and unemployment gets squared in the welfare calculations.46 Any potential policy response must therefore be state contingent, and is thus more complicated than adding a variable with a fixed coefficient to a Taylor rule.

Table 1.

Illustrative Scenarios

article image
Source: IMF staff estimates.

Further Considerations

43. Three considerations are relevant: the impact of leaning against the wind on the probability of set-backs, in addition to crises, effects on central bank credibility, and open economy implications.

44. A fruitful area for further research is the case for leaning against the wind in cases other than a full-fledged crisis. In the analysis above, we rely on a “zero-one” definition of crisis. The methodology implicitly assumes that the only positive impact of leaning against the wind is reducing the probability of a full-fledged crisis. However, it is possible that higher interest rates may also reduce the severity and incidence of “non-crisis” recessions stemming from financial imbalances. Then, if one used a more continuous definition of financial instability and, hence, included less severe economic set-backs with financial roots, the expected benefits from leaning against the wind might increase. This is tantamount to considering the effect of leaning against the wind on the entire distribution of future unemployment, not just its (crisis) tail. The same cost-benefit framework sketched out earlier would apply equally well to this setting.

45. But empirical results capturing this additional benefit are still scarce and inconclusive. Some work shows that credit spreads are mean reverting and correlated to economic activity. Spreads that are compressed by buoyant sentiment today often forecast a widening of spreads in the future, lower credit supply especially to lower credit-quality firms, and an economic contraction. To the extent that expansionary monetary policy contributes to tighter spreads, it may also increase the risk of a reversal in credit markets and corresponding drag on output.47 As discussed earlier, the link from monetary policy to setbacks could also occur through other variables. Credit growth, for instance, leads to a future expansion and then contraction of GDP, according to staff estimates. However, GDP never substantially falls back below its initial level. There is therefore no clear case to lean against credit growth from the stand-point of containing risks of setback. More work in this area is clearly warranted, as also argued by the authors active in this nascent field.

46. Leaning against the wind might undermine the credibility of the central bank, and the effectiveness of monetary policy, including a de-anchoring of inflation expectations. Credibility and policy effectiveness largely stem from transparency, predictability, and observable success, which are key underpinnings of the standard inflation-targeting framework. Leaning against the wind, by contrast, requires policy action to be justified on the basis of distant events that are difficult to forecast, or even to define precisely. It also involves using one instrument for two targets. Transparency and predictability could suffer, making communication more complicated.48 Credibility may also suffer because crises will most likely occur despite leaning against the wind, and because the central bank will under deliver on inflation, at least at times, which could destabilize inflation expectations.49 Moreover if central banks under-delivered on their inflation mandates, real debt and real interest payments on debt would increase, thereby undermining financial stability. Nonetheless, it is also the case that not responding to risks and so allowing crises to emerge could undermine the credibility of the policy framework.

47. For large economies with strong cross-border financial links, both benefits and costs of leaning against the wind may be larger, once spillovers are taken into account. The analysis in this paper measured the costs of crises in terms of domestic effects. However, financial crises in large countries can have strong spillover effects across borders due to financial linkages, as discussed in past editions of the IMF Spillover Report (IMF 2013d, 2014a and 2015, for instance). Thus, if monetary policy in a large country were to decrease the probability of crisis, it would avoid higher domestic, but also international costs. From a global welfare perspective, this would tip the cost-benefit analysis of the earlier simple framework towards leaning against the wind, everything else equal. However, higher interest rates in the large country could also have negative effects on smaller countries through trade linkages (more sluggish demand from the large country, compensated in part by a stronger currency). In addition, in practice, central banks will first and foremost take policy decisions to satisfy their domestic mandates, unless spillover effects on foreign countries could spillback to the domestic economy or if mitigating cross-border spillovers would not affect achieving the domestic objectives.50

48. However, for small open economies, the case for leaning against the wind may be weaker. First, in such economies, financial stability concerns often stem from strong capital inflows that drive up asset prices and compress credit spreads (see Sahay and others, 2014). In this case, increasing domestic interest rates (or cutting rates by less than warranted to stabilize prices) may be counterproductive, and exacerbate instability by attracting further capital inflows.51 Second, whatever the source of financial vulnerability, higher rates would tend to appreciate the domestic currency, and thus strengthen balance sheets for those with debts in foreign currency (the IMF’s October 2015 Global Financial Stability Report (GFSR) highlights the extent to which firms in emerging markets are exposed to foreign currency debt; IMF 2015). In fact, higher rates could even increase the share of foreign currency debt, a common problem in highly dollarized economies. As a result, debt levels may actually increase, instead of decrease.52 Other policies may be more appropriate to manage financial stability risks stemming from capital inflows.53

Implementation Issues

49. Whatever guidance the cost-benefit analysis may provide, implementation is also crucial; three issues arise:

  • First, when to act. If action comes when financial vulnerabilities are already very large, leaning against the wind could bring about a crisis: the immediate effect of higher interest rates is to worsen, not improve, vulnerabilities. Decisions on leaning against the wind must thus take account of the resilience of the financial system. Similarly, as was pointed out earlier, leaning against the wind should not be done after a crisis, when unemployment is already very high. Leaning against the wind should thus be done early in the development of financial vulnerabilities; but this raises new questions as discussed below.

