An Integrated Stress Testing Framework Via Markov Switching Simulation
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An Integrated Stress Testing Framework Via Markov Switching Simulation
The capturing of tail events, especially those that incur severe loss at rare chance, is one of the important objectives for modern risk analysis. However past behavior in financial data is not necessarily a correct reflection of the possible scenarios in the future. The economic turmoils in the recent years have called for a more forward looking approach to financial risk management that integrates expert knowledge on plausible future scenarios with classical risk management models calibrated on past behavior. As a complementary risk analysis tool, stress testing is getting more and more attention from both regulators and practioners. Nevertheless, classical risk analysis models, such as VaR models, based on historical data and stress testing are often disconnected. This disconnection can prevent a comprehensive view of the risk profile of a financial institution. This paper proposes a multi-period switching simulation based method for integrated stress testing risk analysis that incorporate plausible events that are not necessarily captured in history or in historical stressed calibration of risk models. An integrated risk model and stress testing framework not only leads to forward-looking tail risk measurement that mitigates the "Black Swan" effect, but also takes stress testing into advanced risk management decision making analysis like scenario based portfolio optimization.
Financial Risk Management
A global banking risk management guide geared toward the practitioner Financial Risk Management presents an in-depth look at banking risk on a global scale, including comprehensive examination of the U.S. Comprehensive Capital Analysis and Review, and the European Banking Authority stress tests. Written by the leaders of global banking risk products and management at SAS, this book provides the most up-to-date information and expert insight into real risk management. The discussion begins with an overview of methods for computing and managing a variety of risk, then moves into a review of the economic foundation of modern risk management and the growing importance of model risk management. Market risk, portfolio credit risk, counterparty credit risk, liquidity risk, profitability analysis, stress testing, and others are dissected and examined, arming you with the strategies you need to construct a robust risk management system. The book takes readers through a journey from basic market risk analysis to major recent advances in all financial risk disciplines seen in the banking industry. The quantitative methodologies are developed with ample business case discussions and examples illustrating how they are used in practice. Chapters devoted to firmwide risk and stress testing cross reference the different methodologies developed for the specific risk areas and explain how they work together at firmwide level. Since risk regulations have driven a lot of the recent practices, the book also relates to the current global regulations in the financial risk areas. Risk management is one of the fastest growing segments of the banking industry, fueled by banks' fundamental intermediary role in the global economy and the industry's profit-driven increase in risk-seeking behavior. This book is the product of the authors' experience in developing and implementing risk analytics in banks around the globe, giving you a comprehensive, quantitative-oriented risk management guide specifically for the practitioner. Compute and manage market, credit, asset, and liability risk Perform macroeconomic stress testing and act on the results Get up to date on regulatory practices and model risk management Examine the structure and construction of financial risk systems Delve into funds transfer pricing, profitability analysis, and more Quantitative capability is increasing with lightning speed, both methodologically and technologically. Risk professionals must keep pace with the changes, and exploit every tool at their disposal. Financial Risk Management is the practitioner's guide to anticipating, mitigating, and preventing risk in the modern banking industry.
Integrating Solvency and Liquidity Stress Tests: The Use of Markov Regime-Switching Models
Author: Mr.Fei Han
language: en
Publisher: International Monetary Fund
Release Date: 2019-11-15
The paper presents a framework to integrate liquidity and solvency stress tests. An empirical study based on European bond trading data finds that asset sales haircuts depend on the total amount of assets sold and general liquidity conditions in the market. To account for variations in market liquidity, the study uses Markov regime-switching models and links haircuts with market volatility and the amount of securities sold by banks. The framework is accompanied by a Matlab program and an Excel-based tool, which allow the calculations to be replicated for any type of traded security and to be used for liquidity and solvency stress testing.