BoE published a working paper that presents a forward-looking approach to measure systemic solvency risk. The approach uses contingent claims analysis as a theoretical foundation for determining an institution’s default risk based on the uncertainty in its asset value relative to promised debt payments over time. The paper describes the jump diffusion process and how the model parameters are used to calculate the implicit put option values of each bank and describes the data and the empirical results relating to the determination of market-implied expected losses of individual banks. The paper also discusses how these bank-specific expected losses can be used to generate a multivariate density of expected losses with extreme value dependence and explains how the estimation results can inform the design of a market-based capital assessment to complement existing prudential approaches.
This paper applied the Systemic contingent claims analysis framework to model the market-implied systemic solvency risk in the UK banking sector by controlling for common factors affecting the interlinkages between individual risk-adjusted balance sheets of the largest commercial banks (Barclays, HSBC, Lloyds, and RBS). This approach explicitly acknowledges non-linearities in measuring default risk using a generalized extreme value (GEV) distribution setup to quantify simultaneous distress. The underlying option pricing formula for measuring expected losses was augmented with a jump diffusion process of banks’ asset values to mitigate the empirical shortcomings of traditional single-firm structural default models. Accounting for the occurrence of large and interrelated changes of expected losses provides an early indication of systemic risk outside known episodes of system-wide stress.
During times of stress (such as the global financial crisis), joint expected losses become very sensitive to extreme shocks at higher levels of statistical confidence. The cross-validation of the results with the aggregate outcomes of alternative (bivariate) systemic risk measures over the same study period suggests a greater early warning capacity of the approach. The authors also exploited the integrated way of measuring expected losses to generate a market-implied measure of capital adequacy (MCAR). This measure of “shadow capital adequacy” indicated whether estimated expected losses—individually and jointly—were consistent with the default risk implied by the accounting-based capital adequacy ratio reported by sample banks. Hence, this distribution-based perspective of market-implied solvency risk can inform a system-wide capital adequacy assessment that reflects the variability of both assets and liabilities at different levels of statistical confidence.
The results showed that market-based measures of systemic risk can effectively complement prudential reporting in informing a more comprehensive assessment of capital adequacy by considering the impact of changes in market conditions on the perceived risk profile of banks. This has become increasingly relevant due to fundamental changes in the market assessment of banks’ solvency risk following the global financial crisis. Investors are now more uncertain about the value of banks’ net assets and of the underlying asset risks. Low market values may also reflect weak or uncertain profits, or high equity risk premia. The contribution of each factor is not entirely independent and will vary by bank. Going forward, further refinements to the model are possible, including various simulation approaches.
Related Link: Working Paper
Keywords: Europe, UK, Banking, Systemic Risk, Shadow Capital Adequacy, Research, Capital Adequacy, Solvency Risk, BoE
Previous ArticleAPRA Updates Lists of Validation and Derivation Rules for Reporting
BIS published a paper that provides an overview on the use of big data and machine learning in the central bank community.
APRA finalized the reporting standard ARS 115.0 on capital adequacy with respect to the standardized measurement approach to operational risk for authorized deposit-taking institutions in Australia.
ECB published a guide that outlines the principles and methods for calculating the penalties for regulatory breaches of prudential requirements by banks.
MAS and The Association of Banks in Singapore (ABS) jointly issued a paper that sets out good practices for the management of operational and other risks stemming from new work arrangements adopted by financial institutions amid the COVID-19 pandemic.
ACPR announced that a new data collection application, called DLPP (Datalake for Prudential), for collecting banking and insurance prudential data will go into production on April 12, 2021.
BCB announced that the Financial Stability Committee decided to maintain the countercyclical capital buffer (CCyB) for Brazil at 0%, at least until the end of 2021.
EIOPA has launched a European-wide comparative study on non-life underwriting risk in internal models, also kicking-off of the data collection phase.
SRB published an overview of the resolution tools available in the Banking Union and their impact on a bank’s ability to maintain continuity of access to financial market infrastructure services in resolution.
EBA is consulting on the implementing technical standards for Pillar 3 disclosures on environmental, social, and governance (ESG) risks, as set out in requirements under Article 449a of the Capital Requirements Regulation (CRR).
ESAs Issue Advice on KPIs on Sustainability for Nonfinancial Reporting