From Fragmentation to Financial Integration in Europe
Chapter

Chapter 4. Financial, Sovereign, and Macro Risk in the European Union: Contingent Claims Approach

Author(s):
Charles Enoch, Luc Everaert, Thierry Tressel, and Jianping Zhou
Published Date:
December 2013
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Author(s)
Dale Gray

One of the key features of the recent global and euro area financial crises is the role of interconnectedness across financial institutions, sectors of the economy, and across borders. A Contingent Claims Analysis (CCA) framework lends itself to analyzing such interconnections and the transmission of risk within and outside the EU. Such a framework was used for two applications. First was a CCA-network model to estimate the connectedness between 63 banks, 39 insurers, and 17 sovereigns, most of which are in the EU (but some from the United States and Japan are also included). This model shows how interconnectedness evolved during the different stages of the financial crisis that began in 2008 and the more recent sovereign-financial crisis in the EU. The second application is a model framework for the analysis of interactions among banking sector risk, sovereign risk, corporate sector risk, real economic activity, and credit growth for 15 European countries and the United States. Key to the framework is that sovereign credit spreads, banking system credit risk, corporate sector credit risk, economic growth, and credit variables are combined in a fully endogenous setting. This framework permits an analysis of the impact and spillovers of shocks, and helps identify policies that help mitigate banking system, sovereign credit risk, and recession risk.

Introduction

Contingent Claims Analysis (CCA) indicators capture the nonlinearity of changes in bank assets, equity capital, bank credit spreads, and default probabilities that are derived from forward-looking equity market information, in conjunction with balance sheet data. It captures the expected losses, spreads, and default probability for sovereigns.

The first application is a CCA-network network model to estimate the connectedness between 63 banks, 39 insurers, and 17 sovereigns, most of which are in the EU (the United States and Japan are also included). It has the advantage of using forward-looking CCA risk indicators and network connectedness measures (Granger causality and degrees of connectedness) during different stages of the financial crisis that began in 2008, and the more recent sovereign-financial sector crisis in the EU that began in 2010. The data used is monthly data from 2002 to March 2012.1

The second application is a CCA global vector autoregression (GVAR) model framework for 16 countries. For each country, there is banking system CCA risk indicator, sovereign risk indicator, corporate sector risk indicator, economic growth, and credit to the private sector. The data is based on a monthly sample covering the period from January 2002 to December 2012 (132 observations). After estimation, the model is used to conduct scenario simulations, involving multiple shocks to selected sovereigns and banking systems. Input shocks and output responses for the banking systems, corporate sectors, and sovereigns can be transformed into credit spreads and interpreted as changes in bank funding costs, sovereign credit spreads, and corporate sector funding costs. Both positive and negative shock scenarios are illustrated. Such output responses could be input to banking/sovereign submodules, which are used to compute aggregate loss estimates and changes in bank capital.2

Interactions Between Financial Institutions and Sovereigns

There are numerous channels of interaction between the sovereign and the banks. As shown in Figure 4.1, the mark-to-market fall in the value of sovereign bonds held by banks reduces bank assets. This can increase bank-funding costs, and if the sovereign is distressed enough, the value of official support (guarantees) will be eroded. These have knock-on effects, as shown. An adverse feedback loop ties sovereigns’ stresses to banking-sector challenges. In some situations, this vicious cycle can spiral out of control, resulting in the inability of the government to provide sufficient guarantees to banks and leading to a systemic financial crisis and a sovereign debt crisis. In such cases, the relationship of expected losses (ELs) of sovereigns, ELs of banking systems, and gross domestic product (GDP) are interrelated in a way subject to costly destabilization processes. If funds for a banking sector recapitalization come from increased sovereign borrowing, this increases sovereign risk.

Figure 4.1Spillovers from the Sovereign to the Banks and Banks to Sovereign

Negative-feedback effects could arise in a situation where the financial system is outsized compared to the government. Thus, distress in the financial system triggers a large increase in government financial guarantees/contingent liabilities. Potential costs to the government, due to the guarantees, can lead to a rise in sovereign spreads. Bank’s spreads depend on retained risk, which is lower given the application of government guarantees, and also on the creditworthiness of the sovereign (as a result of fiscal sustainability and debt service burden), as investors view the bank’s and the sovereign’s risks as intertwined. Concern that the government balance sheet will not be strong enough for it to make good on guarantees could lead to deposit withdrawals or a cutoff of credit to the financial sector, thereby triggering a destructive feedback loop in which both bank and sovereign spreads increase. In some situations, this vicious cycle can spiral out of control, resulting in the inability of the government to provide sufficient guarantees to banks, and leading to a systemic financial crisis and a sovereign debt crisis (see Gray, Merton, and Bodie, 2007; Gray, Jobst, and Malone 2010; Gray and Jobst 2011; and Jobst and Gray 2013 for more on financial, sovereign, and macroeco-nomic/macro-financial interactions).

