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| Author | : Simona Roccioletti |
| Publisher | : Springer Gabler |
| Total Pages | : 0 |
| Release | : 2015-12-11 |
| Genre | : Business & Economics |
| ISBN | : 9783658119072 |
Download Backtesting Value at Risk and Expected Shortfall Book in PDF, ePub and Kindle
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.
| Author | : Timotheos Angelidis |
| Publisher | : |
| Total Pages | : 28 |
| Release | : 2018 |
| Genre | : |
| ISBN | : |
Download Backtesting VaR Models Book in PDF, ePub and Kindle
Academics and practitioners have extensively studied Value-at-Risk (VaR) to propose a unique risk management technique that generates accurate VaR estimations for long and short trading positions. However, they have not succeeded yet as the developed testing frameworks have not been widely accepted. A two-stage backtesting procedure is proposed in order a model that not only forecasts VaR but also predicts the loss beyond VaR to be selected. Numerous conditional volatility models that capture the main characteristics of asset returns (asymmetric and leptokurtic unconditional distribution of returns, power transformation and fractional integration of the conditional variance) under four distributional assumptions (normal, GED, Student-t, and skewed Student-t) have been estimated to find the best model for three financial markets (US stock, gold and dollar-pound exchange rate markets), long and short trading positions, and two confidence levels. By following this procedure, the risk manager can significantly reduce the number of competing models.
| Author | : Timotheos Angelidis |
| Publisher | : |
| Total Pages | : |
| Release | : 2018 |
| Genre | : |
| ISBN | : |
Download Backtesting VaR Models Book in PDF, ePub and Kindle
Academics and practitioners have extensively studied Value-at-Risk (VaR) to propose a unique risk management technique that generates accurate VaR estimations for long and short trading positions and for all types of financial assets. However, they have not succeeded yet as the testing frameworks of the proposals developed, have not been widely accepted. A two-stage backtesting procedure is proposed to select a model that not only forecasts VaR but also predicts the losses beyond VaR. Numerous conditional volatility models that capture the main characteristics of asset returns (asymmetric and leptokurtic unconditional distribution of returns, power transformation and fractional integration of the conditional variance) under four distributional assumptions (normal, GED, Student-t, and skewed Student-t) have been estimated to find the best model for three financial markets, long and short trading positions, and two confidence levels. By following this procedure, the risk manager can significantly reduce the number of competing models that accurately predict both the VaR and the Expected Shortfall (ES) measures.
| Author | : Christian Steinlechner |
| Publisher | : |
| Total Pages | : 92 |
| Release | : 2013-08 |
| Genre | : |
| ISBN | : 9783656319009 |
Download A Comparison Between Advanced Value at Risk Models and Their Backtesting in Different Portfolios Book in PDF, ePub and Kindle
Master's Thesis from the year 2012 in the subject Business economics - Miscellaneous, grade: 1, Fachhochschule des bfi Wien GmbH, course: Riskmanagement, language: English, comment: gewann den 2. Platz beim CFA Austria Prize, abstract: This thesis analyses three VaR models in detail. To begin with, there is a short description of the theoretical background of the models. Next, four different backtests are performed on two different portfolios for each of the three models. The source code used for the implementation is available in the appendix. The main part will deal with the interpretation of the backtesting results. Each model will be compared with the same backtests and dimensionality, which allows the comparison of models with each other. The main outcome of this backtest is the knowledge as to how a model should be calibrated and how robust a model is. In a validation procedure, the author selects that calibration which yields the best results for each model.
