7

287-338

52

 

John Wiley & Sons, Inc. (Visit publisher website)

2005

English

 

 

 

Advanced

 

 

Quantitative analysis, value at risk, RiskMetrics, square root of time rule, econometric approach to VaR calculation, expected return and forecast error, quantile estimation, quantile and order statistics, quantile regression, extreme value theory (EVT), empirical estimation, maximum likelihood method, regression method, application of EVT to stock returns, EVT approach to VaR, multiperiod VaR, VaR for a Short Position, mean excess function, model checking, exceedance rule, distribution of excesses, independence

 

The intended audience for this chapter includes risk managers seeking to improve methods for determining the likelihood of extreme price changes and researchers seeking insight to the phenomena that affect extreme outcomes. In this chapter conditional value at risk implies modeling extreme changes with additional explanatory variables. The Riskmetrics IGARCH(1,1) model is carefully illustrated through estimation of value at risk statistics for an equity position. Emphasis is placed on the choice of time series model and the necessary steps to construct multiple period risk estimates. The chapter contains a clear presentation of the traditional extreme value value at risk estimation methods. Peaks over threshold estimation methods are also introduced. The peaks over threshold model serves as the basis for an expanded model of risk estimation incorporating additional explanatory variables. The risk measure expected shortfall is introduced and estimated for the example data.

 

7.1 Value at Risk
7.2 RiskMetrics
7.2.1 Discussion
7.2.2 Multiple Positions
7.3 An Econometric Approach to VaR Calculation
7.3.1 Multiple Periods
7.4 Quantile Estimation
7.4.1 Quantile and Order Statistics
7.4.2 Quantile Regression
7.5 Extreme Value Theory
7.5.1 Review of Extreme Value Theory
7.5.2 Empirical Estimation
7.5.3 Application to Stock Returns
7.6 Extreme Value Approach to VaR
7.6.1 Discussion
7.6.2 Multiperiod VaR
7.6.3 VaR for a Short Position
7.6.4 Return Level
7.7 A New Approach Based on the Extreme Value Theory
7.7.1 Statistical Theory
7.7.2 Mean Excess Function
7.7.3 A New Approach to Modeling Extreme Values
7.7.4 VaR Calculation Based on the New Approach
7.7.5 An Alternative Parameterization
7.7.6 Use of Explanatory Variables
7.7.7 Model Checking
7.7.8 An Illustration
Exercises
References

 

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The Second Edition of this critically acclaimed text provides a comprehensive and systematic introduction to financial econometric models and their applications in modeling and predicting financial time series data. This edition continues to emphasize empirical financial data and focuses on real-world examples. Following this approach, readers will master key aspects of financial time series, including volatility modeling, neural network applications, market microstructure and high-frequency financial data, continuous-time models and Ito`s Lemma, Value at Risk, multiple returns analysis, financial factor models, and econometric modeling via computation-intensive methods.

The author begins with the basic characteristics of financial time series data, setting the foundation for the three main topics:

* Analysis and application of univariate financial time series
* Return series of multiple assets
* Bayesian inference in finance methods

This new edition is a thoroughly revised and updated text, including the addition of S-PlusŪ commands and illustrations. Among the new material, readers will find:

* Consistent covariance estimation under heteroscedasticity and serial correlation
* Alternative approaches to volatility modeling
* Financial factor models
* State-space models
* Kalman filtering
* Estimation of stochastic diffusion models

This is an ideal textbook for MBA students as well as a reference for researchers and professionals in business and finance.