Contingent Claims Analysis for Financial Institutions and Sovereigns

CCA originated with the Merton model, and it provides a methodology to combine balance sheet information with widely used finance and risk management tools to construct marked-to-market balance sheets that better reflect underlying risk (see Merton, 1973, 1974; and Gray, Merton, and Bodie, 2008). It can be used to derive a set of risk indicators for individual firms and financial institutions, which can serve as risk indicators and barometers of vulnerability, and in particular, to calculate default probabilities. An estimate of the market value of assets and asset volatility is needed, but the market value of assets is not directly observable, because many of the assets on the balance sheet of a financial institution are not traded. CCA imputes the value and volatility of assets indirectly, using the market value of equity from stock price data, equity volatility (from equity data and/or equity options), and the book value of short- and long-term obligations. This is then used to calculate risk indicators, such as the probability of default, credit spreads, or other risk indicators.

CCA balance sheets are risk-adjusted balance sheets. On the CCA balance sheet, the total market value of assets, A, at any time, t, is equal to the sum of its equity market value, E, and its risky debt, D, maturing at time, T. The asset value is stochastic and may fall below the value of outstanding liabilities. Equity and debt derive their values from the uncertain assets. As pointed out by Merton (1973), equity value is the value of an implicit call option on the assets, with an exercise price equal to default barrier, B. The value of risky debt is equal to default-free debt, minus the present value of expected loss due to default. The firm’s outstanding liabilities constitute the bankruptcy level. The expected loss due to default can be calculated as the value of an implicit put option on the assets, A, with an exercise price equal to the default-free value of debt, B, over time horizon, t, risk-free rate, r, and asset volatility σA. The implicit put option value will be called the expected loss value, ELV.

Risky debt = default-free debt – expected loss due to default

Equity values are consensus views of market participants and thus provide forward-looking information. The value of assets is not directly observable, but it can be implied using CCA. The calibration of the model for banks and firms uses the value of equity, the volatility of equity, and the distress barrier as inputs into two equations in order to calculate the implied asset value and implied asset volatility.3 The implied asset value and volatility can then be used with the other parameters to calculate risk indicators, such as the spreads, the implicit put option, default probabilities, and other risk indicators. There are a variety of techniques that can be used to calibrate the CCA parameters.

From the CCA model, the credit spread, s, can be written as:

where the D = Be−yT risky debt and B = the default barrier, T is the time horizon and r is the risk-free rate. The expected loss ratio4 (defined as EL) is the expected loss value per unit of default-free debt and it is equal to:

In principle, there are a variety of CCA calibration techniques that could be used to calculate CCA parameters for individual banks, insurance companies, and firms. For the calibration of implied assets and volatility in the original Merton model, the following inputs are used: market capitalization value and volatility, default barrier estimates from promised payments on debt, the risk free rate, and a time horizon.5 After the calibration, the expected loss value, ELV (i.e. implicit put option value), and the expected loss ratio (EL) which in turn are used to calculate credit spreads; the credit spread from the CCA model derived from equity and balance sheet information is called the “fair value spread.”

Strong evidence supports the claim that implicit and explicit government backing for banks depresses bank credit default swap (CDS) spreads to levels below where they would be in the absence of government support. Bank creditors are the beneficiaries of implicit and explicit government guarantees, but equity holders are not. CCA, which uses bank equity market information together with balance sheet data, can estimate credit risk indicators and infer a fair value CDS spread (FVCDS) for financial institutions. The FVCDS is an estimate of the spread without implicit or explicit government support, thus disentangling its effect. Several studies have shown that for banks during the crisis in 2008–2009, the CCA-based fair value spreads are higher than the observed market CDS spreads in many cases (see Gray, Merton, and Bodie, 2008; Gapen, 2009; Moody’s Analytics, 2010; Gray and Jobst, 2011; and Schweikhard and Tsemelidakis, 2012). The observed CDS spreads of banks are lower than fair value spreads because of the effect of implicit and explicit government guarantees on observed CDS, especially in times of crisis; thus, the bank CDS is distorted. Also, it is observed that for banks in countries with very high sovereign spreads, the observed CDS is frequently higher than the fair value spreads.

A database from Moody’s CreditEdge provides a long time series of risk indicators, calculated in a consistent manner, which can be used to calculate what Moody’s CreditEdge refers to as the FVCDS. This FVCDS is a good proxy for the fair value spread we need, so we can use it to obtain the individual bank expected loss ratio, ELb, insurer expected loss ratio, ELb, and corporate expected loss ratio, ELc. These expected loss ratios have a five-year horizon (T = 5), monthly frequency, and are reported in basis points.

