A. Overview

The goal of this paper is to develop a framework for the analysis of interactions between banking sector risk, sovereign risk, corporate sector risk, growth, and credit for a large sample of banks and countries. Contingent Claims Analysis (CCA) serves to construct risk indicators for banks, respectively for banking systems, sovereigns, and corporate sectors, which we combine with real GDP growth and credit growth in a global model, comprising 15 EU countries and the United States.

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. A Global Vector Autoregressive (GVAR) model approach serves to combine sovereign credit risk, banking system credit risk, GDP growth, and credit growth in a way to allow all variables to interact fully endogenously. The goal is to use that framework to help identify policies that mitigate banking system, sovereign credit risk and recession risk—policies that include bank liquidity injections, bank capital increases, purchase of sovereign debt, guarantees of bank senior debt by sovereigns, guarantees of sovereigns by other public bodies, etc.

The first section of the paper will present an outline of Contingent Claims Analysis and how the key risk indicators for banks, banking systems, the corporate sector and sovereigns are estimated. A view into how the risk indicators evolve along with real activity shall help develop a first understanding of how sovereign/bank risks relate to business cycle dynamics. The GVAR model framework will then be presented, which is set up for 16 countries, 16 banking sectors which themselves are comprised of 53 individual banks (41 EU banks plus 12 U.S. banks). The five-variable version of the model includes banking system CCA risk indicators, sovereign risk indicators, corporate sector risk indicators, economic growth and credit to the private sector. The use of the GVAR model approach to combine these indicators can be motivated along many lines: cross-border linkages with regard to real activity (primarily via the trade channel) have been the rationale underlying most of the existing empirical GVAR literature as of yet. Regarding domestic credit, cross-border linkages matter to the extent that swings in credit supply from internationally active banks from abroad would spill over to other countries to which they have exposure. As to the linkages between sovereign and banking system risk, in particular, we will in later sections of the paper elaborate further on the rationale for cross-sector/cross-country linkages.

Figure 1 presents a schematic overview of the model framework, including the CCA components that serve as input to the GVAR and then the various sub-modules that receive as input the simulated scenario responses from the GVAR model.

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CCA-GVAR Model Framework

Citation: IMF Working Papers 2013, 218; 10.5089/9781484322185.001.A001

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We estimate and solve the global model based on a monthly sample covering the period from January 2002 to December 2012 (132 observations). We use the model to conduct scenario simulations, involving multiple shocks to selected sovereigns and banking systems. We consider both positive and negative shock scenarios. The output responses are inputs to banking/sovereign sub-modules which are used to compute aggregate loss estimates and changes in bank capital. We aim to use this framework as an analytical means for the analysis of shocks, spillovers and as well as a tool that helps assess tradeoffs among policy alternatives.

B. Motivation and Relationship to the Literature

Recent financial and sovereign crises have shown how financial sector distress is intimately related to government contingent liabilities and bailouts which affect sovereign credit risk. Financial sector and sovereign risks have important effects on credit growth, lending/borrowing rates, corporate and household finances, consumption, investment and economic growth. There are direct channels of risk transmission between banks, sovereigns, and borrowers within and between countries, as well as indirect channels which affect confidence and risk appetite within and between countries. There are many different ways to analyze these transmission channels, different data inputs and various levels of aggregation of the data. While a detailed literature review is not possible here, it is useful to touch on some key recent papers related to three areas, (i) banking sector and sovereign risk interactions; (ii) how financial risks relate to macroeconomic variables; and, (iii) ways to model macro and financial risk connections in a multi-country setting.

Financial sector risk and bailouts negatively impact sovereign credit risk (see Ejsing and Lemke 2009, and Acharya et al. 2011). Draghi et al. 2003 describe how large unanticipated risks can accumulate in the financial sector and on government balance sheets. Structural (CCA) models of banking and sovereign risk have been developed for individual countries which capture non-linear feedbacks between sovereign and banking sector risk (see Gapen et al. 2005, Gray et al. 2007, Gray and Malone 2012). These models are not tractable for a multi-country analysis as it would be difficult to calibrate such models for a multidimensional analysis across a large number of countries.

