Chapter 4 Global Spillovers

Ana Corbacho, and Shanaka Peiris
Published Date:
October 2018
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Global financial cycles and spillovers pose a challenge for the Association of Southeast Asian Nations–5 (ASEAN-5) countries. Rey (2013) argues that capital flows, asset prices, and credit growth observe a global financial cycle and that the cycle (proxied by the Chicago Board Options Exchange Volatility Index [VIX]) is driven mainly by US monetary policy. Potential surprises from US monetary policy normalization and spikes in global risk aversion are sometimes accompanied by capital outflows and tighter domestic financial conditions, with significant macro-financial effects on ASEAN-5 countries.

This chapter takes stock of the impact of global shocks on ASEAN-5 economies and identifies the main transmission channels of global spillovers. It finds that one key channel related to the VIX affects largely capital flows and asset prices;1 another, linked to US interest rates, affects mainly monetary and credit conditions.2 The chapter estimates global financial factors’ impact on ASEAN-5 growth cycles; these factors are transmitted partly through capital flows and are often amplified by equity prices and domestic credit friction.3 Results show that real economy factors, such as external demand from the United States and more recently from China, are also relevant, but global financial shocks tend to dominate growth dynamics in the ASEAN-5. The policy response of ASEAN-5 economies to these global spillovers is further examined in Chapters 5 and 6.

The extensive global spillovers to the ASEAN-5 are likely to pose new challenges in the period ahead. The chapter concludes with simulations of the potential spillovers from the realization of downside risks facing the global outlook, calibrated to one ASEAN-5 economy (the Philippines). Illustrative model-based scenarios show that faster-than-anticipated monetary policy normalization in the United States, an unproductive US fiscal expansion, or an abrupt growth slowdown in China would hit the ASEAN economies hard through lower external demand and higher financing costs, warranting a policy response. Part III of this book delves deeper into the policy reform agenda necessary to face these new challenges.

Global Financial Factors and Domestic Financial Conditions

Domestic financial conditions in the ASEAN-5 economies are sensitive to global factors.4 Following Miranda-Agrippino and Rey (2015), a principal component model is used to identify the underlying global factors that can explain the variability of a comprehensive set of domestic financial indicators.5 The principal component analysis shows that the first two common components explain about 53 to 65 percent of the variation in domestic financial conditions in the ASEAN-5 economies (Figure 4.1; Table 4.1). In general, in each economy, one of the first two principal components seems to be associated with global interest rates and local currency sovereign bond and retail bank interest rates, while the other component is more closely associated with the VIX, which is related to asset prices and bank credit (see Table 4.2).6 The results indicate that there are potentially two key transmission channels of global financial shocks to domestic financial conditions: one related to the VIX, which affects mainly capital flows and asset prices, and another linked to US interest rates, which affects predominantly monetary and credit conditions. These are examined further in the remainder of the chapter.

Figure 4.1.
Comovement of Latent Factors with Global Factors

Sources: Bloomberg Finance L.P.; CEIC Data Co. Ltd.; Haver Analytics; and IMF staff estimates.

Note: VIX = Chicago Board Options Exchange Volatility Index.

Table 4.1.Proportion of Variance Explained by Principal Factors(Share)
Factor 1Factor 2
Source: IMF staff calculations.
Table 4.2.Cross-Correlation of the Principal Factors with Global and Domestic Variables
US 10-year Government Bond RateVIX IndexNet Portfolio FlowsPolicy Rate1Domestic 10-year Government Bond RateLending RateGrowth of Credit to Private Sector2
IndonesiaFactor 10.670370*0.456806*-0.167171***0.678629*0.980260*0.738652*0.174695**
Factor 20.284439*-0.216273**0.229517*0.235127*0.1071230.360456*-0.551433*
MalaysiaFactor 10.529747*-0.302444*0.0302790.855638*-0.0621900.757896*-0.069818
Factor 20.512834*-0.328599*0.455342*-0.325326*0.190464**0.279002*-0.309277*
PhilippinesFactor 10.799873*0.053589*0.153588***0.799903*0.909129*0.946136*-0.352955*
Factor 20.097824-0.224954**0.1478410.1055390.0119710.0087610.586609*
SingaporeFactor 10.776220*20.332237*0.311110*0.0695230.612826*-0.782458*-0.082982
Factor 2-0.127999-0.239292*0.199834**0.153374***-0.321578*0.370595*0.474466*
ThailandFactor 10.564757*-0.079831-0.0348190.850952*0.593241*-0.396958*0.113971
Factor 20.521675*-0.0792500.240941*0.0043790.514422*-0.667889*-0.201467**
Source: IMF staff estimates.Note: VIX = Chicago Board Options Exchange Volatility Index.*p < 0.01; **p < 0.05; ***p < 0.10.

