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Equity Returns in the Banking Sector in the Wake of the Great Recession and the European Sovereign Debt Crisis
This study finds that equity returns in the banking sector in the wake of the Great Recession and the European sovereign debt crisis have been driven mainly by weak growth prospects and heightened sovereign risk and to a lesser extent, by deteriorating funding conditions and investor sentiment. While the equity return performance in the banking sector has been dismal in general, better capitalized and less leveraged banks have outperformed their peers, a finding that supports policymakers’ efforts to strengthen bank capitalization.
Surrogate Data Models: Interpreting Large-scale Machine Learning Crisis Prediction Models
Machine learning models are becoming increasingly important in the prediction of economic crises. The models, however, use datasets comprising a large number of predictors (features) which impairs model interpretability and their ability to provide adequate guidance in the design of crisis prevention and mitigation policies. This paper introduces surrogate data models as dimensionality reduction tools in large-scale crisis prediction models. The appropriateness of this approach is assessed by their application to large-scale crisis prediction models developed at the IMF. The results are consistent with economic intuition and validate the use of surrogates as interpretability tools.
Lasso Regressions and Forecasting Models in Applied Stress Testing
Model selection and forecasting in stress tests can be facilitated using machine learning techniques. These techniques have proved robust in other fields for dealing with the curse of dimensionality, a situation often encountered in applied stress testing. Lasso regressions, in particular, are well suited for building forecasting models when the number of potential covariates is large, and the number of observations is small or roughly equal to the number of covariates. This paper presents a conceptual overview of lasso regressions, explains how they fit in applied stress tests, describes its advantages over other model selection methods, and illustrates their application by constructing forecasting models of sectoral probabilities of default in an advanced emerging market economy.
Do Dynamic Provisions Enhance Bank Solvency and Reduce Credit Procyclicality? a Study of the Chilean Banking System
Dynamic provisions could help to enhance the solvency of individual banks and reduce procyclicality. Accomplishing these objectives depends on country-specific features of the banking system, business practices, and the calibration of the dynamic provisions scheme. In the case of Chile, a simulation analysis suggests Spanish dynamic provisions would improve banks' resilience to adverse shocks but would not reduce procyclicality. To address the latter, other countercyclical measures should be considered.
Bottom-Up Default Analysis of Corporate Solvency Risk
This paper suggests a novel approach to assess corporate sector solvency risk. The approach uses a Bottom-Up Default Analysis that projects probabilities of default of individual firms conditional on macroeconomic conditions and financial risk factors. This allows a direct macro-financial link to assessing corporate performance and facilitates what-if scenarios. When extended with credit portfolio techniques, the approach can also assess the aggregate impact of changes in firm solvency risk on creditor banks’ capital buffers under different macroeconomic scenarios. As an illustration, we apply this approach to the corporate sector of the five largest economies in Latin America.
Monitoring Privately-held Firms' Default Risk in Real Time: A Signal-Knowledge Transfer Learning Model
We develop a mixed-frequency, tree-based, gradient-boosting model designed to assess the default risk of privately held firms in real time. The model uses data from publicly-traded companies to construct a probability of default (PD) function. This function integrates high-frequency, market-based, aggregate distress signals with low-frequency, firm-level financial ratios, and macroeconomic indicators. When provided with private firms' financial ratios, the model, which we name signal-knowledge transfer learning model (SKTL), transfers insights gained from 35 thousand publicly-traded firms to more than 4 million private-held ones and performs well as an ordinal measure of privately-held firms' default risk.
Balance Sheet Network Analysis of Too-Connected-to-Fail Risk in Global and Domestic Banking Systems
The 2008/9 financial crisis highlighted the importance of evaluating vulnerabilities owing to interconnectedness, or Too-Connected-to-Fail risk, among financial institutions for country monitoring, financial surveillance, investment analysis and risk management purposes. This paper illustrates the use of balance sheet-based network analysis to evaluate interconnectedness risk, under extreme adverse scenarios, in banking systems in mature and emerging market countries, and between individual banks in Chile, an advanced emerging market economy.
ABBA: An Agent-Based Model of the Banking System
A thorough analysis of risks in the banking system requires incorporating banks’ inherent heterogeneity and adaptive behavior in response to shocks and changes in business conditions and the regulatory environment. ABBA is an agent-based model for analyzing risks in the banking system in which banks’ business decisions drive the endogenous formation of interbank networks. ABBA allows for a rich menu of banks’ decisions, contingent on banks’ balance sheet and capital position, including dividend payment rules, credit expansion, and dynamic balance sheet adjustment via risk-weight optimization. The platform serves to illustrate the effect of changes on regulatory requirements on solvency, liquidity, and interconnectedness risk. It could also constitute a basic building block for further development of large, bottom-up agent-based macro-financial models.
The Global Financial Crisis and its Impact on the Chilean Banking System
This paper explores how the global turmoil affected the risk of banks operating in Chile, and provides evidence that could help strengthen work on vulnerability indicators and off-site supervision. The analysis is based on the study of default risk codependence, or CoRisk, between Chilean banks and global financial institutions. The results suggest that the impact of the global financial crisis was limited, inducing at most a one-rating downgrade to banks operating in Chile. The paper concludes by assessing government measures aimed at reducing systemic risk in the domestic banking sector and the recommendations to allocate SWF assets to domestic banks.
Regulatory Capital Charges for Too-Connected-to-Fail Institutions
The recent financial crisis has highlighted once more that interconnectedness in the financial system is a major source of systemic risk. I suggest a practical way to levy regulatory capital charges based on the degree of interconnectedness among financial institutions. Namely, the charges are based on the institution’s incremental contribution to systemic risk. The imposition of such capital charges could go a long way towards internalizing the negative externalities associated with too-connected-to-fail institutions and providing managerial incentives to strengthen an institution’s solvency position, and avoid too much homogeneity and excessive reliance on the same counterparties in the financial industry.