  • Second, how to detect vulnerabilities and predict crises in real time. It is precisely in the initial phases of economic expansion and recovery that central banks would find it most difficult to justify leaning against the wind. In this phase, it is difficult to distinguish between credit expansions that are good (driven by productivity) and bad (driven by consumption and expectations of capital gains), and crises appear distant and unlikely events. The difficulty of policy making under uncertainty is augmented relative to price stability, given the longer horizons over which crises must be forecasted. It is instructive, for instance, to go back to the U.S. experience in 2002, when house prices and household debt were beginning to grow especially rapidly (see, for instance, Jorda, Schularick, and Taylor, 2015). Had policy-makers expected a large increase in crisis probability and severity (a rise in the unemployment rate of 4 percent on average for 6 years, starting in 2007), they may well have decided to lean against the wind.54 However, in 2002, even the most pessimistic forecasts of U.S. growth did not foresee such a dramatic surge in unemployment coming just five years later. The IMF’s 2002 WEO, for instance, focused on immediate downside risks stemming from equity prices, investment spending and the exchange rate, but does not discuss risks further out on the horizon. The hope is that today policy makers can benefit from better frameworks to judge the impact of interest rates on financial stability, and better estimates to consider the link between the dynamics of financial variables and crisis probability. In the end, judgment will always need to be applied when indicators on balance suggest some gain from a monetary response.

  • Third, combining monetary and prudential policies. The analysis in this paper has focused on the role that monetary policy can play in supporting financial stability, everything else equal. But this is not sufficient to provide policy guidance: in the end, monetary and prudential policies must be considered together.55 To begin with, well targeted macroprudential (and micro-) policies should shoulder most of the burden to contain financial instabilities. Further, macroprudential and monetary policy seem to be strongly complementary. For example, if an early interest rate hike could contain a possible spike in asset prices (due to non-linearities), it would facilitate the job of prudential policies down the road. Moreover, monetary and prudential policies must be systematically compared on three counts: their relative uncertainty (both policies could potentially have the same expected effects, but the effects of one could be more uncertain than those of the other), expected effects, and expected costs (prudential policies too give rise to distortions even as they correct others).56 Ultimately, the mix of prudential and monetary policy to support financial stability would need to be determined by a joint maximization of social welfare. In doing so, the practical constraints on each policy should be compared, to see if one policy may or may not be able to overcome the hurdles facing the other. For instance, if political pressures are the main hurdle to prudential policy, they will also hinder a potential focus of monetary policy on financial stability.

Concluding Thoughts

50. On balance, based on current understanding and circumstances, the case for leaning against the wind is limited. With substantial slack in the macroeconomy, transmission from interest rates to financial risks seems weak, costs often appear greater than benefits, and implementation hurdles are substantial. Macroprudential policies, including both cyclical and structural measures, will remain a key element of the defense against financial instability. Indeed, these measures, when well targeted and effective, can target imbalances and market imperfections much closer to their source than monetary policy does. Also, they could allow monetary policy to focus on its price stability mandate, thereby simplifying communication and enhancing accountability.

51. Further research is warranted. First, our understanding of the transmission mechanism from monetary policy to financial stability is limited. New evidence and analysis could identify new channels of transmission and strengthen the case for leaning against the wind. Second, even based on current knowledge (and the analysis in this paper), benefits can plausibly outweigh costs in particular, albeit relatively unlikely, circumstances. These circumstances can reflect a confluence of initial conditions pertinent to the conjunctural cycle and structural conditions characteristic of specific countries. For example:

  • Initial conditions. Benefits relative to costs can be boosted by: low unemployment (when rate hikes could lead to smaller macroeconomic costs); rapid credit growth (when rate hikes could have a stronger effect on credit growth and crisis probability by discouraging exuberant, self-fulfilling behavior); and when borrower and bank balance sheets are strong (and hence can withstand the initial interest rate shock).

  • Structural conditions. Benefits also rise relative to costs when: crises are likely to be particularly severe (due to a large and interconnected financial system and the absence of well-targeted macro-prudential measures); financial spillovers could be large (as for systemically important countries with open capital accounts); and if future financial risks can be reliably identified early (so that early increases in interest rates may be able to avoid a large buildup of risks).

