VAR Week

 

This is VaR Week (ISSN 1534-1054) for 03/31/2007 - 04/06/2007
Risk management information and news >from GloriaMundi.org.

Risk Management News This Week

New Resources Added This Week

1
Title
Modeling Portfolio Defaults Using Hidden Markov Models with Covariates

Authors
Banachewicz, Konrad; van der Vaart, Aad; Lucas, Andre;

Date
October 2006

Category
Credit Risk
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Abstract
We extend the Hidden Markov Model for defaults of Crowder, Davis, and Giampieri (2005) to include covariates. The covariates enhance the prediction of transition probabilities from high to low default regimes. To estimate the model, we extend the EM estimating equations to account for the time varying nature of the conditional likelihoods due to sample attrition and extension. Using empirical U.S. default data, we find that GDP growth, the term structure of interest rates and stock market returns impact the state transition probabilities. The impact, however, is not uniform across industries. We only find a weak correspondence between industry credit cycle dynamics and general business cycles.

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2
Title
Pricing Functions and Risk Measures in Incomplete Markets

Authors
Bion-Nadal, Jocelyne;

Date
June 2005

Category
Properties of VaR
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Abstract
We present a new approach for pricing and making decisions of investment in incomplete markets. This we do without fixing in advance any probability measure. The key concept that we introduce is a notion of pricing function compatible with a family of bid and ask prices observed in the market. This method links the theory of asset pricing and the theory of risk measuring.

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3
Title
Mathematics in Financial Risk Management

Authors
Eberlein, Ernst; Frey, Rudiger; Kalkbrener, Michael; Overbeck, Ludger ;

Date
March 2007

Category
Credit Risk
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Abstract
The paper gives an overview of mathematical models and methods used in financial risk management; the main area of application is credit risk. A brief introduction explains the mathematical issues arising in the risk management of a portfolio of loans. The paper continues with a formal overview of credit risk management models and discusses axiomatic approaches to risk measurement. We close with a section on dynamic credit risk models used in the pricing of credit derivatives. Mathematical techniques used stem >from probability theory, statistics, convex analysis and stochastic process theory.

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4
Title
A Remark on Law Invariant Convex Risk Measures

Authors
Kusuoka, Shigeo;

Date
October 2006

Category
Properties of VaR
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Abstract
The author gives a simple proof of the representation theorem for law invariant convex risk measures which was obtained by Kusuoka [6], Frittelli-Gianin [3] and Jouini- Schachermayer-Touzi [5].

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5
Title
The Predictive Performance of Asymetric Normal Mixture GARCH in Risk Management: Evidence From Turkey

Authors
Çifter, Atilla; Ozun, Alper;

Date
January 2007

Category
Backtesting
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Abstract
The purpose of this study is to test predictive performance of Asymmetric Normal Mixture Garch (NMAGARCH) and other Garch models based on Kupiec and Christoffersen tests for Turkish equity market. The empirical results show that the NMAGARCH perform better based on %99 CI out-of-sample forecasting Christoffersen test where Garch with normal and student-t distribution perform better based on %95 Cl out-of-sample forecasting Christoffersen test and Kupiec test. These results show that none of the model including NMAGARCH outperforms other models in all cases as trading position or confidence intervals and these results shows that volatility model should be chosen according to confidence interval and trading positions. Besides, NMAGARCH increases predictive performance for higher confidence internal as Basel requires.

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6
Title
The Level and Quality of Value-at-Risk Disclosure by Commercial Banks

Authors
Perignon, Christophe; Smith, Daniel R.;

Date
December 2006

Category
Risk Reporting
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Abstract
We study (1) the level of Value-at-Risk (VaR) disclosure and (2) the accuracy of the disclosed VaR figures for a sample of US and international commercial banks. To measure the level of VaR disclosures, we develop a VaR disclosure index that captures many different facets of market risk disclosure. Using panel data over the period 1996-2005, we find large differences in the level of disclosure between US commercial banks and an overall upward trend in the quantity of information released to the public. Our cross-sectional analysis of the largest banks in the world indicates that the US disclosures are below the average, although some banks, such as Bank of America and Wachovia, score very high on our 15-point VaR disclosure scale. We also find that Historical Simulation is by far the most popular VaR method. We assess the accuracy of the disclosed VaR figures by studying whether actual daily VaRs contain information about the volatility of subsequent trading revenues. We find that VaR, especially if it is computed using Historical Simulation, contains very little information about future trading revenue volatility and that a simple GARCH model often dominates bank proprietary VaR models.

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7
Title
Estimation Risk Effects on Backtesting for Parametric Value-at-Risk Models

Authors
Escanciano, J. Carlos; Olmo, Jose;

Date
March 2007

Category
Backtesting
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Abstract
One of the implications of the creation of Basel Committee on Banking Supervision was the implementation of Value-at-Risk (VaR) as the standard tool for measuring market risk. Thereby the correct specification of parametric VaR models became of crucial importance in order to provide accurate and reliable risk measures. If the underlying risk model is not correctly specified, VaR estimates understate/overstate risk exposure. This can have dramatic consequences on stability and reputation of financial institutions or lead to sub-optimal capital allocation. We show that the use of the standard unconditional backtesting procedures to assess VaR models is completely misleading. These tests do not consider the impact of estimation risk and therefore use wrong critical values to assess market risk. The purpose of this paper is to quantify such estimation risk in a very general class of dynamic parametric VaR models and to correct standard backtesting procedures to provide valid inference in specification analyses. A Monte Carlo study illustrates our theoretical findings in finite-samples. Finally, an application to S&P500 Index shows the importance of this correction and its impact on capital requirements as imposed by Basel Accord, and on the choice of dynamic parametric models for risk management.

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New in the GloriaMundi Risk Management Career Center

Risk Quantitative Analyst
The D. E. Shaw Group
Salary: Attractive
USA-NY-New York City
05 Apr

The D. E. Shaw group, a global investment and technology development firm with approximately US $29 billion in aggregate inve...