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A Study of Value-at-Risk Based on M-Estimators of the Conditional Heteroscedastic Models

✍ Scribed by Farhat Iqbal; Kanchan Mukherjee


Publisher
John Wiley and Sons
Year
2011
Tongue
English
Weight
119 KB
Volume
31
Category
Article
ISSN
0277-6693

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✦ Synopsis


ABSTRACT

In this paper, we investigate the performance of a class of M‐estimators for both symmetric and asymmetric conditional heteroscedastic models in the prediction of value‐at‐risk. The class of estimators includes the least absolute deviation (LAD), Huber's, Cauchy and B‐estimator, as well as the well‐known quasi maximum likelihood estimator (QMLE). We use a wide range of summary statistics to compare both the in‐sample and out‐of‐sample VaR estimates of three well‐known stock indices. Our empirical study suggests that in general Cauchy, Huber and B‐estimator have better performance in predicting one‐step‐ahead VaR than the commonly used QMLE. Copyright © 2011 John Wiley & Sons, Ltd.


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