Appendix I. Past Policy Advice

52. This appendix examines the Fund’s recent policy advice on monetary and macroprudential policies. In terms of the institutional background, almost all advanced country monetary policy regimes focus on price stability as the primary mandate with financial stability often defined as a secondary objective. For example, some central banks have the responsibility to “promote” financial stability (as in Australia, New Zealand, Sweden, and the U.S.) or—somewhat weaker—“contribute to” financial stability (as in Canada, Euro Area, Japan, Switzerland). An exception is the U.K., where financial stability is defined as an explicit legal objective of the Bank of England. In practice, many of these central banks do already incorporate financial stability considerations in their monetary policy frameworks. Generally, in all these country cases, macroprudential measures are seen as a first-best option to mitigate systemic risks, provided that tools are available and effective. However, recognizing the limitations of macroprudential measures, some central banks have been using monetary policy to lean against the wind (Norway), or leave the door open to doing so (e.g., Australia, Canada, and Sweden, as well as the U.S.).57

53. The Fund’s monetary policy advice has varied depending on specific circumstances. This partly reflects differences in relative cyclical positions (Tables 1 and 3), as well as in the institutional framework for monetary and macroprudential policies and the structure of financial system across AEs. More specifically (Table 2):

  • While macroprudential measures were seen as a first line of defense in the policy advice for Sweden—especially since monetary policy has turned expansionary to contain deflation risk58—staff has stressed that monetary policy may eventually have to “lean against the wind” if macroprudential policies failed to serve their purpose. Staff stressed that this would require careful consideration of the tradeoffs between the impact on growth and the cost of rising financial vulnerabilities, and reinforced the urgency of implementing a comprehensive set of macroprudential measures to contain household credit demand.

  • The policy advice for Canada, Norway, and the U.K. also considered a role for monetary policy tightening should Macroprudential measures to deal with financial stability risks prove ineffective.59 Canada and the U.K. have been pro-active in implementing some Macroprudential measures—albeit of varying impact—and the Fund’s policy advice is to stand ready to take further measures as needed.

  • Financial stability risks are largely left to microprudential policies in the U.S. policy discussions, with the Fund pointing to the need to strengthen the Macroprudential framework. That said, monetary policy advice was not only conditional on the inflation and growth outlook, but also on financial stability risks.

  • In contrast, monetary policy advice for Australia and Switzerland did not raise the issue of monetary tightening for financial stability purposes, which is left to Macroprudential measures. While not explicitly ruling out this option, the monetary policy advice for Australia focused on near-term aggregate demand management. Staff recommends use of the supervisory framework to mitigate house price risks. Tightening for financial stability purposes was not considered for Switzerland in view of significant inflation undershooting. As in the case of Sweden, staff recommends to further strengthen Macroprudential measures.

  • The different approaches to Macroprudential policies in these countries not only reflect differences in financial conditions, but also in financial systems, with the effectiveness of such policies likely to be more limited where non-bank intermediation is more prevalent. Furthermore, it should be acknowledged that there is a large degree of uncertainty regarding the effectiveness of such policies in general.

Table 1.

Select Advanced Economies: Economic and Financial Indicators and Policy Advice

article image
Sources: Staff Reports for Article IV Consultation; credit growth and house price growth based on BIS data.

In percent, with t measured in years (t referring to the year in which the Article IV Consultation took place).

Annual percent change (4Q y/y), with t measured in quarters (t referring to the quarter in which the Article IV Consultation took place).

In percent, with t measured in years (t referring to the year in which the Article IV Consultation took place).

Provided that financial stability risks can be contained, otherwise monetary policy may have to “lean against the wind.”

Any move to normalize monetary policy, needs to be approached cautiously.

Table 2.

Selected Advanced Economies: Detailed Policy Advice

article image
article image
Staff Reports for 2015 Article IV Consultation, except Australia, Canada, Sweden, and the United Kingdom (2014 Article IV Consultation Staff Reports).
Table 3.

G20 Emerging Markets: Economic and Financial Indicators and Policy Advice

article image
Source: Staff Reports for Article IV Consultation and WEO; credit growth and house price growth based on BIS data.

In percent, with t measured in years (t referring to the year in which the Article IV Consultation took place).

Annual percent change (4Q y/y), with t measured in quarters (t referring to the quarter in which the Article IV Consultation took place).

In percent, with t measured in years (t referring to the year in which the Article IV Consultation took place).

Provided that financial stability risks can be contained, otherwise monetary policy may have to “lean against the wind.”

