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A Comparison Between Advanced Value at Risk Models and Their Backtesting in Different Portfolios

A Comparison Between Advanced Value at Risk Models and Their Backtesting in Different Portfolios
Author: Christian Steinlechner
Publisher:
Total Pages: 92
Release: 2013-08
Genre:
ISBN: 9783656319009

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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.


Backtesting Value at Risk and Expected Shortfall

Backtesting Value at Risk and Expected Shortfall
Author: Simona Roccioletti
Publisher: Springer Gabler
Total Pages: 0
Release: 2015-12-11
Genre: Business & Economics
ISBN: 9783658119072

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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.


Measuring Traded Market Risk

Measuring Traded Market Risk
Author: Colleen Cassidy
Publisher:
Total Pages: 37
Release: 1997
Genre: Bank capital
ISBN:

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The proposed market-risk capital-adequacy framework, to be implemented at the end of 1997, requires Australian banks to hold capital against market risk. A fundamental component of this framework is the opportunity for banks to use their value-at-risk (VaR) models as the basis of the market-risk capital charge. Value-at-risk measures the potential loss on a portfolio for a specified level of confidence if adverse movements in market prices were to occur. This paper examines the VaR measure and some of the techniques available for assessing the performance of a VaR model. The first section of the paper uses a simple portfolio of two spot foreign exchange positions to illustrate three of the approaches used in the calculation of a VaR measure: variance-covariance, historical simulation and Monte-Carlo simulation. It is concluded that, although VaR is a very useful tool, it is not without its shortcomings and so should be supplemented with other risk-management techniques. The second section of the paper focuses on the use of backtesting the comparison of model-generated VaR numbers with actual profits and losses for assessing the accuracy of a VaR model. Several statistical tests are demonstrated by testing daily VaR and profit and loss data obtained from an Australian bank. The paper concludes that, although the tests are not sufficiently precise to form the basis of regulatory treatment of banks' VaR results, the tests do provide useful diagnostic information for evaluating model performance.


Advanced REIT Portfolio Optimization

Advanced REIT Portfolio Optimization
Author: W. Brent Lindquist
Publisher: Springer Nature
Total Pages: 268
Release: 2022-11-09
Genre: Business & Economics
ISBN: 3031152867

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This book provides an investor-friendly presentation of the premises and applications of the quantitative finance models governing investment in one asset class of publicly traded stocks, specifically real estate investment trusts (REITs). The models provide highly advanced analytics for REIT investment, including: portfolio optimization using both historic and predictive return estimation; model backtesting; a complete spectrum of risk assessment and management tools with an emphasis on early warning systems, risk budgeting, estimating tail risk, and factor analysis; derivative valuation; and incorporating ESG ratings into REIT investment. These quantitative finance models are presented in a unified framework consistent with dynamic asset pricing (rational finance). Given its scope and practical orientation, this book will appeal to investors interested in portfolio optimization and innovative tools for investment risk assessment.


Backtesting Value-at-Risk

Backtesting Value-at-Risk
Author: Stefano Colucci
Publisher:
Total Pages: 36
Release: 2014
Genre:
ISBN:

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The purpose of this paper is to present a comparison between two risk models for estimating VaR, Historical Simulation and Monte Carlo Filtered Bootstrap. We perform three tests, Unconditional Coverage, Independence and Conditional Coverage according to Christoffersen, P., Pellettier D. (2004) paper. We present results on both VaR 1% and VaR 5% on one day horizon for the two models for the following indices: Standard&Poors 500, Topix, Dax, MSCI United Kingdom, MSCI France, Italy Comit Globale, MSCI Canada, MSCI Emerging Markets, RJ/CRB. Our results show that Filtered Bootstrap Approach satisfy Conditional Coverage for all tested indices while Historical Simulation has many rejection cases. Finally we also tested in a regulatory framework (rolling window of 250 daily observations) the two models and the advantages of using a conditional coverage methodology to validate risk models.


Modeling Value-at-Risk for Commodities

Modeling Value-at-Risk for Commodities
Author: Lionel Gerboth
Publisher:
Total Pages:
Release: 2013
Genre:
ISBN:

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Assessing the significance of the market risk of a portfolio of financial securities has long been acknowledged by academics and practitioners. In measuring market risk, one of the most advanced techniques in the literature is the value-at-risk (VaR) measurement. Although widely used for traditional markets, in commodity markets which usually exhibit peculiar features like volatility jumps and price spikes, the use of nonparametric and semi-parametric VaR modeling techniques has not yet been analyzed exhaustively. This master thesis studies in a comprehensive out-of-sample backtesting procedure traditional models like historical simulation, filtered historical simulation, univariate GARCH-type models with and without leverage and compares them to state of the art modeling techniques like EVT-EGARCH models using the peak-over-threshold procedure as well as EVT-EGARCH-Copula models with Gaussian and Student-t-copulas. Goal of the analysis is to provide a comprehensive comparison of the forecast ability of each model with respect to each out of the four main commodity classes (agricultural, energy, livestock and metals). Among all models presented, empirical results for a number of adequacy and accuracy tests suggest that the conditional t-copula approach with EVT modeled tails and EGARCH standardized residuals performs best for high confidence levels and across all commodity classes analyzed, shortly followed by the Gaussian copula approach. For lower significance levels however, the GARCH and EGARCH model seem to outperform. As anticipated, the naïve historical simulation approach performs worst over all confidence levels and commodity portfolios. The filtered historical simulation and the EVT model demonstrate a mixed performance.


The Validation of Risk Models

The Validation of Risk Models
Author: S. Scandizzo
Publisher: Springer
Total Pages: 242
Release: 2016-07-01
Genre: Business & Economics
ISBN: 1137436964

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This book is a one-stop-shop reference for risk management practitioners involved in the validation of risk models. It is a comprehensive manual about the tools, techniques and processes to be followed, focused on all the models that are relevant in the capital requirements and supervisory review of large international banks.


What are the chances and limitations of value-at-risk (VaR) models?

What are the chances and limitations of value-at-risk (VaR) models?
Author: Alexander Linn
Publisher: GRIN Verlag
Total Pages: 76
Release: 2006-05-21
Genre: Business & Economics
ISBN: 3638503291

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Seminar paper from the year 2004 in the subject Business economics - Controlling, grade: 1,7, European Business School - International University Schloß Reichartshausen Oestrich-Winkel (Department of Accounting and Control), language: English, abstract: The risk and return framework is generally accepted and discussed by scientists, at least since Markowitz introduced his Portfolio Theory in 1952. Subsequently, models were developed to evaluate investments under consideration of risk and return. Traditionally, practitioners primarily focused on past earnings as a measure of the profitability of an investment, without adequately considering potential risks. Therefore, the development of professional risk management systems was often neglected. Thus, the possibility of high losses was not appropriately incorporated in their investment strategies. The consequences of such mistreatment became evident in the mid 1990s, when some of the world’s largest companies faced huge losses and sometimes even insolvency. Most of these failures were a direct result of inappropriate use of financial instruments and insufficient internal control mechanisms. The most spectacular debacles even resulted in losses of more than one billion dollars for each affected institution. In case of Barings Bank, a single trader ruined the 233-year old British financial institution by inappropriate investments in high-risk futures in 1995. The consequent loss of $1.3 billion, realized in a very short period, could not be absorbed and forced the downfall of Barings. At Daiwa Bank, it was also a single trader who caused a $1.1 billion deficit. In contrast, the losses were accumulated over 11 years from 1984. Another well-publicized bankruptcy was declared in 1994 by the Californian Orange County, after losses of $1.8 billion. Such evidence of poor risk management and control shows that proper financial risk management is crucial for all kinds of institutions in order to guarantee stability and continuity. Therefore, it is necessary to establish adequate risk management processes and to develop appropriate tools, which quantify risk exposures of both entire institutions and single financial instruments. This risk quantification should alert management early enough to prevent exceptional losses. One of the key concepts addressing these prob-lems of modern risk management was introduced in 1993 with the Value-at-Risk (VaR) models.


Comparative Analysis of Value at Risk (VAR) Methods for Portfolio with Non-Linear Return

Comparative Analysis of Value at Risk (VAR) Methods for Portfolio with Non-Linear Return
Author: Manohar Lal
Publisher:
Total Pages: 14
Release: 2013
Genre:
ISBN:

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In this study various value at risk methods such as Historical Simulation, Variance-Covariance Approach and Monte Carlo Simulation are calculated, compared and tested for accuracy. Backtesting for the VaR methods is applied to check the accuracy of the VaR methods. The portfolio includes equally weighted three banking stock and one at-the-money (ATM) call option for one of the banking stock in the portfolio. The log return for the portfolio and individual investments are calculated. Different VaR calculation methods are used to calculate the downside risk of the portfolio and individual investments. VaR is calculated at 95% and 99% confidence level for the portfolio and individual securities. The value at risk for the portfolio at 95% confidence level from all the three methods are within the defined level of downside risk, while at 99% confidence level only Mote Carlo Simulation method provides good approximation of downside risk for a portfolio with options. Thus from this study it is inferred that for instrument or portfolio with non-linear return structure Monte Carlo simulation method provide good approximation of the downside risk.