For countries in the euro area periphery (Greece, Ireland, Italy, Portugal, and Spain), Figure 4.2 shows the average bank FVCDS was higher than average observed bank CDS, and higher than the average observed sovereign CDS spread during the 2008–2009 crisis period. However, from mid-2010 to 2012, both the observed bank and sovereign CDS spreads are higher than the bank FVCDS.

Figure 4.2Eurozone Periphery Average Sovereign CDS, Bank CDS, and Bank FVCDS

(Basis points, average 5-year spreads).

Sources: Bloomberg, L.P.; Moody’s Analytics; and IMF staff estimates.

Note: CDS = credit default swaps; FVCDS = fair value CDS.

The FVCDS and its associated expected loss ratio ELb are not distorted in a major way by the effect of government guarantees (situations where FVCDS > CDS in the 2008 to 2009 period), or from spillovers from high sovereign spreads (situations where CDS > FVCDS seen in the 2010 onward in Figure 4.2). Our aim is to try to measure a bank’s pure “stand alone” risk, minimizing distortions caused by implicit and explicit guarantees or sovereign spillovers, so we will use a measure of fair value spreads for each financial institution and the associated EL.

The relationships between bank expected default frequency (EDF), the FVCDS, EL, and the ratio of market capitalization to assets are nonlinear, as shown in Box 4.1. In the center of Figure 4.3, the areas within the respective dotted lines show a “safe zone” where the relationships are less nonlinear and an even safer “investment grade” zone. Various negative or positive shocks—and combinations of policies—can push a bank out of, or into, the safe zone/investment grade zone.

Figure 4.3Relationships between Contingent Claims Analysis Capital Ratio (CCACR), Expected Default Frequency (EDF), fair-value credit default swap (FVCDS), and Expected Loss Ratio for a Typical Bank

Sources: Moody’s CreditEdge data; and author’s estimates.

The smaller rectangle in the center of Figure 4.3 is the safest zone, “investment grade” zone and above: CCACR 3 percent and above, EL of 1000 basis points or less, spreads less than 200 basis points, and EDF of less than 0.5 percent. The slightly larger “safe” zone, just below investment grade, in Figure 4.3 is where EDFs are less than 1.5 percent, spreads are 400 basis points or less, EL is less than 2000 basis points, and CCACR is above 2.5 percent.

Box 4.1Relationships between Contingent Claims Analysis Capital Ratio, Expected Default Frequency, Fair-Value Credit Default Swap Spreads, and Expected Loss Ratio for a Typical Bank

It is useful to understand the nonlinear relationships between the ratio of market capital to assets, the CCA capital ratio (CCACR), the expected default frequency (EDF), the spread (FVCDS spread), and the expected loss ratio (shown in the graphs as a fraction), as illustrated in Figure 4.3. This is the typical pattern for a bank which has experienced periods of distress and nondistress. It is compiled from a data sample covering approximately three years that is taken from the CreditEdgePlus database (Moody’s). If the CCACR is high, 0.8 to 0.9 (8 or 9 percent), EDF is very low, the EL is low (around 0.05, or 500 basis points), credit spreads are low (around 100 basis points). In distress periods, when the CCACR falls from 0.3 to 0.1, the EDF is very high (6 to 7 percent), spreads are 700 to 900 basis points, and the EL is 0.3 to 0.4 (equal to 3000 to 4000 basis points). Once the capital ratio starts moving below 3 percent, the EDF increases over 1 percent, spreads go over the 250 basis points “threshold” and EL is higher than 1000 basis points there is increasing risk of negative shocks leading to sharply higher spreads and EDFs. The dynamics are nonlinear.

The relationship between Moody’s ratings, one-year EDFs, and fair-value spreads is shown in Figure 4.4. Investment grade is defined as ratings BBB- and higher. Spreads of 400 basis points or less corresponds to EDFs of about 1.5 to 2 percent and ratings of B or higher. The sharp increase in spreads and EDFs at rating below B shows significant non linearity. The comparison of ratings spreads and EDFs is shown in Figure 4.4. Once the rating becomes sub-investment grade, the spreads and default probabilities increase sharply as ratings decline further.

Figure 4.4Comparison of Ratings, Spreads, and Expected Default Frequencies (EDFs)

Sources: Gray and others (forthcoming); Moody’s CreditEdge data; and author’s estimates.

One can think of the “safe zone” as a target zone that one would like to reach using combinations of policies (capital injections, increasing the level and lowering asset volatility, and risk transfer policies described in more detail later).

