On the origin of systemic risk

Giovanni Covi, Mattia Montagna and Gabriele Torri

Systemic risk in the bank sector is often associated with long periods of economic downturn and large social costs. In a new paper, we develop a microstructural contagion model to disentangle and quantify the different sources of systemic risk for the euro-area banking system. Calibrated to granular euro-area data, we estimate that the probability of a systemic banking crisis was around 3.6% in 2018. Seventy per cent of the risk stems from economic risks, with fire sales and contagion risk accounting for most of the remainder and only a small role for interbank exposures. Our findings suggest that correlations among banks’ losses play a crucial role in the origins of systemic risk.

What are microstructural contagion models?

Microstructural balance sheet based contagion models are very flexible tools to perform stress-test exercises and assess the contribution of features such as network structure, regulatory requirements, and firms’ behaviours to the overall stability of the system and bank-specific results. These frameworks aim to reproduce the complex dynamics of a financial banking crisis by modelling the behaviour of credit institutions reacting to different shocks considering the network of exposures among them and vis-à-vis non-financial corporations. Crucially, this methodology can capture causal relationships among economic and financial distress events, allowing regulators to identify vulnerabilities in the financial system (risk or entity) and avoid idiosyncratic risk becoming a systemic event.

Despite their complexity, microstructural models have the advantage of being extremely flexible. Policymakers can use these frameworks to perform counterfactual exercises to test the effectiveness of different micro and macroprudential policies.

In our paper, we propose a multi-layer microstructural model that accounts for the transmission of shocks stemming from direct exposures towards the real economy and for the amplification effects generated within the banking system via financial contagion channels. To conduct our analysis, we outline a new definition of systemic risk and draw on confidential granular exposure-level data.

A new definition of systemic risk

Our definition of systemic risk refers to the probability that a large number of banks get into distress or default simultaneously or, more formally, the probability that the percentage of banks’ defaults or other credit events in a certain period is higher than a given threshold. We set this threshold as 1.5% of credit institutions within a one-year timeframe. Hence, a key feature of our financial system and of systemic crises is the correlation of distress events in the banking sector. What’s the source of these correlations in the cross-section? This is the question we set out to answer exploiting the microstructure of the euro-area banking system.

Granular big data sets: present and future of stress testing

The methodology is calibrated and tested on a unique data set of granular exposures recently constructed at the European Central Bank’s (ECB’s) Financial Stability Directorate and includes euro-area banks’ bilateral interbank exposures, exposures to non-bank financial corporations, and holdings of marketable securities issued by banks (CI), Non-financial corporations (NFC), Financial Corporates (FC), and governments (GOV). The data set covers the period 2015 Q1 to 2018 Q4 and captures exposures to the value of EUR 23.2 trillion, or roughly 96% of euro-area banks’ total assets.

Results

Our analytical framework for the estimation of systemic risk is built on a Monte Carlo simulation scheme calibrated on real-world data. It can be conceptually divided into two parts: one consisting of the engine for the generation of shocks coming from the real economy (scenarios), and one consisting of the multi-layer contagion model that transmits and amplifies the initial economic shocks. The output is estimates of banks’ losses and distress events, like in a stress-test exercise although we interact multiple risk channels and we provide distributions, not point estimates. This approach allows us to estimate the level of systemic risk as defined in the previous section. Finally, the results can be decomposed into their determinants by performing the Monte Carlo simulations under different specifications of the model. We refer to the paper for more details on the methodology.

Table 1 presents the estimation and decomposition of systemic risk and of the average bank default probability in 2018 Q4. We see that the main drivers of systemic risk in the euro-area banking system are the correlation of shocks stemming from the real economy and market contagion (fire-sales). The former, correlation of shocks, refers to the component of economic risk stemming from the correlations among non-financial corporates’ default probabilities. In the tail of the distribution, that is, when we assess systemic risk, correlations of economic shocks (164 basis points) matter more than the expected loss given default (direct credit risk losses) captured by the baseline indicator of economic risk (31.2 basis points ). This implies that extreme loss events, resulting from the realisation of simultaneous defaults in the economic sector, depends majorly on the correlation structure of default probabilities rather than on the credit losses banks are facing in expectation. Other contagion channels (solvency and liquidity) do not provide relevant contribution in isolation, but they contribute to increase the level of systemic risk due to the interaction with other channels.