54. Discussions around financial stability objectives are also emerging in a few emerging markets (EMs), although less apparent in general than in AEs. Slower growth, tighter financial conditions, and lower commodity prices have helped to reduce inflationary pressures in EMs since 2014, allowing some countries to ease monetary policy. At the same time, and despite the deceleration in credit growth, financial stability risks remain in some EMs reflecting vulnerabilities built during the past rapid credit expansion fueled in part by buoyant capital inflows. China is a notable example, with Thailand and Peru among other cases facing somewhat similar tensions:

  • China. While output growth and inflation have moderated, and credit growth (including shadow banking) has slowed considerably, Fund advice has stressed the need to maintain a neutral monetary stance. This advice partly reflects the structural nature of the growth slowdown, but also highlights the need for deleveraging to bring the credit-to-GDP and private leverage ratios to more comfortable levels.

  • Peru experienced a sharp output growth slowdown in 2014–15, with the negative output gap expected to close only in 2018. Credit growth has decelerated and inflation expectations are well anchored. However, financial dollarization remains a major vulnerability and constrains the room for further monetary easing given the pressure in the FX market in the context of the anticipated U.S. monetary policy normalization. The Fund has emphasized that exchange rate flexibility should be the main line of defense, with de-dollarization measures, including prudential requirements, aimed at reducing currency-liquidity-maturity mismatches.

  • Thailand is seeing a tentative growth recovery following political uncertainties during 2013–14, but the sizeable negative output gap is expected to close gradually, and headline inflation has turned negative. While the Fund does not see the recent monetary easing as exacerbating financial stability risks, it has called for strengthened regulation of non-commercial banks, including specialized financial institutions and credit cooperatives, which are heavily exposed to the highly-indebted household sector.

Moreover, spillovers from advanced economy monetary policy have been a complicating factor also from a financial stability perspective for smaller open economies, including many EMs (Box 8).

Cross-Border Spillovers from Monetary Policy on Financial Stability1

Spillover effects of U.S. monetary policy on foreign asset prices and capital flows have been widely documented. Changes in U.S. monetary policy have significant effects on financial conditions abroad (Glick and Leduc, 2013; Rogers et al., 2014), with the impact varying with country-specific conditions (Basu et al., 2014; Chen et al., 2014).

This box investigates the effects of U.S. monetary policy on financial stability abroad. To capture risks to financial stability, this box focuses on real credit growth. Data cover 20 economies—both advanced and emerging—between 1969Q3 and 2008Q4.

Two specifications are used to investigate this issue. The first assesses the effect of U.S. monetary policy shocks on foreign real credit growth, and the second whether country-specific characteristics affect this relationship.2

Results suggest that U.S. monetary policy shocks affect credit dynamics in other countries. In particular, an exogenous increase of 100 basis points in the U.S. monetary policy rate typically contracts the level of real credit by about 1 percent after 12 quarters (Figure 8, top panel). The effect varies across countries (Figure 8, bottom panel), and is larger for emerging market economies (about 1.3 percent) than for advanced economies (about 0.8 percent).

Results also suggest that spillover effects are larger for countries with certain characteristics: Countries with fixed exchange rate regimes (1 percent) than for countries with flexible exchange rates (0.4 percent), and countries with higher capital account openness (0.8 percent). In contrast, the impact of U.S. monetary policy shocks on real credit is similar and not statistically different across countries with different degree of market capitalization.

Much more needs to be done to understand the nature and consequences of spillovers from monetary policy. Two pressing questions are: to what extent do spillovers depend on the underlying drivers of policy normalization (better growth prospects vs. financial stability concerns), and how do financial risks, and financial stress propagate across borders.

Figure 8.1.
Figure 8.1.

Spillovers

Citation: Policy Papers 2015, 045; 10.5089/9781498344265.007.A001

Source: IMF staff estimates.Note: The y-axis shows the impact of monetary policy shocks on the log level of real credit—The coefficients in δ Equation. The x-axis indicates quarters after the shock in t = 0. Dashed lines indicate the 90-percent confidence bars.Source: IMF staff estimates.
1 The author of this box is Aleksandra Zdzienicka (SPR) with assistance from Federico Diaz Kalan (SPR).2 The first specification is: ∆cit = αi + δ(L)mt + εt where c is the (log of) real credit in country i, m are U.S exogenous monetary policy shocks identified by Coibion (2012), and αi are country fixed-effects. In the second specification, monetary policy shocks are interacted with country characteristics, as in Auerbach and Gorodnichecnko (2013).
Table 4.

G20 Emerging Markets: Detailed Policy Advice

article image
Sources: Staff Reports for 2015 Article IV Consultation, except for Mexico, South Africa and Turkey (2014 Article IV Consultation Staff Reports).