For the EU banks in the Moody’s CreditEdge database, Table 4.1 give the statistics by size range—assets (market value of assets), market capitalization, EDF, FVCDS, CDS value, and the sensitivity value of percent change in market cap, divided by the percent change in assets (derived from scenario analysis). Insurance companies are also shown.

Table 4.1EU Banks—Contingent Claims Analysis Statistics for 136 Banks in EU Countries
Bank size range (millions of euros)NumberAssets (millions of euros)Market capitalization (millions of euros)Market capitalization to assets (percent)Debt (DB) (millions of euros)EDF one-year, (percent)FVCDS (basis points)CDS (basis points)Percent change in market capitalization/percent assets ratio
1,000,000 to 2,000,000811,798,600451,2463.8210,163,0671.2464018112.2
500,000 to 1,000,00053,243,830137,6004.242,783,0831.0660420311.2
100,000 to 500,000193,983,896241,2796.063,313,1351.3860630010.7
0 to 100,0001041,540,59273,4184.771,333,4913.6064361913.5
Totals/Averages13620,566,919903,5434.7217,592,7751.8262332612
Insurance Companies595,963,580374,5756.283.05394458.9
Sources: Moody’s Credit Edge; and author’s estimates.Note: Debt is default barrier, CDS if available, otherwise FVCDS is used for an average. A shock in percent of assets multiplied by sensitivity value gives change in market capitalization. CDS = credit default swaps; EDF = expected default frequency; FVCDS = fair value credit default swap
Sources: Moody’s Credit Edge; and author’s estimates.Note: Debt is default barrier, CDS if available, otherwise FVCDS is used for an average. A shock in percent of assets multiplied by sensitivity value gives change in market capitalization. CDS = credit default swaps; EDF = expected default frequency; FVCDS = fair value credit default swap

For the sovereigns, we do not have equity values, so we use actual market sovereign CDS spreads, because we assume there is no one guaranteeing their debt and CDS should reflect the sovereign credit risk (see Merton and others, 2013).6 Recent analysis of sovereign CDS (IMF, 2013) shows the sovereign CDS market is not prone to speculative excesses, nor does it lead to higher sovereign funding costs, and this market does not appear to be more prone to high volatility than other financial markets. The CDS expected loss ratio for a sovereign is a function of the time horizon and the sovereign CDS.

CCA-Network Models

The CCA-network model uses inputs from the individual CCA risk indicators in a network model using connectedness measures. The model uses ELs from 63 individual banks, insurers, and 17 sovereigns, including 13 EU countries, plus Japan, Norway, Switzerland, and the United States (ELs calculated as described in the previous section.

To estimate feedback effects of credit quantitatively, Granger causality tests take the EL of entity X at time, t, and relate it to the EL of entity Y at time, t + 1. If, for example, entity X is a sovereign, the sovereign credit measure EL is related to the EL credit measure of entity Y—perhaps a domestic bank (or insurer) or another sovereign’s bank (or insurer)—in the next period (month). Then, the model is estimated in the other direction. If something happens to the credit of domestic bank Y, how does it affect sovereign X’s credit? Equation 4 presents the formal Granger causality test.

If the set of bj coefficients is statistically significant, then Y influences or “Granger-causes” X. Similarly, if the set of cj coefficients is significant, then X influences or “Granger-causes” Y. If both sets of coefficients are significant, then there is mutual influence between Y and X. Of course, Y and X can be any pair of entities. It is important to understand that, in addition to assessing general connectedness between two entities, we are assessing the direction of the connectedness; for example, it may be that Y influences X, but X does not influence Y.7 The degree of connectedness between banks (BAN), sovereigns in euro area periphery countries (SOV-PER), sovereigns in euro area non-periphery countries (SOV-NPER), and insurers (INS) for different periods during the crisis are shown in Table 4.2.8

Table 4.2Degree of Interconnectedness between Banks, Insurers, and Sovereigns: Various Stages of the Financial Crisis(percent significant 99 percent level or higher)
TO
BANSOV-PERSOV-NPERINS
FROMJul04–Jun07
BAN3.90%0.94%0.54%1.93%
SOV-PER1.88%0.00%3.33%3.59%
SOV-NPER4.71%6.67%6.82%5.13%
INS7.02%5.13%6.20%5.87%
Sep05–Aug08
BAN13.51%4.06%7.94%17.50%
SOV-PER26.88%0.00%43.33%46.67%
SOV-NPER13.77%23.33%34.09%33.33%
INS7.36%0.51%5.77%17.34%
Apr09–Mar12
BAN9.14%7.81%2.86%6.21%
SOV-PER21.88%25.00%13.33%22.05%
SOV-NPER9.18%8.33%6.82%9.83%
INS9.81%2.56%1.07%8.30%
Source: Billio and others (forthcoming).Note: BAN = banks; SOV-PER = sovereigns in euro area periphery countries; SOV-NPER = sovereigns in non-euro area periphery countries; INS = insurers.
Source: Billio and others (forthcoming).Note: BAN = banks; SOV-PER = sovereigns in euro area periphery countries; SOV-NPER = sovereigns in non-euro area periphery countries; INS = insurers.

