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The financial systems in most developed countries today build up a large amount of model risk on a daily basis. However, this is not particularly visible as the financial risk management agenda is still dominated by the subprime-liquidity crisis, the sovereign crises, and other major political events. Losses caused by model risk are hard to identify and even when they are internally identified, as such, they are most likely to be classified as normal losses due to market evolution.Model Risk in Financial Markets: From Financial Engineering to Risk Management seeks to change the current perspective on model innovation, implementation and validation. This book presents a wide perspective on model risk related to financial markets, running the gamut from financial engineering to risk management, from financial mathematics to financial statistics. It combines theory and practice, both the classical and modern concepts being introduced for financial modelling. Quantitative finance is a relatively new area of research and much has been written on various directions of research and industry applications. In this book the reader gradually learns to develop a critical view on the fundamental theories and new models being proposed.
A risk measurement and management framework that takes model risk seriously Most financial risk models assume the future will look like the past, but effective risk management depends on identifying fundamental changes in the marketplace as they occur. Bayesian Risk Management details a more flexible approach to risk management, and provides tools to measure financial risk in a dynamic market environment. This book opens discussion about uncertainty in model parameters, model specifications, and model-driven forecasts in a way that standard statistical risk measurement does not. And unlike current machine learning-based methods, the framework presented here allows you to measure risk in a fully-Bayesian setting without losing the structure afforded by parametric risk and asset-pricing models. Recognize the assumptions embodied in classical statistics Quantify model risk along multiple dimensions without backtesting Model time series without assuming stationarity Estimate state-space time series models online with simulation methods Uncover uncertainty in workhorse risk and asset-pricing models Embed Bayesian thinking about risk within a complex organization Ignoring uncertainty in risk modeling creates an illusion of mastery and fosters erroneous decision-making. Firms who ignore the many dimensions of model risk measure too little risk, and end up taking on too much. Bayesian Risk Management provides a roadmap to better risk management through more circumspect measurement, with comprehensive treatment of model uncertainty.
The financial systems in most developed countries today build up a large amount of model risk on a daily basis. However, this is not particularly visible as the financial risk management agenda is still dominated by the subprime-liquidity crisis, the sovereign crises, and other major political events. Losses caused by model risk are hard to identify and even when they are internally identified, as such, they are most likely to be classified as normal losses due to market evolution. Model Risk in Financial Markets: From Financial Engineering to Risk Management seeks to change the current perspective on model innovation, implementation and validation. This book presents a wide perspective on model risk related to financial markets, running the gamut from financial engineering to risk management, from financial mathematics to financial statistics. It combines theory and practice, both the classical and modern concepts being introduced for financial modelling. Quantitative finance is a relatively new area of research and much has been written on various directions of research and industry applications. In this book the reader gradually learns to develop a critical view on the fundamental theories and new models being proposed.
A guide to the validation and risk management of quantitative models used for pricing and hedging Whereas the majority of quantitative finance books focus on mathematics and risk management books focus on regulatory aspects, this book addresses the elements missed by this literature--the risks of the models themselves. This book starts from regulatory issues, but translates them into practical suggestions to reduce the likelihood of model losses, basing model risk and validation on market experience and on a wide range of real-world examples, with a high level of detail and precise operative indications.
Financial Risk Forecasting is a complete introduction to practical quantitative risk management, with a focus on market risk. Derived from the authors teaching notes and years spent training practitioners in risk management techniques, it brings together the three key disciplines of finance, statistics and modeling (programming), to provide a thorough grounding in risk management techniques. Written by renowned risk expert Jon Danielsson, the book begins with an introduction to financial markets and market prices, volatility clusters, fat tails and nonlinear dependence. It then goes on to present volatility forecasting with both univatiate and multivatiate methods, discussing the various methods used by industry, with a special focus on the GARCH family of models. The evaluation of the quality of forecasts is discussed in detail. Next, the main concepts in risk and models to forecast risk are discussed, especially volatility, value-at-risk and expected shortfall. The focus is both on risk in basic assets such as stocks and foreign exchange, but also calculations of risk in bonds and options, with analytical methods such as delta-normal VaR and duration-normal VaR and Monte Carlo simulation. The book then moves on to the evaluation of risk models with methods like backtesting, followed by a discussion on stress testing. The book concludes by focussing on the forecasting of risk in very large and uncommon events with extreme value theory and considering the underlying assumptions behind almost every risk model in practical use – that risk is exogenous – and what happens when those assumptions are violated. Every method presented brings together theoretical discussion and derivation of key equations and a discussion of issues in practical implementation. Each method is implemented in both MATLAB and R, two of the most commonly used mathematical programming languages for risk forecasting with which the reader can implement the models illustrated in the book. The book includes four appendices. The first introduces basic concepts in statistics and financial time series referred to throughout the book. The second and third introduce R and MATLAB, providing a discussion of the basic implementation of the software packages. And the final looks at the concept of maximum likelihood, especially issues in implementation and testing. The book is accompanied by a website - www.financialriskforecasting.com – which features downloadable code as used in the book.
The current financial crisis has revealed serious flaws in models, measures and, potentially, theories, that failed to provide forward-looking expectations for upcoming losses originated from market risks. The Proceedings of the Perm Winter School 2011 propose insights on many key issues and advances in financial markets modeling and risk measurement aiming to bridge the gap. The key addressed topics include: hierarchical and ultrametric models of financial crashes, dynamic hedging, arbitrage free modeling the term structure of interest rates, agent based modeling of order flow, asset pricing in a fractional market, hedge funds performance and many more.
