Systemic Risk Forecasting: Development and Challenges

Dear Colleagues,

Since the 2007–2009 global financial crisis, the focus of regulation has shifted from a microprudential to a macroprudential perspective as the interconnectedness of financial institutions has proven to amplify financial distress. Now, a broad set of measures captures the contribution and exposure of financial institutions to systemic risk (e.g., Billio et al., 2012; Adrian and Brunnermeier, 2016; Acharya et al., 2017; Brownlees and Engle, 2017). However, for policymakers, their value depends on the ability to signal distress before it materializes. Systemic risk measures and financial conditions provide insight into potential risks to future economic activity (Allen et al., 2012; Giglio et al., 2016; Adrian et al., 2019), and credit booms are a reliable predictor of banking crises (Schularick and Taylor, 2012). However, alternative measures often disagree (Kleinow et al., 2017), can send misleading signals (Löffler and Raupach, 2018), and machine learning models do not always outperform simple benchmarks (Beutel et al., 2019).

The goal of this Special Issue is to publish original research articles and reviews that advance the forecasting of systemic risk in banks and the broader financial system. In line with the scope of Forecasting, we welcome methodological, empirical, and policy-oriented contributions on the construction, evaluation, and use of forward-looking signals of financial distress, ranging from early warning models and tail-risk forecasts to scenario-based assessments of emerging risks. We particularly encourage studies addressing out-of-sample performance and forecast evaluation, forecast horizons, and the value of forecasts for policy decisions.

This Special Issue will welcome manuscripts that link the following themes:

• Out-of-sample predictive performance of systemic risk measures across crisis episodes;
• Early warning systems for banking and financial crises;
• Machine learning, high-dimensional, and text-based methods for systemic risk prediction;
• Density, quantile, and tail-risk forecasting;
• Network, contagion, and connectedness-based forecasts of system-wide distress;
• Climate-related and ESG risks in forward-looking systemic risk assessment;
• Forecast evaluation for macroprudential policy and stress testing.

We look forward to receiving your original research articles and reviews.

References

• Acharya, V.V., Pedersen, L.H., Philippon, T., Richardson, M., 2017. Measuring Systemic Risk. Review of Financial Studies 30, 2–47.
• Adrian, T., Boyarchenko, N., Giannone, D., 2019. Vulnerable Growth. American Economic Review 109, 1263–1289.
• Adrian, T., Brunnermeier, M.K., 2016. CoVaR. American Economic Review 106, 1705–1741.
• Allen, L., Bali, T.G., Tang, Y., 2012. Does Systemic Risk in the Financial Sector Predict Future Economic Downturns? Review of Financial Studies 25, 3000–3036.
• Beutel, J., List, S., von Schweinitz, G., 2019. Does machine learning help us predict banking crises? Journal of Financial Stability 45, 100693.
• Billio, M., Getmansky, M., Lo, A.W., Pelizzon, L., 2012. Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of Financial Economics 104, 535–559.
• Brownlees, C., Engle, R.F., 2017. SRISK: A Conditional Capital Shortfall Measure of Systemic Risk. Review of Financial Studies 30, 48–79.
• Giglio, S., Kelly, B., Pruitt, S., 2016. Systemic risk and the macroeconomy: An empirical evaluation. Journal of Financial Economics 119, 457–471.
• Kleinow, J., Moreira, F., Strobl, S., Vähämaa, S., 2017. Measuring systemic risk: A comparison of alternative market-based approaches. Finance Research Letters 21, 40–46.
• Löffler, G., Raupach, P., 2018. Pitfalls in the Use of Systemic Risk Measures. Journal of Financial and Quantitative Analysis 53, 269–298.
• Schularick, M., Taylor, A.M., 2012. Credit Booms Gone Bust: Monetary Policy, Leverage Cycles, and Financial Crises, 1870-2008. American Economic Review 102, 1029–1061.

Dr. Nicu Sprincean
Guest Editor

Manuscript Submission Information

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