The degree of interconnectedness between banks, insurers, and sovereigns varies with the stages of the crisis. The numbers in Table 4.2 are percent of causal connections out of the total number of possible connections that were significant at 99 percent or higher confidence level. Interconnections are not symmetric; sovereigns seem to affect banks and insurers (and other sovereigns) more than the reverse. Interconnections were low before the crisis, but increased sharply during the global financial crisis. From September 2005 to August 2008, the non-periphery sovereigns were affecting other sovereigns and insurers, and periphery sovereigns were affecting non-periphery sovereigns, banks, and insurers. During the European sovereign debt crisis and Greek credit event (April 2009 to March 2012), the periphery sovereigns, in particular, were having a big impact on each other and on banks and insurers.

By integrating network models using CCA risk indicators between sovereigns and selected types of financial institutions (banks and insurers together), we can gauge how, when, and how strongly sovereign risks are transmitted to financial institutions and vice versa.4Figure 4.5 shows the percentage of significant connections to sovereigns from financial firms (banks and insurers), and from financial firms to sovereigns from January 2001 through March 2012. An examination of results shows that from 2003 to 2005 the proportion of significant connections to sovereigns from financial institutions was greater, whereas the reversed (connections from sovereigns to institutions) was dominant from mid-2009 to 2012 (right-hand side of Figure 4.5).

Figure 4.5Network Measures: Degrees from and to Sovereigns

Source: Merton and others (2013); reproduced in IMF (2013).

Note: The x-axis captures 36-month rolling windows from January 2001 through March 2012.

CCA-Global VAR Model

To analyze the interactions between banking, sovereign, and corporate risks and real economic activity, an integrated macroeconomic systemic risk model framework was built, drawing on the advantages of forward-looking CCA risk indicators for the banking systems in each country, forward-looking CCA risk indicators for sovereigns, and a GVAR model to combine the banking, the sovereign, and the macro sphere. Key to the framework is that sovereign credit spreads, banking system credit risk, corporate sector credit risk, economic growth, and credit variables are combined in a fully endogenous setting.

Individual banks’ and nonfinancial corporations’ risk indicators that belong to country j were aggregated by computing a weighted average of the expected loss ratios of major banks in the country (weighted respectively by bank or corporate asset size, i.e. the market value of assets). The banking system expected loss ratio is ELbs,j, and the corporate sector expected loss ratio is ELcs,j in country j.

After estimating and calibrating the global model, it was used to conduct shock scenarios, particularly to bank or sovereign risk (for a full description of the model, see Gray and others, forthcoming). The goal is to use this framework to help analyze the impact and spillover of shocks, and to help identify policies that mitigate banking system, sovereign credit risk and recession risk—policies that include bank capital increases, purchase of sovereign debt, and potential guarantees.9

The model is used to conduct scenario simulations, involving multiple shocks to selected sovereigns and banking systems. Input shocks and output responses for the banking systems, corporate sectors, and sovereigns can be transformed into credit spreads and interpreted as changes in bank funding costs and sovereign credit spreads. The output responses are inputs to banking/sovereign submodules, which are used to compute aggregate loss estimates and changes in bank capital.

Taking Italy as an illustration, one can clearly note how the banking sector expected loss ratio (here at a five-year horizon) spiked when real activity dropped sharply in the course of 2008–09 (Figure 4.6). More recently, banking, sovereign, and corporate risk indicators increased significantly, and while sovereign risk indicators fell back below their peaks, financial institutions’ indicators continue to show stresses. Similar indicators for all countries are shown in Appendix 4.1.

Figure 4.6Italy: Sovereign, Banking System, Corporate Sector Expected Loss, Real GDP Growth, and Credit Growth

Sources: Eurostat; Gray and others (forthcoming); Moody’s; and author’s estimates.

Shock Scenario Analysis

To illustrate the linkages among sovereigns, the financial sectors, the corporate sectors, GDP growth, and credit growth, the global model was subject to four different shock scenarios: negative and positive shocks to banks and sovereigns, respectively, of Italy and Spain. Two indicators are reported: the shock profile on impact and the largest cumulative shock for a two-period horizon (see Gray and others, forthcoming, for details). It is important to note that the starting point of the model simulations was December 2012.10 All expected loss values are converted in fair value spread equivalents for ease of comparison.