Both global financial conditions and domestic policy rates seem to influence domestic financial conditions, with the former having a greater impact (Figure 4.2). Simple recursive vector autoregression (VAR) models of the first two factors of domestic financial conditions in the ASEAN-5, following the approach of IMF 2017a, show that US interest rates, the VIX, or both have a significant impact in all countries.7 Policy rates also have a significant impact in most cases, albeit of a lesser magnitude. Variance decompositions of the domestic financial conditions factors confirm that for most ASEAN-5 economies, a greater proportion is explained by global factors than by domestic policy rates. Still, the share of the first two principal components explained by global factors is lower than the share explained by a country’s own shocks, suggesting that other domestic variables and structural factors continue to influence domestic financial conditions in the ASEAN-5.

Figure 4.2.
Global Financial Shocks and Domestic Financial Indicators

Source: IMF staff estimates.

Note: Cholesky ordering: (1) US 10-year government bond yield, (2) VIX, (3) net capital flows in domestic economy, (4) domestic policy rate, (5) principal factors (F1 or F2). “Own impact” refers to the impact of a country’s own shocks on the principal factors. VIX = Chicago Board Options Exchange Volatility Index.

External Factors, Credit Shocks, and Business Cycles

The role of external factors in driving emerging market economic growth is well established.8 The approach in this section follows IMF 2014a and analyzes the relationship between emerging market business cycles and external conditions by assuming that global economic conditions are exogenous to small open emerging market economies, at least on impact.9 Thus, the impact of external shocks on a particular economy depends on how exposed the economy is to these shocks via cross-border links and on how domestic policy stabilizers are allowed to work. Over time, the cumulated effect on domestic growth may be amplified or dampened as domestic policies respond further to external shocks. Although the framework assumes that any contemporaneous feedback effects from emerging market economies’ domestic conditions within a quarter are small enough to be ignored, it allows for these domestic conditions to affect global conditions with a lag.

The chapter uses a Bayesian structural VAR model to quantify the growth effects of external shocks. The external variables (“external block”) include US real GDP growth, the 10-year US Treasury bond rate, the VIX, and economy-specific terms-of-trade growth. Within the external block, the structural shocks are identified using a recursive approach, based on the above order. In other words, US growth shocks can affect all other variables within a quarter, whereas shocks to other variables can affect US growth only with a lag of at least one quarter. Taken together, the US variables in the external block serve as a proxy for advanced economy economic conditions.10 Changes in emerging market financing conditions arising from factors other than external demand conditions are incorporated through the VIX, a measure of global risk aversion. IMF 2014a also shows the rising importance of economic activity in China, directly and indirectly through changes in terms-of-trade growth, to represent factors other than changes in demand from advanced economies.

The impact of external shocks on economic activity could be transmitted through different channels and amplified by structural features and domestic policies. The baseline specification for domestic variables (“internal block”) includes real GDP growth, domestic credit growth to the private sector, the domestic short-term interest rate, the rate of appreciation of the economy’s real exchange rate against the US dollar, and domestic lending rates.11 This specification captures the more traditional transmission channels of external demand and global financing conditions through trade channels and the domestic monetary policy response, including credit, interest, and exchange rate channels. However, as highlighted in the previous section, there may be an additional channel more closely related to the VIX operating through capital flows and asset prices. Thus, an alternative specification includes net capital flows, the foreign exchange sovereign bond yield (from the JPMorgan Emerging Market Government Bond Index [EMBIG]), local currency 10-year government bond yield, and equity prices, with the latter a proxy for net worth of corporations to reflect financial accelerator effects.12

The ASEAN-5 are sensitive to both real and financial external shocks (Figure 4.3). External demand shocks from the United States and China have a significant impact on real economic activity, as expected, with a rising role for China. Chinese GDP shocks have a greater impact than US GDP on Singapore and Thailand, possibly reflecting their role as hubs of regional supply chains in Asia. On the other hand, country-specific global commodity prices, which are partly affected by Chinese shocks, have an additional significant impact in most countries, particularly in Indonesia and Malaysia. Global financial shocks also have a large and significant impact on real economy dynamics. The VIX, a measure of global risk aversion, or US interest rates—or both—are consistently a major influence on growth dynamics in all ASEAN-5 countries, with a rise in the VIX or US interest rates tightening domestic financial conditions and having a contractionary impact on economic activity. Higher US interest rates associated with rising global economic activity are also at times related to higher growth in the ASEAN-5, albeit with the contractionary effect dominating, on average. Variance decompositions corroborate the view that global shocks, in particular global financial shocks, are a major driver of growth dynamics in the ASEAN-5.