Our model also allows to compute endogenously the average default probability of banks in the system and to decompose it according to the same determinants considered for systemic risk. By comparing systemic risk to average default probabilities estimated by the model we see a stark difference: average default probabilities are mostly driven by the magnitude of economic shocks (baseline), with contagion and shock correlations playing little or no part.

Table 1: Decomposition of systemic risk (SR), and of the average bank default probability

SourcesSystemic risk (SR)Average default probability
Economic risk195.29.9
    Baseline31.29.9
    Correlation of shocks164.00.0
Contagion risk166.64.6
    Market contagion83.40.7
    Liquidity contagion0.60.8
    Solvency contagion2.01.3
    Interaction77.01.8
   Total358.214.5

Note: All the figures are reported in basis points and refer to 2018 Q4. The interaction term is approximated by the difference between each contagion channel and economic risk, thereby providing a conservative estimate of amplification effects (source: own calculation).

Chart 1 shows the evolution over time of our indicators and compares them with two market-based measures: the Composite Indicator of Systemic Stress (CISS) (sources: ECB and Holló et al (2012)), as a benchmark for our systemic risk measure (Panel A), and the average spread for senior CDS for European banks (Panel B), that proxies default risk in the banking sector. Concerning systemic risk, our measure seems to move in the same direction as the CISS indicator, sometimes anticipating it by one quarter. Whereas, our indicator for the average bank default probability nicely resembles the average senior CDS spread, at least from 2016 onward.

Chart 1: Probability of a systemic event and average expected probability of a bank default

Panel A: Probability of systemic event

 Panel B: Average probability of bank default

Note: BASE refer to the baseline model with no contagion and independent shocks, CORR to the effect of correlation of economic shocks and overlapping portfolios, FIN to the cumulated effect of solvency, liquidity and market contagion (without accounting for their interaction), INT to the interaction of financial contagion channels (source: own calculation). CISS is the level of the Composite Index of Systemic Stress (source: ECB), and CDS is the average credit risk spread for European banks (source: Thomson Reuters Datastream).

Conclusions and policy implications

From a policy perspective, the model represents a flexible tool for regulators, thanks to the ability to estimate the level of systemic risk endogenously, the microstructural foundations, and the possibility to run counterfactual exercises by changing individual aspects of the system. It is an effective policy laboratory through which regulators can monitor which risks/firms are mostly determining fragilities in the banking/financial system.

The relevant differences in the determinants of systemic risk and the determinants of average default risk (with the former being much more influenced by correlations of economic shocks and contagion), suggest that reducing banks’ default probabilities may not be sufficient to reduce the probability of experiencing a systemic crisis. Should policymakers aim for a reduction of the average bank default probability (less frequent bank default) or of systemic risk (fewer banks defaulting simultaneously)? These two targets differ from one another, and reducing the former does not imply a reduction of the latter. In particular, our results indicate that, to reduce systemic risk, regulators need to consider correlations among banks’ losses.

This work should not be reported as representing the views of the ECB or of the Bank of England. The views expressed are those of the authors and do not necessarily reflect those of the ECB or the Bank of England.


Giovanni Covi works in the Bank’s Stress Test Strategy Division, Mattia Montagna works in the Systemic Risk and Financial Institutions Division at European Central Bank and Gabriele Torri is the Assistant Professor at University of Bergamo.

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One thought on “On the origin of systemic risk

  1. We also highlight how the interaction amongst different contagion channels can significantly increase the level of systemic risk. Systemic risk in the banking sector is usually associated with long periods of economic downturn and very large social costs. In particular, for reducing systemic risk regulators need to target and reduce correlations among banks’ losses. Microstructural Contagion Models.

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