Shock Scenario One—Adverse Shock to Sovereigns in Italy and Spain

The adverse shocks to the sovereign EL, with marginal shock probabilities set to 5 percent (resulting in a joint probability of 0.7 percent), let the ELs for the two countries increase by about 250–260 basis points on impact of the scenario. Responses are pronounced for other euro area periphery countries, such as Ireland and Portugal.

Spillovers to banking system ELs are notable for Greece, Ireland, Italy, Portugal, and Spain. The corporate sector EL shock profile on impact shows smaller effects, yet suggests a rather similar ranking of countries, with Greece, Ireland, Portugal, and Spain, attaining the highest ranks with respect to corporate sector EL deviations.

With respect to GDP and credit, impact rankings suggest strong effects for Greece, Ireland, and Spain, with their GDPs falling by between -0.6 percent (Spain) and -1.4 percent (Greece) in the first month of the simulation horizon. Credit to the private sector for the first five most strongly affected countries would be contracting by -0.5 percent (Greece) and -1.4 percent (Italy) in the first month.

Turning to the maximum cumulative deviations, the same set of euro area periphery countries attain the highest ranks (Figure 4.7). For Ireland and Portugal, the simulated sovereign fair value CDS responses equal about 250 basis points. The Italian and Spanish banking systems have cumulative maximum responses ranging around 140 basis points. Corporate sector responses appear moderate. Maximum deviations for GDP and credit can again be observed for several euro area periphery countries. Overall, the scenario thus implies sizable adverse responses for risk across sovereigns, banks, and the corporate sector. Real activity and credit contract markedly.

Figure 4.7Shock Scenario One—Sovereigns’ versus Banks’ Maximum Cumulative Fair-Value Spread Responses

Source: Gray and others (forthcoming).

Note: CDS = credit default swaps.

Shock Scenario Two—Adverse Shock to Banking Systems in Italy and Spain

The adverse shocks to banking systems in Italy and Spain, with marginal probabilities set to 5 percent (resulting in a joint probability of 0.8 percent), imply shock sizes to the bank EL ratios for the two countries of 665 and 275 basis points, respectively. With regard to the corporate sector ELs, responses are large in France, Greece, Italy, and Portugal. Most adverse cumulative deviations are again summarized for a smaller subset of strongly affected countries in Figure 4.8.

Figure 4.8Shock Scenario Two—Sovereigns’ versus Banks’ Maximum Cumulative Fair-Value Spread Responses

Source: Gray and others (forthcoming).

Note: CDS = credit default swaps.

In comparison to the sovereign shock scenario one, it can be seen that responses are now more pronounced along the bank EL dimension, and relatively less along the sovereign dimension. Corporate sector responses are high for Portugal. Scenario two results imply a cumulative contraction of GDPs up to -0.5 percent for Italy, and let credit to private sector contract by close to -2.5 percent for Spain.

Shock Scenario Three—Positive Shock to Sovereigns in Italy and Spain

The positive shocks to the sovereigns, with marginal shock probabilities set to 5 percent (resulting in a joint probability of 1.6 percent), mean the ELs for Italy and Spain fall by about 290–352 basis points on impact of the scenario.

Turning to cumulative responses to assess how much deviation the scenario implies for along the horizon, Figure 4.9 scatters sovereign and bank fair value spread deviations for a small subset of countries.

Figure 4.9Shock Scenario Three—Sovereigns’ versus Banks’ Minimum Cumulative Fair-Value Spread Responses

Source: Gray and others (forthcoming).

Note: CDS = credit default swaps.

For the countries shown in Figure 4.9, all sovereign and banking system fair value spreads would move back into the “safe zone,” comprising less than 400 basis points for banking systems and sovereigns. For the sovereigns, fair value spreads would fall by between -70 basis points for Belgium and -310 basis points for Portugal. For the banking systems, the cumulative deviations range between -500 and -900 basis points for Italy and Belgium, respectively. The simulated corporate sector responses for the median of the countries range between -65 basis points for Belgium and -400 basis points for Greece. With respect to GDP and credit growth, the scenario implied relative strong positive cumulative reactions for all countries. Cumulative credit growth rises in Belgium, Greece, Ireland, Italy, and Spain.

Shock Scenario Four—Positive Shock to Banking Systems in Italy and Spain

The positive shocks to the banking systems, with marginal shock probabilities set to 5 percent (resulting in a joint probability of 0.8 percent) let the ELs for banks in Italy and Spain fall by about 1,700–580 basis points on impact of the scenario. Italy’s end-sample EL ratio would fall by over 50 percent. The scenario therefore envisages a sizable positive impulse to the two banking systems.