There are different approaches on how financial risks relate to macroeconomic variables. First there are asset pricing approaches which relate asset price declines to changes in risk premiums—equity risk, interest rate, and risk appetite (pricing kernel/market price of risk) and macroeconomic variables (see Gabaix 2008). There are econometric models linking financial sector risk indicators to macro variables (Gray and Walsh 2008) and can be incorporated in monetary policy models (Garcia et al. 2011). Economy-wide risk transmission using interlinked structural balance sheets for sectors show how credit risk can be amplified in a crisis and how it affects output (Gray et al. 2002, 2008, and 2010). There are many ways to model macro and financial risk connections in a multi-country setting. Global VAR models are well suited to analysis of macroeconomic interrelationships and can include financial risk indicators (as in Chen et al. 2010, Chudik and Fratzscher 2011, Eickmeier and Ng 2011 and others). Structural financial sector risk indicators and sovereign risk were used in a multi-country setting using a Factor Augmented VAR approach by Gray, Jobst and Malone 2011. Network models can be used in a multi-country analysis of links between individual banks, insurers and sovereigns using CCA risk indicators (e.g., Merton 2013 and Billio et al. forthcoming) but are not tractable or as useful when the interest is in examining macroeconomic impacts.

The analysis in this paper integrates the advantages of Global VAR models to measure the interconnectedness of sector level CCA risk indicators and GDP and credit “flow” variables in a multi-country setting. Using data at the sector level keeps the model size manageable. The advantages of the CCA sector risk indicators are: (i) they are forward-looking measures of risk (unlike backward looking accounting ratios); (ii) they combine together leverage, asset volatility, and risk appetite into a single indicator; and (iii) they can be transformed into a credit spread (at the sector or individual entity level), which are intuitive to understand.

A. Expected Loss Ratios for Banks and Non-Financial Corporations

In order to incorporate the banking system risk indicators in the GVAR model we need risk indicators for the individual banks in a country, which will be used to construct a national banking system risk indicator. In principle there are a variety of CCA calibration techniques that could be used to calculate bank-by-bank CCA parameters and calculate the implicit put option (expected loss value), spreads and expected loss ratios.

For the calibration of implied assets and volatility the following inputs are used: Market capitalization values and volatility, historical or estimated or derived from equity options, default barrier estimates from promised payments on debt, the risk free rate, a time horizon, etc. However, also 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 other process as described in Backus et al., 2004, and Jobst and Gray, 2013). After the calibration of the asset process the expected loss value (i.e. implicit put option value) can be calculated which in turn is used to calculate credit spreads; we will call this spread a “fair value spread,” which is the credit spread from the CCA model derived from equity and balance sheet information.

Strong evidence supports the claim that implicit and explicit government backing for banks depresses bank 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. Several studies have shown that for banks during the crisis in 2008–2009 when the CCA model is used (based on equity market and balance sheet data) the resulting fair value spreads are higher than the observed market CDS spreads in many cases (see Gray et al. 2008, Gapen 2009, Moody’s Analytics 2011, Gray and Jobst 2011, 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, and thus the bank CDS is distorted. Also, it is observed that for banks in countries with very high sovereign spreads, the observed bank CDS is frequently higher than the bank fair value spread.

There is a database from Moody’s CreditEdge that 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 Fair Value CDS spread (FVCDS). This FVCDS is a good proxy for the fair value spread we need, so we can use it to obtain the bank-by-bank expected loss ratio, EL_b, and corporate expected loss ratio, EL_c (described in detail in Appendix I). These expected loss ratios have a five year horizon (T=5), monthly frequency, and are expressed in basis points. The FVCDS and its associated expected loss ratio, EL_b, are not distorted in a major way by the effect of government guarantees (situations where FVCDS > CDS) or from spillovers from high sovereign spreads (situations where CDS > FVCDS) and thus are a “purer” forward-looking measure of bank risk. Box 1 shows the nonlinear relationships between the CCA capital ratio, EDF, FVCDS and the EL for a typical bank.³

Box 1.