References

  • Acharya, Viral V., and Hassan Naqvi, 2012, “The Seeds of a Crisis: A Theory of Bank Liquidity and Risk-Taking Over the Business Cycle,” CEPR Discussion Papers 8851.

    • Search Google Scholar
    • Export Citation
  • Adrian, Tobias, and Hyun Song Shin, 2014, “Procyclical Leverage and Value-at-Risk,” Review of Financial Studies, 27(2), pp. 373403.

    • Search Google Scholar
    • Export Citation
  • Agrippino, Silvia Miranda, and Helene Rey, 2014, “World Asset Markets and the Global Financial Cycle,” Working Paper London Business School.

    • Search Google Scholar
    • Export Citation
  • Ahmed, Shaghil, and Andrei Zlate, 2014, “Capital flows to Emerging Market Economies: A Brave New World?Journal of International Money and Finance, Elsevier, Vol. 48(PB), pp. 221248.

    • Search Google Scholar
    • Export Citation
  • Aiyar, Shekhar, Charles W. Calomiris, and, Tomasz Wieladek, 2012, “Does Macro-Pru Leak? Evidence from a UK Policy Experiment,” NBER Working Papers 17822, National Bureau of Economic Research, Inc.

    • Search Google Scholar
    • Export Citation
  • Akinci, Ozge, and Jane Olmstead-Rumsey, 2015, “How Effective are Macroprudential Policies? An Empirical Investigation,” International Finance Discussion Papers 1136, Board of Governors of the Federal Reserve System (U.S.).

    • Search Google Scholar
    • Export Citation
  • Alessandri, Piergiorgio, and Haroon Mumtaz, 2014a, “Financial Indicators and Density Forecasts for U.S. Output and Inflation,” Temi di discussione 977, Bank of Italy.

    • Search Google Scholar
    • Export Citation
  • Alessandri, Piergiorgio, and Haroon Mumtaz, 2014b, “Financial Regimes and Uncertainty Shocks,” Working Papers 729, Queen Mary University of London, School of Economics and Finance.

    • Search Google Scholar
    • Export Citation
  • Allen, Franklin, and Douglas Gale, 2000, “Asset Price Bubbles and Monetary Policy,” Center for Financial Institutions Working Papers 01-26, Wharton School Center for Financial Institutions, University of Pennsylvania.

    • Search Google Scholar
    • Export Citation
  • Alpanda, Sami, and Sarah Zubairy, 2014, “Addressing Household Indebtedness: Monetary, Fiscal or Macroprudential Policy?Working Papers 14–58, Bank of Canada.

    • Search Google Scholar
    • Export Citation
  • Altavilla, Carlo, and Matteo Ciccarelli, 2009, “The Effects of Monetary Policy on Unemployment Dynamics Under Model Uncertainty—Evidence from the US and the Euro Area,” CESifo Working Paper Series 2575, CESifo Group Munich.

    • Search Google Scholar
    • Export Citation
  • Altunbas, Yener, Leonardo Gambacorta, and David Marques-Ibanez, 2010, “Bank Risk and Monetary Policy,” Journal of Financial Stability, Elsevier, Vol. 6(3), pp. 121129, September.

    • Search Google Scholar
    • Export Citation
  • Ananchotikul, Nasha, and Seneviratne Dulani, 2015, “Monetary Policy Transmission in Emerging Asia: The Role of Banks,” IMF Working Paper (Washington: International Monetary Fund) (forthcoming).

    • Search Google Scholar
    • Export Citation
  • Angeloni, Ignazio, Ester Faia, and Marco Lo Duca, 2015, “Monetary Policy and Risk Taking,” Journal of Economic Dynamics and Control, 52(C), pp. 285307.

    • Search Google Scholar
    • Export Citation
  • Arregui, Nicolas, Jaromír Beneš, Ivo Krznar, Srobona Mitra, and Andre Oliveira Santos, 2013, “Evaluating the Net Benefits of Macroprudential Policy: A Cookbook,” IMF Working Paper WP/13/167 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Arsov, Ivailo, Elie Canetti, Laura Kodres, and Srobona Mitra, 2013, “Near-Coincident Indicators of Systemic Stress,” IMF Working Paper WP/13/115 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Atanasova, Christina, 2003, “Credit Market Imperfections and Business Cycle Dynamics: A Nonlinear Approach,” Studies in Nonlinear Dynamics and Econometrics, 7(4), pp. 122.

    • Search Google Scholar
    • Export Citation
  • Auerbach, Alan J., and Yuriy Gorodnichenko, 2013, “Output Spillovers from Fiscal Policy,” American Economic Review, American Economic Association, Vol. 103(3), pp. 14146, May.