Real GDP responses on impact are somewhat less pronounced when compared to scenario three, with the most positive response being recorded for Spain’s GDP that would rise by +0.9 percent upon arrival of the shock. Credit growth responses, on the other hand, are somewhat more pronounced on average compared to scenario three, with credit in Spain for instance growing by 2.7 percent. Figure 4.10 presents the most positive cumulative responses along the simulation horizon again for the subsample of countries for which responses are most pronounced.

Figure 4.10Shock Scenario Four—Sovereigns’ versus Banks’ Minimum Cumulative Fair-Value Spread Responses

Source: Gray and others (forthcoming).

Note: CDS = credit default swaps.

Despite the banking systems (in Italy and Spain) having been the shock origins in scenario four, it is Belgium’s and Portugal’s sovereigns that move back into the safe zone that is delineated by the 400 basis point threshold for the fair value spread. Their banking system fair value spreads remain at elevated levels, at 770 basis points for Belgium and 550 basis points for Portugal. To the extent that scenarios three and four are comparable in terms of severity in a probabilistic sense (1 percent marginal shock probabilities), the simulation results suggest that positive impulses to sovereign risk have more potential to compress jointly banks’ and sovereigns’ risk, as measured by their fair value credit spreads.

Refinements and Extensions

The framework developed here may be a tool to assess the combinations of policies that reduce risk for banking systems and sovereigns while increasing real GDP growth. Going forward, there are several extensions and refinements to the framework described above. One is to consider alternative thresholds/criteria for defining the boundaries of the “low risk zone.” This framework could be adapted for conditional/unconditional forecasting of CCA-GVAR model variables. Also, additional fiscal variables and sovereign debt analysis could be included.

One of the benefits of CCA is its ability to compare different types of risk mitigation policies, both on balance sheet changes and risk transfer-type instruments and policies, as shown in Table 4.3. Ways to mitigate risk (lower the EL and reduce spreads) are to increase bank capital or debt to equity conversion (bail-in). Guarantees on bank debt or toxic ring-fenced asset guarantees will lower spreads and reduce risk. For sovereigns, ways to mitigate risk include increasing debt maturity, having debt roll-over backstops from supra national organizations, and credible long-term fiscal policies. Sovereign debt purchases or explicit guarantees by a public entity (ECB, the European Stability Mechanism, or other entity) help lower sovereign spreads. Other policies such as Outright Monetary Transactions (OMT) purchases, or potential for OMT purchases of sovereign debt, can lower sovereign spreads, lower risk, and have positive growth impacts.

Table 4.3Risk Mitigation Policies
On-balance sheet adjustment policies to mitigate risk to:Risk transfer-type instruments and policies to mitigate risk to:
BanksSovereignBanksSovereignFirms
Increase market capitalIncrease regulatory capital; increase solvency ratioReduce or increase maturity of debtGuarantees on bank senior debt; asset protection guaranteesGuarantees or insurance or selling CDS protection on sovereign debtIncentives for banks to lend to firms
Increase assets and lower asset riskMacroprudential policies that affect credit growthRaise assets and lower asset riskEU-wide deposit insuranceDebt purchases by banks (e.g., LTRO) Debt purchases by public entity (SMP/OMT, EFSF/ESM, other)Corporate debt (or equity) across the board purchases by government or central bank
Debt equity conversion/bail-inExtending debt maturity or restructuringEU-wide bank resolutionMutualize, socialize existing and/or new sovereign debt
Source: authors’ compilation.Note: CDS = credit default swaps; EFSF/ESM = European Financial Stability Facility/European Stability Mechanism; LTRO = long-term refinancing operation; OMT = Outright Monetary Transactions; SMP = Securities Market Program.
Source: authors’ compilation.Note: CDS = credit default swaps; EFSF/ESM = European Financial Stability Facility/European Stability Mechanism; LTRO = long-term refinancing operation; OMT = Outright Monetary Transactions; SMP = Securities Market Program.

Conclusions

CCA indicators capture the nonlinearity of changes in bank assets, capital, and bank credit spreads that are derived from forward-looking equity market information in conjunction with balance sheet data, and the expected losses for sovereigns. It can be used for simulation and stress testing or can be used in Network models of interconnectedness, or used in VAR and global VAR models to capture risk transmission between sectors and macro variables within and across countries. The CCA-GVAR model successfully integrates forward-looking banking system, sovereign, and corporate risk indicators in a GVAR with real GDP growth and credit levels in a 16-country framework which is fully endogenous. It is a stable global model which provides meaningful responses in terms of directions and magnitude. CCA framework allows a range of policy options to be modeled, and their risk mitigation impact to be quantified.

Appendix 4.1. Contingent Claims Analysis-Global Vector Autoregression: Expected Loss, GDP, and Credit Indicators for 16 Countries

Figure A4.1Austria: Risk Indicators, GDP Growth, and Credit Growth

Sources: Eurostat; Moody’s CreditEdge; and author’s estimates.