Relationships between CCA Capital Ratio, EDF, FVCDS 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 2. This is the typical pattern for a bank which has experienced periods of distress and non-distress. 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 bps), credit spreads are low (around 100 bps). In distress periods, when the CCACR falls from 0.3 to 0.1, the EDF is very high (6 to 7 percent), spreads reach about 700 to 900 bps, and the EL is 0.3 to 0.4 (equal to 3000 to 4000 bps). Once the capital ratio starts moving below 3 percent, the EDF increases over 1 percent, spreads start exceeding a 250 bps “threshold” and EL is higher than 1000 bps there is increasing risk of negative shocks leading to sharply higher spreads and EDFs. The dynamics are nonlinear.

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Relationships between CCA Capital Ratio, EDF, FVCDS and EL for a Typical Bank

Citation: IMF Working Papers 2013, 218; 10.5089/9781484322185.001.A001

Source: Moody’s CreditEdge data and author estimates

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The red square in the center of Figure 2 is the safest zone, investment grade and above. CCACR 3 percent and above, EL of 1000 bps or less, spreads less than 200 bps, and EDF of less than 0.5 percent. The slightly larger safe zone, just below investment grade, in Figure 2 is the zone where EDFs are less than 1.5 percent, spreads are 400 bps or less, EL is less than 2000 bps and CCACR is above 2.5 percent.

The relationship between Moody’s ratings, one-year EDFs, and fair-value spreads is shown in Figure 3. Investment grade is defined as ratings BBB- and higher. Spreads of 400 bps or less corresponds to EDFs of about 1.5–2 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 3. Once the rating becomes sub-investment grade, the spreads and default probabilities increase sharply as ratings decline further.

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Comparison of Ratings, Spreads and EDFs

Citation: IMF Working Papers 2013, 218; 10.5089/9781484322185.001.A001

Source: Moody’s CreditEdge data and author estimates.

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One of the benefits of using the EL in the GVAR is that the shocks to EL can be related to shocks to the CCA capital ratio, EDF and spreads, and the output responses from the model can be transformed from EL to the corresponding change in capital ratios, EDFs, and spreads. 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).

B. Aggregating Expected Loss Ratios for Banking Systems and the Non-Financial Corporate Sector

For the purpose of aggregating individual banks’ and non-financial corporations’ risk indicators that belong to country j we compute 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 EL_bs,j, and the corporate sector expected loss ratio is EL_cs,j in country j.

The monthly banking and corporate sector aggregate EL ratios for each country are then used in the GVAR model. The output responses for the sectors as a whole can be transformed back into the EL for the individual banks and corporates (using the weights that were initially used to construct the sector EL). In particular for the individual banks, the EL responses can be related to changes in capital, EDF, spreads (and the Expected Loss Value, which is the implicit put option described earlier).⁴

C. Sovereign CCA Expected Loss Ratio

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 et al. 2013).⁵ A recent analysis of sovereign CDS (IMF, 2013) shows that the sovereign CDS market is not prone to speculative excesses, nor leading to higher sovereign funding costs and this market does not appear to be more prone to high volatility than other financial markets.⁶ The CDS spread for a sovereign (as well as banks and corporate) is a function of the time horizon and the expected loss ratio. The formula for the sovereign CDS, expressed in basis points, is:

Solving for the expected loss ratio for the sovereign as a function of time horizon and sovereign CDS value would give the following formula.