    • Search Google Scholar
    • Export Citation
  • Avdjiev, Stefan and Zheng Zeng, 2014, “Credit Growth, Monetary Policy, and Economic Activity in a Three-Regime TVAR Model,” Applied Economics, 46(24), pp. 29362951.

  • Bakker, Bas B., Giovanni Dell’Ariccia, Deniz Igan, Luc Laeven, Hui Tong, and Jerome Vandenbussche, 2012, “Policies for Macrofinancial Stability: How to Deal with Credit Booms,” IMF Staff Discussion Note, June.

    • Search Google Scholar
    • Export Citation
  • Balke, Nathan, 2000, “Credit and Economic Activity: Credit Regimes and Nonlinear Propagation of Shocks,” Review of Economics and Statistics, 82(2), pp. 344349.

    • Search Google Scholar
    • Export Citation
  • Basu, Kaushik, Barry Eichengreen, and Poonam Gupta, 2014. “From Tapering to Tightening: the Impact of the Fed’s Exit on India,” Policy Research Working Paper Series 7071, The World Bank.

    • Search Google Scholar
    • Export Citation
  • Bayoumi, Tamim, Giovanni Dell’Ariccia, Karl Habermeier, Tommaso Mancini-Griffoli, and Fabian Valencia, 2014, “Monetary Policy in the New Normal,” IMF Staff Discussion Note, April.

    • Search Google Scholar
    • Export Citation
  • Berger, Helge, Tom Dowling, Sergei Lanau, Weicheng Lian, Mico Mrkaic, Pau Rabanal, and Marzie Taheri Sanjani, 2015, “Steady as She Goes—Estimating Potential Output During the Financial “Booms and Busts,” IMF Working Paper (Washington: International Monetary Fund) (forthcoming).

    • Search Google Scholar
    • Export Citation
  • Bernanke, Ben, and Mark Gertler, 1999a, “Monetary Policy and Asset Price Volatility,” Economic Review, Federal Reserve Bank of Kansas City, issue Q IV, pp. 1751.

    • Search Google Scholar
    • Export Citation
  • Bernanke, Ben, Mark Gertler, and Simon Gilchrist, 1999b, “The Financial Accelerator in a Quantitative Business Cycle Framework,” Handbook of Macroeconomics, in: J. B. Taylor and M. Woodford (ed.), Handbook of Macroeconomics, Edition 1, Vol. 1, Chapter 21, pp. 13411393, Elsevier.

    • Search Google Scholar
    • Export Citation
  • Blanchard, Olivier, 2006, “European Unemployment: The Evolution of Facts and Ideas,” Economic Policy, CEPR; CES; MSH, Vol. 21(45), pp. 5–59, 01.

    • Search Google Scholar
    • Export Citation
  • Blanchard, Olivier, and Jordi Galí, 2007, “Real Wage Rigidities and the New Keynesian Model,” Journal of Money, Credit and Banking, Blackwell Publishing, Vol. 39(s1), pp. 35–65, 02.

    • Search Google Scholar
    • Export Citation
  • Blancher, Nicolas, Srobona Mitra, Hanan Morsy, Akira Otani, Tiago Severo, and Laura Valderrama, 2013, “Systemic Risk Monitoring (“SysMo”) Toolkit—A User Guide,” IMF Working Paper WP/13/168 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Boot, Around W. A., and Lev Ratnovski, 2014, “Banking and Trading,” IMF Working Paper WP/12/238 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Borio, Claudio, and Mathias Drehmann, 2009, “Assessing the Risk of Banking Crises—Revisited,” BIS Quarterly Review, March, pp. 2944 (Basel: Bank for International Settlements).

    • Search Google Scholar
    • Export Citation
  • Borio, Claudio, and Haibin Zhu, 2012, “Capital Regulation, Risk-Taking and Monetary Policy: A Missing Link in the Transmission Mechanism?Journal of Financial Stability, 8(4), pp. 236251.

    • Search Google Scholar
    • Export Citation
  • Borio, Claudio, Piti Disyatat, and Mikael Juselius, 2014, “A Parsimonious Approach to Incorporating Economic Information in Measures of Potential Output,” BIS Working Papers 442, (Basel: Bank for International Settlements).

    • Search Google Scholar
    • Export Citation
  • Brunnermeier, Markus K., Thomas M. Eisenbach, and Yuliy Sannikov, 2012, “Macroeconomics With Financial Frictions: A Survey,” NBER Working Papers 18102, National Bureau of Economic Research, Inc.

    • Search Google Scholar
    • Export Citation
  • Bruno, Valentina, and Hyun Song Shin, 2014, “Assessing Macroprudential Policies: Case of South Korea,” Scandinavian Journal of Economics, Wiley Blackwell, Vol. 116(1), pp. 128–157, 01.