Figure A4.2Belgium: Risk Indicators, GDP Growth, and Credit Growth

Sources: Eurostat; Moody’s CreditEdge; and author’s estimates.

Figure A4.3Denmark: Risk Indicators, GDP Growth, and Credit Growth

Sources: Eurostat; Moody’s CreditEdge; and author’s estimates.

Figure A4.4France: Risk Indicators, GDP Growth, and Credit Growth

Sources: Eurostat; Moody’s CreditEdge; and author’s estimates.

Figure A4.5Germany: Risk Indicators, GDP Growth, and Credit Growth

Sources: Eurostat; Moody’s CreditEdge; and author’s estimates.

Figure A4.6Greece: Risk Indicators, GDP Growth, and Credit Growth

Sources: Eurostat; Moody’s CreditEdge; and author’s estimates.

Figure A4.7Ireland: Risk Indicators, GDP Growth, and Credit Growth

Sources: Eurostat; Moody’s CreditEdge; and author’s estimates.

Figure A4.8Italy: Risk Indicators, GDP Growth, and Credit Growth

Sources: Eurostat; Moody’s CreditEdge; and author’s estimates.

Figure A4.9Netherlands: Risk Indicators, GDP Growth and Credit Growth

Sources: Eurostat; Moody’s CreditEdge; and author’s estimates.

Figure A4.10Norway: Risk Indicators, GDP Growth, and Credit Growth

Sources: Eurostat; Moody’s CreditEdge; and author’s estimates.

Figure A4.11Portugal: Risk Indicators, GDP Growth, and Credit Growth

Sources: Eurostat; Moody’s CreditEdge; and author’s estimates.

Figure A4.12Spain: Risk Indicators, GDP Growth, and Credit Growth

Sources: Eurostat; Moody’s CreditEdge; and author’s estimates.

Figure A4.13Sweden: Risk Indicators, GDP Growth, and Credit Growth

Sources: Eurostat; Moody’s CreditEdge; and author’s estimates.

Figure A4.14Switzerland: Risk Indicators, GDP Growth, and Credit Growth

Sources: Eurostat; Moody’s CreditEdge; and author’s estimates.

Figure A4.15United Kingdom: Risk Indicators, GDP Growth, and Credit Growth

Sources: Eurostat; Moody’s CreditEdge; and author’s estimates.

Figure A4.16United States: Risk Indicators, GDP Growth, and Credit Growth

Sources: Eurostat; Moody’s CreditEdge; and author’s estimates.

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This is joint work with several academics at MIT (Robert Merton and Andrew Lo), University of Massachusetts (Mila Getmansky), and University of Venice (Monica Billio and Loriana Pelizzon), and results have been published in Merton and others, 2013 and in Billio and others, forthcoming.

This is joint work with the European Central Bank (ECB). The details of this model and results can be found in the forthcoming IMF Working Paper (Gray and others, forthcoming).

The expected loss ratio can be shown to also equal to the risk neutral default probability times loss given default.

Models could be used that incorporate not just the volatility of the asset return process but higher moments as well (e.g., jump diffusion, Gram-Charlier, or some other process as described in Backus and others, 2004, and Jobst and Gray, 2013).

If the ESM or another entity outside of a certain country were to explicitly guarantee sovereign debt then it might be possible to measure the effect on the sovereign CDS. The potential ECB Outright Monetary Transactions (OMT) purchases of sovereign debt in the euro area is not a guarantee but rather more like rollover financing and thus sovereigns risk would decrease and the sovereign CDS would be expected to decline.

For more information on this type of analysis, see Billo and others (2012) and Merton and others (2013).

Sovereigns in the periphery include Greece, Ireland, Italy, Portugal, and Spain.

The CCA-GVAR model variables are real GDP, credit to the private sector, sovereign EL, national banking system EL, and corporate sector EL. The sample of data has a monthly frequency and ranges from January 2002 to December 2012 (132 observations). The sample covers 16 countries (13 EU countries plus Norway, Switzerland, and the United States). GDP is interpolated from quarterly to monthly by means of a quadratic match sum conversion method. With five endogenous model variables and 16 countries, the global model has 80 equations in total. All variables are modeled in first (i.e., monthly) differences of logarithmic levels. The first differences of the variables are stationary at conventional levels of significance for all countries.

Since all variables are modeled in first differences of logarithmic levels, the simulated raw model responses, including those for ELs for banks, sovereigns, and the corporate sectors are to be understood as logarithmic percentage point deviations. ELs in are transformed back to absolute EL basis point changes by chaining the relative changes implied by the scenario to end-sample (December 2012) EL ratios.

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