For sovereigns, the five-year CDS is used since, as with bank and corporate CDS, the five-year CDS is the most liquid. There are numerous channels through which the sovereign and the banks interact. 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 strongly distressed, the value of official support (guarantees) will be eroded leading to knock-on and contagion effects. 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 ELs of sovereigns, ELs of banking systems, and GDP are inter-related in a way subject to costly destabilization processes. If funds for a banking sector recapitalization come from increased sovereign borrowing, this increases sovereign risk (more details on the sovereign CCA framework and risk transmission to banking systems can be found in Appendix I).

A. Local Models

The following outline of the GVAR methodology follows by and large the model framework proposed by Pesaran et al. (2004). We assume that the global model comprises N+1 countries that are indexed by i=0,…,N. A set of country-specific endogenous variables are collected in a k_i × 1 vector y_it which is related to a number of autoregressive lags up to P, and a $k_i^{*}\times 1$ vector of weighted foreign variables $y_{\mathrm{it}}^{*}$ that enters the model time-contemporaneously and with a number of lags up to Q, that is,

where a_i0, a_i1, Φ_iq, and ϕ are coefficient matrices of size k_i × 1, k_i × 1, k_i × k_i, and d_i × 1 respectively. The vector d_t contains global weakly exogenous variables. We shall assume that the idiosyncratic error vector ε_it is i.i.d. and has zero mean and covariance matrix Σ_ii.

B. Weight Matrices

For estimating the local models of the GVAR model, the tradition has been to construct weights based on external data sources (trade, financial asset exposures and other sources). The weights are needed to compute the foreign variable vectors $y_{\mathrm{it}}^{*}$ in equation (9) and to later solve the model (see Section C). For the GVAR model that we aim to develop in this paper, we refrain however from referring to external data sources and instead estimate the weights along with the other GVAR parameters, following a method suggested by Gross (2013). The rationale for estimating the weights in particular for the application at hand is that there is no obvious choice for the weights that would link the sovereigns, the banks, and the corporate sectors. For GDP and credit growth, trade-based weights might be an option; estimated weights and the accompanying error bounds, however, suggested significant deviations from trade-based weights for about 75–80 percent of the weights.⁷ In order to not to induce any biases, we therefore estimate all weights for the five variables in the model separately.⁸ The estimated weighting matrices that we employ are presented in Appendix II.

C. Global Solution of the Model

For solving the global model, we define a country-specific $\left(k_i\times k_i^{*}\right)\times 1$ vector z_it as follows.

The local models in equation (9) can then be reformulated.

where we assume for ease of notation in the following that P=Q and the global exogenous variable vector d_t to be empty. The A_ip coefficient matrices are all of size $k_{i\times }\left(k_i\times k_i^{*}\right)$ and have the following form.

The endogenous variables across countries are stacked in one global vector y_t which is of size k × 1 where $k={\sum }_{i=0}^N{k}_i$. Here, we need to map the local variable vectors z_it to the global endogenous variable vector y_t which is accomplished via $\left(k_i\times k_i^{*}\right)\times k$ link matrices W_i. With z_it = W_iy_t we can rewrite the model once more.

We move from country-specific models to the global model by stacking the former in one global system, that is,

The k × k matrices G have the following format.

Finally, we obtain a reduced form of the global model by pre-multiplying the system with the inverse of G₀.

For shock simulation purposes, standard impulse response techniques could be employed, e.g., of a ‘generalized’ type (see Pesaran and Shin, 1998) in order to account for correlation among shocks right upon arrival of a shock. Generalized impulse responses are often used with GVAR models because a causal ordering that would be needed to design an orthogonalized impulse response profile can hardly be meaningfully set up for such large-scale models that easily involve up to 100 or more equations (the present model consists of 80 equations).