A risk measurement and management framework that takes model risk seriously Most financial risk models assume the future will look like the past, but effective risk management depends on identifying fundamental changes in the marketplace as they occur. Bayesian Risk Management details a more flexible approach to risk management, and provides tools to measure financial risk in a dynamic market environment. This book opens discussion about uncertainty in model parameters, model specifications, and model-driven forecasts in a way that standard statistical risk measurement does not. And unlike current machine learning-based methods, the framework presented here allows you to measure risk in a fully-Bayesian setting without losing the structure afforded by parametric risk and asset-pricing models. Recognize the assumptions embodied in classical statistics Quantify model risk along multiple dimensions without backtesting Model time series without assuming stationarity Estimate state-space time series models online with simulation methods Uncover uncertainty in workhorse risk and asset-pricing models Embed Bayesian thinking about risk within a complex organization Ignoring uncertainty in risk modeling creates an illusion of mastery and fosters erroneous decision-making. Firms who ignore the many dimensions of model risk measure too little risk, and end up taking on too much. Bayesian Risk Management provides a roadmap to better risk management through more circumspect measurement, with comprehensive treatment of model uncertainty.
Quantitative methods have revolutionized the area of trading, regulation, risk management, portfolio construction, asset pricing and treasury activities, and governmental activity such as central banking to name but some of the applications. Downside-risk, as a quantitative method, is an accurate measurement of investment risk, because it captures the risk of not accomplishing the investor's goal. 'Downside Risk in Financial Markets' demonstrates how downside-risk can produce better results in performance measurement and asset allocation than variance modelling. Theory, as well as the practical issues involved in its implementation, is covered and the arguments put forward emphatically show the superiority of downside risk models to variance models in terms of risk measurement and decision making. Variance considers all uncertainty to be risky. Downside-risk only considers returns below that needed to accomplish the investor's goal, to be risky. Risk is one of the biggest issues facing the financial markets today. 'Downside Risk in Financial Markets' outlines the major issues for Investment Managers and focuses on "downside-risk" as a key activity in managing risk in investment/portfolio management. Managing risk is now THE paramount topic within the financial sector and recurring losses through the 1990s has shocked financial institutions into placing much greater emphasis on risk management and control. Free Software Enclosed To help you implement the knowledge you will gain from reading this book, a CD is enclosed that contains free software programs that were previously only available to institutional investors under special licensing agreement to The pension Research Institute. This is our contribution to the advancement of professionalism in portfolio management. The Forsey-Sortino model is an executable program that: 1. Runs on any PC without the need of any additional software. 2. Uses the bootstrap procedure developed by Dr. Bradley Effron at Stanford University to uncover what could have happened, instead of relying only on what did happen in the past. This is the best procedure we know of for describing the nature of uncertainty in financial markets. 3. Fits a three parameter lognormal distribution to the bootstrapped data to allow downside risk to be calculated from a continuous distribution. This improves the efficacy of the downside risk estimates. 4. Calculates upside potential and downside risk from monthly returns on any portfolio manager. 5. Calculates upside potential and downside risk from any user defined distribution. Forsey-Sortino Source Code: 1. The source code, written in Visual Basic 5.0, is provided for institutional investors who want to add these calculations to their existing financial services. 2. No royalties are required for this source code, providing institutions inform clients of the source of these calculations. A growing number of services are now calculating downside risk in a manner that we are not comfortable with. Therefore, we want investors to know when downside risk and upside potential are calculated in accordance with the methodology described in this book. Riddles Spreadsheet: 1. Neil Riddles, former Senior Vice President and Director of Performance Analysis at Templeton Global Advisors, now COO at Hansberger Global Advisors Inc., offers a free spreadsheet in excel format. 2. The spreadsheet calculates downside risk and upside potential relative to the returns on an index Brings together a range of relevant material, not currently available in a single volume source. Provides practical information on how financial organisations can use downside risk techniques and technological developments to effectively manage risk in their portfolio management. Provides a rigorous theoretical underpinning for the use of downside risk techniques. This is important for the long-run acceptance of the methodology, since such arguments justify consultant's recommendations to pension funds and other plan sponsors.
The book aims to provide solutions on how to include model risk into existing risk measurement frameworks. It also aims to provide solutions on how to build models of higher accuracy and thus lower model risk.
Author : Mark Carey Publisher : University of Chicago Press Page : 669 pages File Size : 27,51 MB Release : 2007-11-01 Category : Business & Economics ISBN : 0226092984
Until about twenty years ago, the consensus view on the cause of financial-system distress was fairly simple: a run on one bank could easily turn to a panic involving runs on all banks, destroying some and disrupting the financial system. Since then, however, a series of events—such as emerging-market debt crises, bond-market meltdowns, and the Long-Term Capital Management episode—has forced a rethinking of the risks facing financial institutions and the tools available to measure and manage these risks. The Risks of Financial Institutions examines the various risks affecting financial institutions and explores a variety of methods to help institutions and regulators more accurately measure and forecast risk. The contributors--from academic institutions, regulatory organizations, and banking--bring a wide range of perspectives and experience to the issue. The result is a volume that points a way forward to greater financial stability and better risk management of financial institutions.