| Author | : Juan Carlos Escanciano |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2009 |
| Genre | : |
| ISBN | : |
Download Robust Backtesting Tests for Value-at-Risk Models Book in PDF, ePub and Kindle
Backtesting methods are statistical tests designed to uncover excessive risk-taking from financial institutions. We show in this paper that these methods are subject to the presence of model risk produced by a wrong specification of the conditional VaR model, and derive its effect on the asymptotic distribution of the relevant out-of-sample tests. We also show that in the absence of estimation risk, the unconditional backtest is affected by model misspecification but the independence test is not. Our solution for the general case consists on proposing robust subsampling techniques to approximate the true sampling distribution of these tests. We carry out a Monte Carlo study to see the importance of these effects in finite samples for location-scale models that are wrongly specified but correct on average. An application to Dow-Jones Index shows the impact of correcting for model risk on backtesting procedures for different dynamic VaR models measuring risk exposure.
| Author | : Tze Leung Lai |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 363 |
| Release | : 2008-07-25 |
| Genre | : Business & Economics |
| ISBN | : 0387778268 |
Download Statistical Models and Methods for Financial Markets Book in PDF, ePub and Kindle
The idea of writing this bookarosein 2000when the ?rst author wasassigned to teach the required course STATS 240 (Statistical Methods in Finance) in the new M. S. program in ?nancial mathematics at Stanford, which is an interdisciplinary program that aims to provide a master’s-level education in applied mathematics, statistics, computing, ?nance, and economics. Students in the programhad di?erent backgroundsin statistics. Some had only taken a basic course in statistical inference, while others had taken a broad spectrum of M. S. - and Ph. D. -level statistics courses. On the other hand, all of them had already taken required core courses in investment theory and derivative pricing, and STATS 240 was supposed to link the theory and pricing formulas to real-world data and pricing or investment strategies. Besides students in theprogram,thecoursealso attractedmanystudentsfromother departments in the university, further increasing the heterogeneity of students, as many of them had a strong background in mathematical and statistical modeling from the mathematical, physical, and engineering sciences but no previous experience in ?nance. To address the diversity in background but common strong interest in the subject and in a potential career as a “quant” in the ?nancialindustry,thecoursematerialwascarefullychosennotonlytopresent basic statistical methods of importance to quantitative ?nance but also to summarize domain knowledge in ?nance and show how it can be combined with statistical modeling in ?nancial analysis and decision making. The course material evolved over the years, especially after the second author helped as the head TA during the years 2004 and 2005.
| Author | : Siridej Putsorn |
| Publisher | : |
| Total Pages | : 154 |
| Release | : 2015 |
| Genre | : Financial risk |
| ISBN | : |
Download Backtest Criteria for the Quantile Correction Under Model Risk Book in PDF, ePub and Kindle
The fact that financial risks cannot be exactly determined but have to be estimated make Value-at-Risk (VaR) models less reliable. Thus far, VaR-model risk have gained increasing concerns and have been addressed in two general ways. The first way is to evaluate risk models using statistical tests, called backtests. In particular, backtests employ a comparison of VaR series and realized returns in the specified period to examine whether risk estimates are appropriate or not. The second way is adjusting VaR for model risk, which one of the recently proposed frameworks is the quantile correction method via the outcome of backtesting. Set of backtest methods are chosen for being adjustment criteria by considering three desirable properties of VaR models, namely, unconditional coverage, independence, and magnitude of violations (losses that exceed VaR). This thesis extend the general quantile correction framework by applying various backtest methods focusing on their statistical power of backtests shown by authors. Five standard data generating models (DGMs) are used to compute VaR of Stock Exchange of Thailand (SET) index daily returns. The results from ex post validation show that model-risk-adjusted series provide better results than original VaR in many cases. With regards to criteria sets, higher-statistical-power backtest criteria sets outperform their counterparts when static VaR models are used.