An alternative concept that we employ for generating shock profiles is motivated by the fact that we aim to apply multiple shocks, while not wanting to neglect an instantaneous, correlated shock response on impact to other variables, which for instance a simple non-factorized, multiple shock would imply. The generalized impulse response concept is not useful here because it would allow considering only single shock origins. Instead, we operate with the global model’s residual matrix and proceed in two steps. First, their joint, multivariate distribution is simulated so as to obtain a bootstrap replicate, which is accomplished without assuming any concrete functional form for the copula, that, together with the marginal distributions of the residual series, constitute the joint distribution of the residuals.⁹ The bootstrap is therefore entirely nonparametric in nature.¹⁰ Second, we then condition the bootstrap replicate on an assumption for a number (a multiple) of variables and retain only those simulated bootstrap samples that are conform to these assumptions. Assumptions are formulated as inequalities, for instance by hypothesizing that Spain’s sovereign and banking risk indicators (their expected loss ratios) increase at least by 100 bps or more. The simulated bootstrap paths that let the two shock origins move beyond the self-set thresholds would be retained and a shortfall measure, i.e., an average (or in principle any other moment) of the truncated distribution for all equations’ residuals be computed. To motivate the thresholds for conducting this shortfall estimation conditional on a scenario assumption we take an unconditional quantile of the marginal distribution of the respective shock origins’ residuals at a pre-set probability level (e.g., at 1 percent).¹¹

For deriving error bounds around the simulated dynamic responses from the GVAR, we simulate a large number of pseudo-datasets from the initial model calibration upon which we re-estimate and simulate the impulse responses from the model. From the resulting distribution we compute selected percentiles from their tails. The resampling procedure for generating pseudo-data samples is again nonparametric in nature, i.e., we refrain from imposing a concrete parametric assumption on the joint and the marginal distributions for the model residuals.

A. 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 (see Appendix IV). The model-implied response at T=1 for Greece suggests an approximate 700 basis point increase for the Greek sovereign EL ratio. Responses are pronounced for other stressed European economies such as Portugal and Ireland.

Spillovers to banking system ELs are notable for Ireland, Spain, Greece, Italy and Portugal, with EL responses ranging between 160–370 basis points. The corporate sector EL shock profile at T=1 shows smaller effects, yet suggests a rather similar ranking of countries, with Portugal, Spain, Greece, and Ireland attaining the highest ranks with respect to corporate sector EL deviations. They range between 46–150 basis points.

With respect to GDP and credit, T=1 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 dynamic responses (see Appendix V) and the corresponding maximum cumulative deviations (Appendix VI), we find the same set of stressed European economies attaining the highest ranks.¹⁴ Figure 5 below takes the small subset of countries that appear to be particularly strongly affected and scatters the sovereign against the banks’ fair value spread at the end-sample position (December 2012) and the shock scenario. The underlying deviations correspond to the results presented in Appendix VI.

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Shock Scenario One—Sovereign Versus Banks’ Maximum Cumulative Fair-Value Spread Responses

Citation: IMF Working Papers 2013, 218; 10.5089/9781484322185.001.A001

Source: Author estimates.

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For the Greek sovereign, the maximum cumulative fair-value CDS would move beyond 10,000 basis points; it is not included in Figure 5 for that reason. For Ireland and Portugal, the simulated sovereign fair value CDS responses equal about 250 basis points. The Italian and Spanish banking systems, where shocks were set for the respective sovereigns, rank only sixth and seventh, with cumulative maximum responses yet ranging around 140 basis points. Corporate sector responses appear less pronounced in magnitudes, with the responses for Greece equaling 210 basis points. Maximum deviations for GDP and credit can again be observed for several stressed European economies. Greece’s GDP contracts by -1.9 percent cumulatively. Ireland’s credit to the private sector contracts by -5.1 percent. Overall, the scenario thus implies sizable adverse responses for risk across sovereigns, banks, and the corporate sector. Real activity and credit contract markedly.

B. 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 EL ratios for the two countries of 665 and 275 basis points, respectively.

The shock profiles at T=1, in particular for sovereign and bank ELs, suggest that peripheral countries are strongly affected, while the EL indicators for selected countries such as Sweden, Norway, Austria, France and Germany would fall on impact; for the French sovereign EL for instance by about 70 basis points. For Portugal, the simulated response at T=1 is stronger than for the shock originating banking systems in Spain and Italy, both on the sovereign (+370 basis points) and the banking system side (+690 basis points). See Figure 6.