Figure 4.3.
Domestic Activity and External Shocks (Baseline Model)

Source: IMF staff estimates.

Note: Cholesky ordering: (1) external factors: US GDP, US interest rate, VIX, and China GDP; (2) domestic factors: terms of trade, GDP, domestic credit, short-term interest rate, REER, and lending rate. ID = Indonesia; JIBOR = Jakarta interbank offered rate; MY = Malaysia; PH = Philippines; REER = real effective exchange rate; SG = Singapore; TH = Thailand; VIX = Chicago Board Options Exchange Volatility Index.

External shocks have a pervasive impact on the economy, operating partly through traditional monetary transmission mechanisms. Global financial factors such as US interest rate or VIX shocks have a strong influence on the traditional interest rate, credit, and exchange rate channels of monetary transmission, with a subsequent impact on real economic activity. Short-term interest rates, in particular, appear to be driven largely by global factors, raising the question as to whether financial globalization has weakened monetary autonomy in the ASEAN-5 countries despite the greater exchange rate flexibility observed since the Asian financial crisis (Chapter 2). Real exchange rate dynamics are driven by global financial shocks as well, with the Philippines and Thailand also affected by domestic credit and terms-of-trade shocks. Singapore’s real effective exchange rate, with its unique, nominal effective exchange rate–based inflation-targeting regime, is influenced by a more diverse set of factors. Domestic bank credit to the private sector is determined by a more balanced set of global and other domestic shocks, including policy variables, suggesting that it may be more amenable to policy actions. Both credit demand and credit supply factors appear to be at play, with real lending rates only one of many factors affecting credit growth paths.

External shocks are also amplified and intermediated through the domestic financial system, partly channeled through capital flows (Figure 4.4). Impulse response functions of alternative specifications including more asset price and balance sheet variables suggest an amplification of global financial shocks through the financial system. The role of capital flows in transmitting and amplifying global shocks is unambiguous in all countries (IMF 2014b). However, the transmission channels are country specific and depend on external exposures, structural factors, and policies. For example, in Indonesia, real lending rates seem to be an important source of economic fluctuations, with yields of domestic government bonds with a substantial foreign exposure significantly explaining their evolution. In other ASEAN-5 economies, equity prices play a significant role in amplifying global financial factors and capital inflows, perhaps through financial accelerator effects that reduce the external finance premium and borrowing costs to firms. Interestingly, local-currency-denominated borrowing from the domestic financial system through bank credit or bonds appears more important than external financing spreads of the sovereign (EMBIG) or firms (JPMorgan Corporate Emerging Markets Bond Index), indicating that reliance on foreign-currency-denominated borrowing has diminished since the Asian financial crisis (see Chapter 3).

Figure 4.4.
Global Factors, Net Capital Flows, and Domestic Activity

(Alternative Model)
(Alternative Model)

Source: IMF staff estimates.

Note: Cholesky ordering: (1) external factors: US GDP, US interest rate, capital flows, and China GDP; (2) domestic factors: terms of trade, GDP, emerging market spread, 10-year sovereign bond yield, REER, equity prices, and lending rate. CEMBI = JPMorgan Corporate Emerging Markets Bond Index; EMBIG = JPMorgan Emerging Market Government Bond Index; FFR = federal funds rate; ID = Indonesia; MY = Malaysia; PH = Philippines; REER = real effective exchange rate; SG = Singapore; TH = Thailand.

Interest Rate Spillovers

Although the influence of global risk aversion on emerging market equity prices has been carefully studied (IMF 2014b; Yilmaz 2010), a closer look at spillovers on ASEAN-5 countries’ domestic interest rates is needed, given their direct implications for the monetary and financial policy framework. How “reserve currency” monetary policies are transmitted to domestic long-term sovereign bond yields is of particular interest given that they act as a benchmark for pricing corporate bonds and household mortgages. The influence of global financial factors and risk aversion on domestic retail bank rates, directly or indirectly, through the monetary transmission mechanism is also important given the dominance of banks in the ASEAN-5 economies.13

Spillovers of Global Financial Shocks on Domestic Long-Term Sovereign Bond Yields