    • Search Google Scholar
    • Export Citation
  • Bruno, Valentina, and Hyun Song Shin, 2015, “Capital Flows and the Risk-Taking Channel of Monetary Policy,” Journal of Monetary Economics, Elsevier, Vol. 71(C), pp. 119132.

    • Search Google Scholar
    • Export Citation
  • Caballero, Ricardo J., and Arvind Krishnamurthy, 2003, “Inflation Targeting and Sudden Stops,” NBER Working Papers 9599, National Bureau of Economic Research, Inc.

    • Search Google Scholar
    • Export Citation
  • Calvo, Guillermo A., and Enrique G. Mendoza, 1996, “Mexico’s Balance-of-Payments Crisis: A Chronicle of a Death Foretold,” Journal of International Economics, Elsevier, Vol. 41(3–4), pp. 235264, November.

    • Search Google Scholar
    • Export Citation
  • Calza, Alessandro, and João Sousa, 2006, “Output and Inflation Responses to Credit Shocks: are There Threshold Effects in the Euro Area?Studies in Nonlinear Dynamics & Econometrics, De Gruyter, Vol. 10(2), pp. 121.

    • Search Google Scholar
    • Export Citation
  • Carl E. Walsh, 1998, “Monetary Theory and Policy,” Boston, Massachusetts: MIT Press.

  • Carlstrom, Charles T., Fuerst, Timothy S., and Paustian, Matthias, 2010, “Optimal Monetary Policy in a Model with Agency Costs,” Journal of Money, Credit and Banking, Blackwell Publishing, Vol. 42(s1), pp. 37–70, 09.

    • Search Google Scholar
    • Export Citation
  • Caruana, Jaime, 2011, “Central Banking Between Past and Future: Which Way Forward After the Crisis?speech given at the South African Reserve Bank 90th Anniversary Seminar, July 2011.

    • Search Google Scholar
    • Export Citation
  • Cecchetti, Stephen, Tommaso Mancini-Griffoli, Tommaso, and Machiko Narita, 2015, “Does Persistently Loose Monetary Policy Brew Financial Risks?IMF Working Paper (Washington: International Monetary Fund) (forthcoming).

    • Search Google Scholar
    • Export Citation
  • Stijn Claessens, and Luc Laeven, 2015, “The Use and Effectiveness of Macroprudential Policies: New Evidence,” IMF Working Paper WP/15/61 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Cerutti, Eugenio, Stijn Claessens, and Luc Laeven, 2015, “The Use and Effectiveness of Macroprudential Policies: New Evidence,” IMF Working Paper WP/15/61 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Chen, Jiaqian, Tommaso Mancini-Griffoli, and Ratna Sahay, 2014, “Spillovers from United States Monetary Policy on Emerging Markets: Different This Time?,” IMF Working Paper WP/14/240 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Chen, Qianying, Andrew Filardo, Dong He, and Feng Zhu, 2015, “Financial Crisis, U.S. Unconventional Monetary Policy and International Spillovers,” Journal of International Money and Finance, DOI: 10.1016/j.jimonfin.2015.06.011.

    • Search Google Scholar
    • Export Citation
  • Choi, Henry, Tom Fong, Ka-fai Li, and Eric Wong, 2011, “Loan-to-Value Ratio as a Macro-Prudential Tool—Hong Kong’s Experience and Cross-Country Evidence,” Working Papers 1101, Hong Kong Monetary Authority.

    • Search Google Scholar
    • Export Citation
  • Christiano, Lawrence, and Daisuke Ikeda, 2013, “Leverage Restrictions in a Business Cycle Model,” NBER Working Papers 18688, National Bureau of Economic Research, Inc.

    • Search Google Scholar
    • Export Citation
  • Christiano, Lawrence J., and Terry J. Fitzgerald, 2003, “The Band Pass Filter,” International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, Vol. 44(2), pp. 435465, 05.

    • Search Google Scholar
    • Export Citation
  • Christiano, Lawrence, Cosmin Ilut, Roberto Motto, and Massimo Rostagno, 2010, “Monetary Policy and Stock Market Booms,” Proceedings—Economic Policy Symposium—Jackson Hole, Federal Reserve Bank of Kansas City, pp. 85145.

    • Search Google Scholar
    • Export Citation
  • Claessens, Stijn, Swati R. Ghosh, and Roxana Mihet, 2013, “Macro-Prudential Policies to Mitigate Financial System Vulnerabilities,” Journal of International Money and Finance, Elsevier, Vol. 39(C), pp. 153185.

    • Search Google Scholar
    • Export Citation
  • Clarida, Richard, Jordi Gali, and Mark Gertler, 1999, “The Science of Monetary Policy: A New Keynesian Perspective,” Journal of Economic Literature, American Economic Association, Vol. 37(4), pp. 16611707, December.