| Author | : Juan Carlos Escanciano |
| Publisher | : |
| Total Pages | : 39 |
| Release | : 2008 |
| Genre | : |
| ISBN | : |
Download Backtesting Parametric Value-at-Risk With Estimation Risk Book in PDF, ePub and Kindle
One of the implications of the creation of Basel Committee on Banking Supervision wasthe implementation of Value-at-Risk (VaR) as the standard tool for measuring market risk.Since then, the capital requirements of commercial banks with trading activities are basedon VaR estimates. Therefore, appropriately constructed tests for assessing the out-of-sampleforecast accuracy of the VaR model (backtesting procedures) have become of crucial practicalimportance. In this paper we show that the use of the standard unconditional and independencebacktesting procedures to assess VaR models in out-of-sample composite environmentscan be misleading. These tests do not consider the impact of estimation risk and thereforemay use wrong critical values to assess market risk. The purpose of this paper is to quantifysuch estimation risk in a very general class of dynamic parametric VaR models and tocorrect standard backtesting procedures to provide valid inference in out-of-sample analyses.A Monte Carlo study illustrates our theoretical findings in finite-samples and shows that ourcorrected unconditional test can provide more accurately sized and more powerful tests thanthe uncorrected one. Finally, an application to Samp;P500 Index shows the importance of thiscorrection and its impact on capital requirements as imposed by Basel Accord.
| Author | : Ali Shirazi |
| Publisher | : |
| Total Pages | : 15 |
| Release | : 2014 |
| Genre | : |
| ISBN | : |
Download Value at Risk (VaR) Backtesting Techniques and P-Value Risk Decomposition Analysis Book in PDF, ePub and Kindle
This paper presents a methodology to analyze the Value at Risk (VaR) backtesting probability values to detect the soundness of the VaR model, the integrity of the VaR input and output as well as providing information about the type of the risk that a subportfolio is exposed to in every trading day. The paper presets statistical methods to back test the number of VaR breaches when there is no or some autocorrelation in the P&L daily values. It illustrates a method to evaluate the model by backtesting all quintiles. It also presents a methodology to test the integrity of the P&L and consequently the p-values using the run test. Finally a model is presented to decompose the subportfolios' P&L risk into systematic and idiosyncratic risk using a Gaussian Copula model. The risk decomposition can be used to detect any unusual subportfolio exposures to specific risk or detect the unusual rise in the systematic risk across different subportfolios.
| Author | : Carol Alexander |
| Publisher | : John Wiley & Sons |
| Total Pages | : 503 |
| Release | : 2009-02-09 |
| Genre | : Business & Economics |
| ISBN | : 0470997885 |
Download Market Risk Analysis, Value at Risk Models Book in PDF, ePub and Kindle
Written by leading market risk academic, Professor Carol Alexander, Value-at-Risk Models forms part four of the Market Risk Analysis four volume set. Building on the three previous volumes this book provides by far the most comprehensive, rigorous and detailed treatment of market VaR models. It rests on the basic knowledge of financial mathematics and statistics gained from Volume I, of factor models, principal component analysis, statistical models of volatility and correlation and copulas from Volume II and, from Volume III, knowledge of pricing and hedging financial instruments and of mapping portfolios of similar instruments to risk factors. A unifying characteristic of the series is the pedagogical approach to practical examples that are relevant to market risk analysis in practice. All together, the Market Risk Analysis four volume set illustrates virtually every concept or formula with a practical, numerical example or a longer, empirical case study. Across all four volumes there are approximately 300 numerical and empirical examples, 400 graphs and figures and 30 case studies many of which are contained in interactive Excel spreadsheets available from the the accompanying CD-ROM . Empirical examples and case studies specific to this volume include: Parametric linear value at risk (VaR)models: normal, Student t and normal mixture and their expected tail loss (ETL); New formulae for VaR based on autocorrelated returns; Historical simulation VaR models: how to scale historical VaR and volatility adjusted historical VaR; Monte Carlo simulation VaR models based on multivariate normal and Student t distributions, and based on copulas; Examples and case studies of numerous applications to interest rate sensitive, equity, commodity and international portfolios; Decomposition of systematic VaR of large portfolios into standard alone and marginal VaR components; Backtesting and the assessment of risk model risk; Hypothetical factor push and historical stress tests, and stress testing based on VaR and ETL.