The methodology follows Peiris 2013, estimating an exponential generalized autoregression conditional heteroscedastic (EGARCH [1,1]) model of sovereign bond yields in the ASEAN-5 economies during 2000‒15 using a comprehensive set of macro-financial variables including global factors. The results show that a decline in the shadow US federal funds rate14 reduces long-term government bond yields in all ASEAN-5 economies. An increase in the US term premium, such as during the so-called taper tantrum episode in 2013, also results in higher long-term bond yields in all ASEAN-5 economies. The results indicate that a rise in the shadow federal funds rate and US term premium could have a greater impact on Indonesia and the Philippines (Table 4.3). Greater global risk aversion proxied by the VIX has a mixed effect on long-term rates, with a rise in the VIX increasing yields in Indonesia and the Philippines while lowering yields in Thailand, probably reflecting the greater home bias of Thai financial institutions. Robust fundamentals such as stronger current account balances and lower public debt tend to keep bond yields down. Expectations of currency depreciation can also drive bond yields higher. On an interesting note, better growth expectations often result in lower bond yields than vice versa, suggesting that investors may see better growth prospects as a sign of improved creditworthiness rather than just a cyclical consideration. Overall, the susceptibility of long-term bond yields to global factors is consistent with the high degree of foreign participation in the ASEAN-5 economies, with foreign portfolio capital flows being a key channel of spillovers, albeit with expectations and domestic residents continuing to play a significant role.15

Table 4.3.Determinants of Sovereign Bond Yields (Ten-year government bond)1,2
Domestic FactorsExternal Factors
Debt-to-GDPExpected GDP (real % change, 1-yr forecast)InflationCurrent Account Balance in % of GDP (21)Expected Exchange Rates (1-yr forecast)Share of Foreign Holdings in Total LCY Government BondsVIXEffective Federal Funds RateUS Term Premium
Source: IMF staff estimates.Note: LCY = local currency; VIX = Chicago Board Options Exchange Volatility Index; yr = year.*p < 0.01; **p < 0.05; ***p < 0.10.

Spillovers of Global Shocks on Retail Bank Interest Rates

Spillovers of global factors to retail bank rates in the ASEAN-5 countries were investigated following the approach of Ricci and Shi (2016) by estimating the domestic and global determinants of both deposit and loan rates (Tables 4.4 and 4.5).16 In addition, the specification allows for liquidity effects and rigidities in interest rate transmission. The results indicate that global financial factors significantly affect bank behavior in the ASEAN-5 economies except possibly for Thailand.17 Lending rates are also affected by lagged equity prices, which are a proxy for net worth of firms and reflect balance sheet or financial accelerator effects affecting the cost of bank credit. However, the domestic policy rate and liquidity conditions (measured by the deviation of reserve money from a Hodrick-Prescott trend) also matter, affirming the important role of domestic monetary policy and liquidity management in influencing credit cycles.

Table 4.4.Determinants of Deposit Rates1,2
Domestic FactorsExternal Factors
Policy RateReserve Money GapDeposit Interest Rate (21)VIXFederal Funds RateUS Term Premium
Source: IMF staff estimates.Note: NEER = nominal effective exchange rate; VIX = Chicago Board Options Exchange Volatility Index.*p < 0.01; **p < 0.05; ***p < 0.10.
Table 4.5.Determinants of Lending Rates1,2
Domestic FactorsExternal Factors
Policy RateReserve Money GapLending Interest Rate (-1)Equity Prices (-1)VIXFederal Funds RateUS Term Premium
Source: IMF staff estimates.Note: NEER = nominal effective exchange rate; VIX = Chicago Board Options Exchange Volatility Index.*p < 0.01; **p < 0.05; ***p < 0.10.

What Lies Ahead? Spillovers from Alternative Global Scenarios

Global policy uncertainties are currently elevated, and global shocks could have large spillovers on the ASEAN-5 and emerging markets in general in the period ahead. For instance, faster-than-expected monetary policy normalization in the United States could tighten global financial conditions and trigger reversals in capital flows to emerging market economies, along with US dollar appreciation (Obstfeld 2017). Moreover, despite a decline in election risks, policy uncertainty could well rise further, reflecting, for example, difficult-to-predict US fiscal policies (Obstfeld 2017). In China, failure to address financial stability risks and curb excessive credit growth could result in an unwanted, abrupt growth slowdown, with adverse spillovers to other countries through trade, commodity price, and confidence channels.

