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This paper defines a set of banking stability measures which take account of distress dependence among the banks in a system, thereby providing a set of tools to analyze stability from complementary perspectives by allowing the measurement of (i) common distress of the banks in a system, (ii) distress between specific banks, and (iii) distress in the system associated with a specific bank. Our approach defines the banking system as a portfolio of banks and infers the system's multivariate density (BSMD) from which the proposed measures are estimated. The BSMD embeds the banks' default inter-dependence structure that captures linear and non-linear distress dependencies among the banks in the system, and its changes at different times of the economic cycle. The BSMD is recovered using the CIMDO-approach, a new approach that in the presence of restricted data, improves density specification without explicitly imposing parametric forms that, under restricted data sets, are difficult to model. Thus, the proposed measures can be constructed from a very limited set of publicly available data and can be provided for a wide range of both developing and developed countries.

Business & Economics --- Economic Theory --- Economic stabilization. --- Banks and banking. --- Agricultural banks --- Banking --- Banking industry --- Commercial banks --- Depository institutions --- Adjustment, Economic --- Business stabilization --- Economic adjustment --- Stabilization, Economic --- Finance --- Financial institutions --- Money --- Economic policy --- Banks and Banking --- Finance: General --- Macroeconomics --- Money and Monetary Policy --- Banks --- Depository Institutions --- Micro Finance Institutions --- Mortgages --- Monetary Policy, Central Banking, and the Supply of Money and Credit: General --- Prices, Business Fluctuations, and Cycles: General (includes Measurement and Data) --- General Financial Markets: Government Policy and Regulation --- Monetary economics --- Economic growth --- Credit default swap --- Business cycles --- Systemic risk --- Banks and banking --- Credit --- Financial risk management --- Mexico

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Banks’ living wills involve both recovery and resolution. Since it may not always be clear when recovery plans or actions should be triggered, there is a role for an objective metric to trigger recovery. We outline how such a metric could be constructed meeting criteria of (i) adequate loss absorption; (ii) distinguishing between weak and sound banks; (iii) little susceptibility to manipulation; (iv) timeliness; (v) scalable from the individual bank to the system. We show how this would have worked in the U.K., during 2007–11. This approach has the added advantage that it could be extended to encompass a whole ladder of sanctions of increasing severity as capital erodes.

Banks and Banking --- Finance: General --- Financial Risk Management --- Industries: Financial Services --- Investments: Stocks --- Banks --- Depository Institutions --- Micro Finance Institutions --- Mortgages --- Financial Institutions and Services: Government Policy and Regulation --- Bankruptcy --- Liquidation --- General Financial Markets: Government Policy and Regulation --- International Financial Markets --- Pension Funds --- Non-bank Financial Institutions --- Financial Instruments --- Institutional Investors --- Finance --- Banking --- Investment & securities --- Bank solvency --- Systemic risk --- Loans --- Asset valuation --- Financial sector policy and analysis --- Financial institutions --- Financial sector stability --- Stocks --- Banks and banking --- Financial risk management --- Asset-liability management --- Financial services industry --- United Kingdom

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Portfolio credit risk measurement is greatly affected by data constraints, especially when focusing on loans given to unlisted firms. Standard methodologies adopt convenient, but not necessarily properly specified parametric distributions or simply ignore the effects of macroeconomic shocks on credit risk. Aiming to improve the measurement of portfolio credit risk, we propose the joint implementation of two new methodologies, namely the conditional probability of default (CoPoD) methodology and the consistent information multivariate density optimizing (CIMDO) methodology. CoPoD incorporates the effects of macroeconomic shocks into credit risk, recovering robust estimators when only short time series of loans exist. CIMDO recovers portfolio multivariate distributions (on which portfolio credit risk measurement relies) with improved specifications, when only partial information about borrowers is available. Implementation is straightforward and can be very useful in stress testing exercises (STEs), as illustrated by the STE carried out within the Danish Financial Sector Assessment Program.

Risk. --- Bank investments. --- Bank loans. --- Bank capital. --- Bank credit --- Bank portfolios --- Banks and banking --- Investments --- Capital --- Loans --- Economics --- Uncertainty --- Probabilities --- Profit --- Risk-return relationships --- Banks and Banking --- Finance: General --- Financial Risk Management --- Industries: Financial Services --- Money and Monetary Policy --- Mathematical Methods --- Econometric and Statistical Methods: Other --- Model Evaluation and Selection --- Optimization Techniques --- Programming Models --- Dynamic Analysis --- Business Fluctuations --- Cycles --- Banks --- Depository Institutions --- Micro Finance Institutions --- Mortgages --- Financing Policy --- Financial Risk and Risk Management --- Capital and Ownership Structure --- Value of Firms --- Goodwill --- International Financial Markets --- Financial Institutions and Services: Government Policy and Regulation --- Monetary Policy, Central Banking, and the Supply of Money and Credit: General --- Finance --- Financial services law & regulation --- Banking --- Monetary economics --- Credit risk --- Asset valuation --- Stress testing --- Financial regulation and supervision --- Financial institutions --- Financial sector policy and analysis --- Asset and liability management --- Credit --- Money --- Financial risk management --- Asset-liability management --- Denmark

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We propose a framework to link empirical models of systemic risk to theoretical network/ general equilibrium models used to understand the channels of transmission of systemic risk. The theoretical model allows for systemic risk due to interbank counterparty risk, common asset exposures/fire sales, and a “Minsky" cycle of optimism. The empirical model uses stock market and CDS spreads data to estimate a multivariate density of equity returns and to compute the expected equity return for each bank, conditional on a bad macro-outcome. Theses “cross-sectional" moments are used to re-calibrate the theoretical model and estimate the importance of the Minsky cycle of optimism in driving systemic risk.

Banks and Banking --- Finance: General --- Industries: Financial Services --- Semiparametric and Nonparametric Methods --- Financial Forecasting and Simulation --- Banks --- Depository Institutions --- Micro Finance Institutions --- Mortgages --- Financing Policy --- Financial Risk and Risk Management --- Capital and Ownership Structure --- Value of Firms --- Goodwill --- General Financial Markets: Government Policy and Regulation --- General Financial Markets: General (includes Measurement and Data) --- Banking --- Finance --- Systemic risk --- Interbank markets --- Consumer loans --- Loans --- Banks and banking --- Financial risk management --- International finance