Backtesting Value At Risk And Expected Shortfall In The Presence Of Estimation Error
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Backtesting Value At Risk And Expected Shortfall In The Presence Of Estimation Error
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Author : Sander Barendse
language : en
Publisher:
Release Date : 2019
Backtesting Value At Risk And Expected Shortfall In The Presence Of Estimation Error written by Sander Barendse and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
We investigate the effect of estimation error on backtests of (multi-period) expected shortfall (ES) forecasts. These backtests are based on first order conditions of a recently introduced family of jointly consistent loss functions for Value-at-Risk (VaR) and ES. We provide explicit expressions for the additional terms in the asymptotic covariance matrix that result from estimation error, and propose robust tests that account for it. Monte Carlo experiments show that the tests that ignore these terms suffer from size distortions, which are more pronounced for higher ratios of outof-sample to in-sample observations. Robust versions of the backtests perform well, although this also depends on the choice of conditioning variables. In an application to VaR and ES forecasts for daily FTSE 100 index returns as generated by AR-GARCH, AR-GJR-GARCH, and AR-HEAVY models, we find that estimation error substantially impacts the outcome of the backtests.
Backtesting Value At Risk And Expected Shortfall
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Author : Simona Roccioletti
language : en
Publisher: Springer
Release Date : 2015-12-04
Backtesting Value At Risk And Expected Shortfall written by Simona Roccioletti and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-04 with Business & Economics categories.
In this book Simona Roccioletti reviews several valuable studies about risk measures and their properties; in particular she studies the new (and heavily discussed) property of "Elicitability" of a risk measure. More important, she investigates the issue related to the backtesting of Expected Shortfall. The main contribution of the work is the application of "Test 1" and "Test 2" developed by Acerbi and Szekely (2014) on different models and for five global market indexes.
On The Role Of The Estimation Error In Prediction Of Expected Shortfall
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Author : Carl Lönnbark
language : en
Publisher:
Release Date : 2012
On The Role Of The Estimation Error In Prediction Of Expected Shortfall written by Carl Lönnbark and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
Sample Size Skewness And Leverage Effects In Value At Risk And Expected Shortfall Estimation
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Author : Laura García Jorcano
language : en
Publisher:
Release Date : 2017
Sample Size Skewness And Leverage Effects In Value At Risk And Expected Shortfall Estimation written by Laura García Jorcano and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
The estimation of risk measures is an area of highest importance in the financial industry. Risk measures play a major role in the risk-management and in the computation of regulatory capital. The Basel III document [13] has suggested to shift from Value-at-Risk (VaR) into Expected Shortfall (ES) as a risk measure and to consider stressed scenarios at a new con dence level of 97:5%. This change is motivated by the appealing theoretical properties of ES as a measure of risk and the poor properties of VaR. In particular, VaR fails to control for tail risk". In this transition, the major challenge faced by nancial institutions is the unavailability of simple tools for evaluation of ES forecasts (i.e. backtesting ES) The objective of this thesis is to compare the performance of a variety of models for VaR and ES estimation for a collection of assets of di erent nature: stock indexes, individual stocks, bonds, exchange rates, and commodities. Throughout the thesis, by a VaR or an ES model" is meant a given speci cation for conditional volatility, combined with an assumption on the probability distribution of return innovations...
Estimation Risk In Financial Risk Management
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Author : Daniel Giamouridis
language : en
Publisher:
Release Date : 2008
Estimation Risk In Financial Risk Management written by Daniel Giamouridis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
Christoffersen and Goncalves (2005) study the effect of parameter estimation error in computing Value at Risk and Expected Shortfall through commonly used methods including the Cornish-Fisher/Gram-Charlier approximations approach. We provide a correction to the expression used for the computation of the Expected Shortfall under the Cornish-Fisher/Gram-Charlier approximations and illustrate the effect of the error found in assessing the accuracy of Expected Shortfall point forecasts.
Estimation Error Of Expected Shortfall
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Author : Imre Kondor
language : en
Publisher:
Release Date : 2014
Estimation Error Of Expected Shortfall written by Imre Kondor and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
The problem of estimation error of Expected Shortfall is analyzed, with a view of its introduction as a global regulatory risk measure.
Robust Forecasting And Backtesting Of Value At Risk Var And Expected Shortfall Es Risk Measures
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Author : Christos Argyropoulos
language : en
Publisher:
Release Date : 2017
Robust Forecasting And Backtesting Of Value At Risk Var And Expected Shortfall Es Risk Measures written by Christos Argyropoulos and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Comparative Analyses Of Expected Shortfall And Var
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Author : Yasuhiro Yamai
language : en
Publisher:
Release Date : 2001
Comparative Analyses Of Expected Shortfall And Var written by Yasuhiro Yamai and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Financial futures categories.
Expected shortfall is compared with Value-at-Risk (VaR) in three aspects: estimation errors, decomposition into risk factors, and optimization. Advantages and disadvantages of expected shortfall over VaR are shown, and that expected shortfall is easily decomposed (needing a larger size of sample than VaR for the same level of accuracy) and optimized, while VaR is not.
Value At Risk And Expected Shortfall When There Is Long Range Dependence
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Author : Wolfgang K. Härdle
language : en
Publisher:
Release Date : 2017
Value At Risk And Expected Shortfall When There Is Long Range Dependence written by Wolfgang K. Härdle and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Empirical studies have shown that a large number of financial asset returns exhibit fat tails and are often characterized by volatility clustering and asymmetry. Also revealed as a stylized fact is Long memory or long range dependence in market volatility, with significant impact on pricing and forecasting of market volatility. The implication is that models that accomodate long memory hold the promise of improved long-run volatility forecast as well as accurate pricing of long-term contracts. On the other hand, recent focus is on whether long memory can affect the measurement of market risk in the context of Value-at-Risk (V aR). In this paper, we evaluate the Value-at-Risk (VaR) and Expected Shortfall (ESF) in financial markets under such conditions. We examine one equity portfolio, the British FTSE100 and three stocks of the German DAX index portfolio (Bayer, Siemens and Volkswagen). Classical VaR estimation methodology such as exponential moving average (EMA) as well as extension to cases where long memory is an inherent characteristics of the system are investigated. In particular, we estimate two long memory models, the Fractional Integrated Asymmetric Power-ARCH and the Hyperbolic-GARCH with different error distribution assumptions. Our results show that models that account for asymmetries in the volatility specifications as well as fractional integrated parametrization of the volatility process, perform better in predicting the one-step as well as five-step ahead VaR and ESF for short and long positions than short memory models. This suggests that for proper risk valuation of options, the degree of persistence should be investigated and appropriate models that incorporate the existence of such characteristic be taken into account.
Financial Risk Forecasting
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Author : Jon Danielsson
language : en
Publisher: John Wiley & Sons
Release Date : 2011-04-20
Financial Risk Forecasting written by Jon Danielsson and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-04-20 with Business & Economics categories.
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.