This section uses a four-region version of the IMF’s Global Integrated Monetary and Fiscal Model—consisting of China, the Philippines, the United States, and the rest of the world—to quantify potential spillover effects to the Philippines informed by the empirical analyses in the previous sections.18 The simulations are based on three alternative scenarios that illustrate the global outlook under the realization of different downside risks: (1) a faster-than-expected pace of US monetary policy normalization that leads to an unexpected tightening of global financial conditions, (2) an unproductive US fiscal expansion, and (3) a funding shock in China that leads to lower-than-expected growth in China over the medium term.

Faster Monetary Policy Normalization in the United States

In this scenario, faster-paced monetary policy normalization in the United States, including through a gradual reduction in the Federal Reserve’s securities holdings, causes a greater-than-expected tightening of global financial conditions. As discussed in IMF 2014c, this unexpected tightening could be triggered by market misperceptions about the speed of future monetary policy normalization in the United States. The US term premium rises by 20 basis points in 2018 and 2019, and by 15 basis points in the subsequent two years (Bonis, Ihrig, and Wei 2017). This increase in the US term premium, in turn, raises the term premiums in other countries, consistent with the historical correlation for this type of shock. Furthermore, sovereign bond yields in the Philippines increase temporarily in 2018 (based on the estimates in Table 4.3) as investors become more reluctant to hold bonds issued by emerging markets.

Results: As financial conditions unexpectedly tighten, US real GDP falls by 0.5 percent in 2018 and 0.7 percent in 2019 (Figure 4.5). The Federal Reserve responds to market fears quickly by easing its monetary stance relative to the baseline, which helps contain the rise in US short-term interest rates. The adverse spillover to the Philippines could be significant, with real GDP falling by close to 1 percent in 2018 and 2019. The increases in the sovereign risk premium and the term premium raise the real interest rate and the external financing premium for leveraged firms, leading to weaker investment. The increase in the user cost of capital also reduces firm profitability and dividend payments to households and lowers production and labor demand, leading to weaker consumption. In response to weaker domestic private demand and the resulting moderate decline in inflation, the authorities lower the nominal policy interest rate and increase government spending. Improvement in the trade balance, which reflects mainly lower imports and a weaker currency, partially offsets the output loss.

Figure 4.5.
Faster-than-Expected Monetary Policy Normalization in the United States

(Deviation from case with no shocks)

Source: IMF staff estimates.

1 For firms in the tradables sector.

Unproductive US Fiscal Expansion

In this scenario, the United States embarks on a four-year debt-financed fiscal expansion (2018–21) through a combination of reduced taxes on labor and corporate income and increased infrastructure spending (IMF 2017c).19 After four years, the US government adjusts its policy to stabilize the long-term government-debt-to-GDP ratio. During the first two years, households and firms take the fiscal stimulus as temporary in nature and behave accordingly. While US monetary policy responds endogenously to the change in demand, the rest of the world—except China and the Philippines—keeps policy rates at the effective lower bound. The infrastructure spending is assumed to be unproductive, leading to higher US inflation and faster normalization of the US term premium than with productive infrastructure spending. Labor tax cuts go mostly to wealthy households.

Results: During the fiscal expansion period, US real GDP rises by about 0.5 percent, and US monetary policy tightens in response to higher domestic demand and inflation (Figure 4.6). Real US interest rates also rise, and the US dollar appreciates in real effective terms. The US fiscal expansion affects the Philippine economy through the interest rate and the trade channels. The net spillover impact on Philippine GDP is negative (about 0.2 percent) in the short term because global financial conditions tighten more than enough to offset the expected positive gains in trade.

Figure 4.6.
US Fiscal Expansion with Unproductive Infrastructure Investment

(Deviation from case with no shocks)

Source: IMF staff estimates.

1 For firms in the tradables sector.

Funding Shock and Lower Growth Path in China

In this scenario, China follows a lower growth path over the medium term owing to a temporary but persistent funding shock. The shock could be triggered by system-wide turbulence in the Chinese wholesale funding market or a run on short-term asset management products issued by nonbank financial institutions, as described in IMF 2017d. Under this scenario, real GDP growth falls about 2.5 percentage points below the baseline in 2018 and 2019, and remains below the baseline over the medium term. Furthermore, sovereign risk premiums rise in 2018, by 100 basis points in China and by 25 basis points in other economies, excluding the United States.

