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Robust Conditional Variance and Value-at-Risk Estimation
Company: Journal of Financial Econometrics
Year Of Publication: 2014
Month Of Publication: July
Resource Link: Click here to open
Download Count: 0
View Count: 1410
Comment Num: 0
Language: English
Source: article
Who Can Read: Free
Date: 8-20-2014
Publisher: Administrator
This article is concerned with robust conditional variance and value-at-risk (VaR) estimation. Losses due to idiosyncratic events can have a disproportionate impact on traditional VaR estimates, upwardly biasing these estimates, increasing capital requirements, and unnecessarily reducing the available capital and profitability of financial institutions. We propose new bias-robust conditional variance estimators based on weighted likelihood at heavy-tailed models, as well as VaR estimators based on the latter and on volatility updated historical simulation. The new VaR estimators also use optimally chosen rolling window length and smoothing parameter value. A simulation study illustrates the strong performance of the proposed methodology and highlights the model's ability to mitigate the potentially costly upward bias generated by idiosyncratic shocks. Real data examples and extensive backtesting results illustrate the impact of idiosyncratic shocks on other VaR estimators.
Dupuis, Debbie J. Sign in to follow this author
Papageorgiou, Nicolas Sign in to follow this author
Remillard, Bruno Sign in to follow this author
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