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Shock Scenario Two—Sovereign Versus Banks’ Maximum Cumulative Fair-Value Spread Responses

Citation: IMF Working Papers 2013, 218; 10.5089/9781484322185.001.A001

Source: Author estimates.

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With regard to the corporate sector ELs, vulnerable appear Italy, where the shock originated, and further Portugal, France and Greece, with shocks ranging between 90 and 175 basis points. Most adverse cumulative deviations are again summarized for a smaller subset of strongly affected countries.

In comparison to the sovereign shock scenario (see previous subsection and in particular Figure 5), it can be seen that responses are now more pronounced along the bank EL dimension, and relatively less along the sovereign dimension. Cumulative deviations away from the start point fair value CDS for the sovereign shown in Figure 6 range between 30 basis points for Belgium and 110 basis points for Portugal. Deviations for the banking systems range between 160 basis points for Belgium and 290 basis points for Portugal. Corporate sector responses approach 90 basis points for Portugal. The scenario, furthermore, implies a cumulative contraction of GDPs up to -0.5 percent for Italy, and let credit to the private sector contract by close to -2.5 percent for Spain.

C. 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), let the ELs for Italy and Spain fall by about 290–352 basis points on impact of the scenario (see Appendix IV). The Greek sovereign EL response at T=1 would be relatively pronounced in absolute terms, with the EL ratio falling by about 700 basis points. With respect to the banking systems, Belgium reacts most strongly at T=1 with its EL ratio decreasing by almost 1,000 basis points. On the corporate sector side, responses are sizable, too, ranging between -35 basis points for Portugal and -350 basis points for Spain.

Turning again to cumulative responses to assess how much deviation the scenario implies for along the horizon (Appendix VI), Figure 7 scatters sovereign and bank fair value spread deviations for a small subset of countries.

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Shock Scenario Three—Sovereign Versus Banks’ Minimum Cumulative Fair-Value Spread Responses

Citation: IMF Working Papers 2013, 218; 10.5089/9781484322185.001.A001

Source: Author estimates.

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For the countries contained in Figure 7, all sovereign and banking system fair value spreads would move back into the ‘safe zone’ comprising the less than 400 basis point area. 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 implies relatively strong positive cumulative reactions for all countries. The cumulative most positive impact on Greece’s GDP for instance has been estimated to equal 5.5 percent. Cumulative credit growth would even surpass 8 percent for Ireland.

D. 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 (see Appendix IV). Italy’s end-sample EL ratio of 2,730 basis points would fall by 65 percent. The scenario therefore envisages a sizable positive impulse to the two banking systems.

In parallel to the T=1 impulses of the shock origins themselves, sovereigns and banks in Greece, Belgium, and Portugal appear to benefit the most. Sovereign EL fall by -230 basis points for Portugal and -800 basis points for Greece. Banking system ELs decrease by close to 800 basis points for Belgium and -2,300 for Greece. The set of countries whose corporate sectors benefit the most include again Italy, Portugal, Spain and Greece, with EL responses ranging between -135 and -180 basis points. Real GDP responses at T=1 are somewhat less pronounced 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 8 presents the most positive cumulative responses along the simulation horizon again for the sub-sample of countries for which responses are most pronounced.

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Shock Scenario Four—Sovereign Versus Banks’ Minimum Cumulative Fair-Value Spread Responses

Citation: IMF Working Papers 2013, 218; 10.5089/9781484322185.001.A001

Source: Author estimates.

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Despite the banking systems (in Italy and Spain) having been the shock origins in Scenario Four, it is Portugal’s and Belgium’s sovereigns that move back into the safe zone that is delineated by the 400 basis point threshold for the fair value spreads; 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 (5 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.