Results: Notwithstanding the significant output decline in China, the estimated spillovers to the Philippines are relatively moderate (Figure 4.7). Real GDP declines by about 0.6 percent in 2018 and 2019. The external financing premium for Philippine firms rises about 15 basis points in 2018. The currency remains broadly stable in real effective terms, but depreciates by almost 1 percent against the US dollar in real terms.

Figure 4.7.
Lower Growth in China

(Deviation from case with no shocks)

Source: IMF staff estimates.

1 For firms in the tradables sector.


The rising sensitivity of domestic financial conditions in the ASEAN-5 countries to global financial factors is a key macro-financial transmission channel for external shocks. In the ASEAN-5 economies, two key macro-financial channels transmit global financial shocks: one is related to the VIX and affects largely capital flows and asset prices and the other is linked to US interest rates and affects mainly monetary and credit conditions.

The estimated macro-financial transmission of US interest rate and VIX shocks suggests a significant and pervasive impact of global financial factors on ASEAN-5 business cycle fluctuations, transmitted partly through capital flows. The global shocks tend to be amplified by asset prices (“financial accelerator” effects) and credit friction, with domestic short-term rates one of many factors driving business cycles. The susceptibility of asset prices to global factors, particularly via the interest rate structure of the economy, raises the prospect that financial globalization has weakened monetary autonomy in the ASEAN-5 despite the greater exchange rate flexibility observed since the Asian financial crisis. Real economy factors, such as external demand from the United States and more recently China, are also important, but global financial shocks tend to dominate growth dynamics in the ASEAN-5.

The extensive global spillovers to the ASEAN-5 are likely to pose new challenges. Global policy uncertainty is high, and several global policy scenarios, particularly those emanating from China and the United States, could spill over significantly to emerging markets based on historical experience. Illustrative model-based scenarios show that faster-than-anticipated monetary policy normalization in the United States or an abrupt growth slowdown in China would hit the ASEAN-5 economies hard through lower external demand and higher financing costs, warranting a policy response.


    Adler, Gustavo, andCamilo E.Tovar. 2012. “Riding Global Financial Waves: The Economic Impact of Global Financial Shocks on Emerging Market Economies.” IMF Working Paper 12/188, International Monetary Fund, Washington, DC.

    Adrian, Tobias, andNellieLiang. 2016. “Monetary Policy, Financial Conditions, and Financial Stability.” Staff Report 690, Federal Reserve Bank of New York.

    Ahmed, Shaghil, andAndreiZlate. 2013. “Capital Flows to Emerging Market Economies: A Brave New World?Journal of International Money and Finance 48 Part B (November): 22148.

    Akinci, Ozge. 2013. “Global Financial Conditions, Country Spreads and Macroeconomic Fluctuations in Emerging Countries.” International Finance Discussion Paper 1085, Board of Governors of the Federal Reserve System, Washington, DC.

    Anderson, David,Benjamin L.Hunt,MikaKortelainen,MichaelKumhof,DouglasLaxton,Dirk V.Muir,SusannaMursula, andStephenSnudden. 2013. “Getting to Know GIMF: The Simulation Properties of the Global Integrated Monetary and Fiscal Model.” IMF Working Paper 13/55, International Monetary Fund, Washington, DC.

    Bonis, Brian,JaneIhrig, andMinWei. 2017. “The Effect of the Federal Reserve’s Securities Holdings on Longer-Term Interest Rates.” FEDS Notes, April 20.

    Chen, Jiaqin,TommasoMancini-Griffoli, andRatnaSahay. 2014. “Spillover from United States Monetary Policy on Emerging Markets: Different This Time?IMF Working Paper 12/240, International Monetary Fund, Washington, DC.

    Diebold, F. X., andK.Yilmaz. 2014. “On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms.” Journal of Econometrics 182:11934.

    Eichengreen, Barry, andPoonamGupta. 2014. “Tapering Talk: The Impact of Expectations of Reduced Federal Reserve Security Purchases on Emerging Markets.” Policy Research Working Paper 6754, World Bank, Washington, DC.

    Houssa, Romain,JolanMohimont, andChrisOtrok. 2013. “Credit Shocks and Macroeconomic Fluctuations in Emerging Markets.” Working Paper 4281, CESifo Group, Munich.

    International Monetary Fund (IMF). 2014a. “On the Receiving End? External Conditions and Emerging Market Growth before, during, and after the Global Financial Crisis.” In World Economic Outlook, Washington, DC, April.

    International Monetary Fund (IMF). 2014b. “How Do Changes in the Investor Base and Financial Deepening Affect Emerging Market Economies?” In Global Financial Stability Report, Washington, DC, April.