    • Search Google Scholar
    • Export Citation
  • Columba, Francesco, A. Costa, Piyabha Kongsamut, Chen Hoon Lim, A. Otani, Mustafa Sayid, Torsten Wezel, Xiaoyong Wu,Macroprudential Policy: What Instruments and How to Use Them? Lessons from Country Experiences,” IMF Working Paper WP/11/238 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Crowe Christopher W., Giovanni Dell’Ariccia, Deniz Igan, and Pau Rabanal, 2011, “Policies for Macrofinancial Stability; Options to Deal with Real Estate Booms,” IMF Staff Discussion Note, February.

    • Search Google Scholar
    • Export Citation
  • Cúrdia, Vasco, and Michael Woodford, 2009, “Credit Frictions and Optimal Monetary Policy,” BIS Working Papers 278, (Basel: Bank for International Settlements).

    • Search Google Scholar
    • Export Citation
  • Dassatti Camors, Cecilia and Jose-Luis Peydro, 2014, “Macroprudential and Monetary Policy: Loan-Level Evidence from Reserve Requirements,” mimeo, Universitat Pompeu Fabra, Spain.

    • Search Google Scholar
    • Export Citation
  • De Nicolo, Gianni, Dell’Ariccia, Giovanni, Luc Laeven, and Fabian Valencia, 2010, “Monetary Policy and Bank Risk Taking,” IMF Staff Position Note 10/09.

    • Search Google Scholar
    • Export Citation
  • Dell’Ariccia, Giovanni, Luc Laeven, and Gustavo Suarez, 2013, “Bank Leverage and Monetary Policy’s Risk-Taking Channel; Evidence from the United States,” IMF Working Paper WP/13/143 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Dell’Ariccia, Giovanni, Luc Laeven, and Robert Marquez, 2014, “Real Interest Rates, Leverage and Bank Risk-Taking,” Journal of Economic Theory, Vol. 149, pp. 6599.

    • Search Google Scholar
    • Export Citation
  • Detragiache, Enrica, and Asli Demirguc-Kunt, 1998, “Financial Liberalization and Financial Fragility,” IMF Working Paper WP/98/83, 238 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Detragiache, Enrica, and Asli Demirguc-Kunt,, 2005, “Cross-Country Empirical Studies of Systemic Bank Distress: A Survey,” National Institute Economic Review, National Institute of Economic and Social Research, Vol. 192(1), pp. 6883, April.

    • Search Google Scholar
    • Export Citation
  • Diamond, Douglas W., and Raghurarm G. Rajan, 2012, “Illiquid Banks, Financial Stability, and Interest Rate Policy,” Journal of Political Economy, University of Chicago Press, Vol. 120(3), pp. 552591.

    • Search Google Scholar
    • Export Citation
  • Diaz Kalan, Federico, Stefan Laseen, Andrea Pescatori, David Vestin, and Aleksandra Zdzienicka, 2015, “Leaning Against the Wind: A Note on Costs and Benefits.” IMF Working Paper 238 (Washington: International Monetary Fund) (forthcoming).

    • Search Google Scholar
    • Export Citation
  • Eickmeier, Sandra, Massimiliano Marcellino, and Esteban Prieto, 2013, “Time Variation in Macro-Financial Linkages,” CEPR Discussion Papers 9436.

    • Search Google Scholar
    • Export Citation
  • ECB, 2014, “Capturing the Financial Cycle in Euro Area Countries,” ECB Financial Stability Review, November.

  • Farhi, Emmanuel, and Jean Tirole 2012, “Collective Moral Hazard, Maturity Mismatch, and Systemic Bailouts,” American Economic Review, American Economic Association, Vol. 102(1), pp. 6093, February.

    • Search Google Scholar
    • Export Citation
  • Faust, Jon, Simon Gilchrist, Jonathan H. Wright, and Egon Zakrasek, 2013, “Credit Spreads as Predictors of Real-Time Economic Activity: A Bayesian Model-Averaging Approach,” The Review of Economics and Statistics, 95(5): pp. 15011519.

    • Search Google Scholar
    • Export Citation
  • Fernández, Andrés, and Adam Gulan, 2015, “Interest Rates, Leverage, and Business Cycles in Emerging Economies: The Role of Financial Frictions,“ American Economic Journal: Macroeconomics,” 7(3): 15388.

    • Search Google Scholar
    • Export Citation
  • Feroli, Michael, Anil K. Kashyap, Kermit L. Schoenholtz, and Hyun Song Shin, 2014, “Market Tantrums and Monetary Policy,” Chicago Booth Research Paper 14–09.