    International Monetary Fund (IMF). 2014c. “IMF Multilateral Policy Issues Report—2014 Spillover Report.” IMF Policy Paper, Washington, DC.

    International Monetary Fund (IMF). 2015. “Financial Spillovers in Asia: Evidence from Equity Markets.” In Regional Economic Outlook Update, Asia and Pacific, Washington, DC, October.

    International Monetary Fund (IMF). 2016a. “Financial Conditions in Asia and the Role of External Factors.” In Regional Economic Outlook Update, Asia and Pacific, Washington, DC, October.

    International Monetary Fund (IMF). 2016b. “The Growing Importance of Financial Spillovers from Emerging Market Economies.” In Global Financial Stability Report, Washington, DC, April.

    International Monetary Fund (IMF). 2017a. “Are Countries Losing Control of Domestic Financial Conditions?” In Global Financial Stability Report, Washington, DC, April.

    International Monetary Fund (IMF). 2017b. “Roads Less Traveled: Growth in Emerging Market and Developing Economies in a Complicated External Environment.” In World Economic Outlook, Washington, DC, April.

    International Monetary Fund (IMF). 2017c. World Economic Outlook: Gaining Momentum?, Washington, DC, April.

    International Monetary Fund (IMF). 2017d, People’s Republic of China—Staff Report for the 2017 Article IV Consultation, IMF Staff Country Report 17/247, Washington, DC.

    Koepke, Robin. 2015. “What Drives Capital Flows to Emerging Markets? A Survey of the Empirical Literature.” Working Paper, Institute of International Finance, Washington, DC.

    Krippner, Leo. 2014. Documentation for United States Measures of Monetary Policy. Wellington: Reserve Bank of New Zealand.

    Litterman,RobertB.1986. “Forecasting with Bayesian Vector Autoregressions: Five Years of Experience.” Journal of Business and Economic Statistics4 (1): 2538.

    Miranda-Agrippino, Silvia, andHélèneRey. 2015. “World Asset Markets and the Global Financial Cycle.” NBER Working Paper 21722, National Bureau of Economic Research, Cambridge, MA.

    Nier, Erlend,TahsinSaadi Sedik, andTomasMondino. 2014. “Gross Private Capital Flows to Emerging Markets: Can the Global Financial Cycle Be Tamed?IMF Working Paper 14/196, International Monetary Fund, Washington, DC.

    Obstfeld, Maurice. 2017. “A Firming Recovery.” IMFBlog, July 24.

    Österholm, Pär, andJerominZettelmeyer. 2007. “The Effect of External Conditions on Growth in Latin America.” IMF Working Paper 07/176, International Monetary Fund, Washington, DC.

    Peiris,ShanakaJayanath. 2013. “Foreign Participation in Local Currency Bond Markets of Emerging Economies.” Journal of International Commerce, Economics and Policy4 (3): 115.

    Rey, Hélène. 2013. “Dilemma Not Trilemma: The Global Financial Cycle and Monetary Policy Independence.” Paper presented at the 25th Jackson Hole Symposium, Wyoming, August2123.

    Rey, Hélène. 2016. “International Channels of Transmission of Monetary Policy and the Mundellian Trilemma.” NBER Working Paper 21852, National Bureau of Economic Research, Cambridge, MA.

    Ricci, Luca, andWeiShi. 2016. “Trilemma or Dilemma: Inspecting the Heterogeneous Response of Local Currency Interest Rates to Foreign Rate.” IMF Working Paper 16/47, International Monetary Fund, Washington, DC.

    Utlaut, Johannes, andBjörnvan Roye. 2010. “The Effects of External Shocks on Business Cycles in Emerging Asia: A Bayesian VAR Model.” Working Paper 1668, Kiel Institute for the World Economy, Kiel, Germany.

    Yilmaz, K.2010. “Return and Volatility Spillovers among the East Asian Equity Markets.” Journal of Asian Economics21 (3): 30413.

Ahmed and Zlate (2013); Nier, Sedik, and Mondino (2014); and Koepke (2015) list global risk aversion as among the most important global determinants of capital flows.

Rey (2016) presents evidence that US monetary policy shocks are transmitted internationally and affect financial conditions even in inflation-targeting economies with large financial markets.

Financial spillovers, as discussed in IMF 2016a and Diebold and Yilmaz 2014, can be transmitted across borders in part via capital flows (IMF 2016b).

See Adrian and Liang 2016 and IMF 2017a for a broader look at drivers and use of domestic financial conditions.

The domestic financial factors included about 25 to 30 financial variables for each economy used to estimate financial conditions indices for Asia in Box 1.4 of IMF 2015.

The 10-year US Treasury yield is used as a proxy for global interest rates in this section because it acts as a benchmark for the global yield curve. In following sections, alternative US interest rates, including the federal funds rate, are used to represent global interest rates as the key reference reserve currency, with other systemic economies’ (China, euro area, Japan, United Kingdom, United States) interest rates less significant a factor in the ASEAN-5.

The recursive VAR is ordered as follows: US 10-year Treasury yield, VIX, net capital inflows, policy rates, and principal components. For parsimony, figures show only the impulse response functions of the first two principal components for each country in response to global and domestic shocks.

Studies analyzing the role of external conditions in emerging markets’ growth include Österholm and Zettelmeyer 2007 for Latin America; Utlaut and van Roye 2010 for Asia; and Adler and Tovar 2012, Akinci 2013, and Houssa, Mohimont, and Otrok 2013 for a more diverse group of emerging markets.

On the other hand, see IMF 2017b for the impact of external factors on trend or medium-term growth in emerging markets.

With the federal funds rate at or near zero and the Federal Reserve’s focus on lowering US interest rates at the long end following the global financial crisis, the 10-year Treasury bond rate or term premium is likely a better proxy for US monetary policy for the analysis. That said, results are robust to using alternative US interest rates.

The baseline model is estimated individually for each ASEAN-5 economy using quarterly data from the first quarter of 2000 through the first quarter of 2017. An alternative specification to the baseline to include real estate prices did not significantly change the results and is not reported here.

The model is estimated individually for each ASEAN-5 economy using quarterly data from the first quarter of 2000 through the first quarter of 2017. The focus is on the period after the 1990s, given the significant structural breaks (for example, the Asian financial crisis) and shifts in policies in these economies during this time. The number of variables and lags chosen for the specification results in a generous parameterization relative to the short sample length. As a result, degrees of freedom are limited such that standard VAR techniques may yield imprecisely estimated relationships that closely fit the data—a problem referred to as “overfitting.” A Bayesian approach, as advocated by Litterman (1986), is adopted to overcome this problem. This approach allows previous information about the model’s parameters to be combined with information contained within the data to provide more accurate estimates (see IMF 2014a).

This section is focused on estimating global spillovers on ASEAN-5 interest rates since 2000, taking into account unconventional monetary policies in advanced economies. For a more detailed focus on the impact of unconventional monetary policies and their potential unwinding on emerging markets, see Chen, Mancini-Griffoli, and Sahay 2014 and Eichengreen and Gupta 2014.

The federal funds rate provides the conventional measure of the US monetary policy stance, but at a near-zero rate since the end of 2008 it cannot capture the role of unconventional monetary policy. This prompts the consideration of other measures, including a shadow short rate (Krippner 2014). The shadow short rate is computed using estimates from a two-state variable shadow yield curve and has historically tracked the actual federal funds rate very closely, before reaching the zero lower bound.

The degree of foreign participation has a direct impact on sovereign bond yields in the ASEAN-5 as in other emerging markets (see Peiris 2013), while the role of global financial factors also remains significant. The impact of quantitative easing in the euro area and Japan was not distinguishable with US financial variables, which are the dominant global factor for the ASEAN-5. The increasing spillovers from China to emerging markets’ financial markets, as reported in IMF 2016b, were also not discernible in the quarterly data from 2000–15 given the frequency of the sample.

The empirical methodology follows Ricci and Shi 2016 in assessing the robustness of the findings to alternative specifications and subsample estimations, but the results were largely unchanged from the ordinary least squares estimates below for the full sample period, allaying concerns of omitted variable bias and structural breaks. The robustness of the results to alternative publicly available retail bank rate data was also tested, although supervisory data on bank deposit and loan rates were unavailable and may provide a more accurate measure of financing costs.

The increase in provisioning rates by the Bank of Thailand and tightening of banks’ lending standards, probably related to rising household leverage, may explain the different results for Thailand.

See Anderson and others 2013 for simulation properties of the Global Integrated Monetary and Fiscal Model.

This scenario is based on the “unproductive” infrastructure spending scenario in Scenario Box 1 of the April 2017 World Economic Outlook (IMF 2017c). The latter, however, used the IMF’s G20 model for simulation. The estimation results here and in the World Economic Outlook are qualitatively similar, although the magnitude is generally smaller in this simulation.

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