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Estimation of Extreme Value-at-Risk: An EVT Approach for Quantile GARCH Model
Company: Economics Letters
Company Url: Click here to open
Year Of Publication: 2014
Month Of Publication: July
Resource Link: Click here to open
Download Count: 0
View Count: 1013
Comment Num: 0
Language: English
Source: article
Who Can Read: Free
Date: 7-6-2014
Publisher: Administrator
Summary
We proposed a method to estimate extreme conditional quantiles by combining quantile GARCH model of Xiao and Koenker (2009) and extreme value theory (EVT) approach. We first estimate the latent volatility process using the information of intermediate quantiles. We then apply EVT to the tail observations to obtain a sound estimate of the likelihood of experiencing an extreme event. Quantile autoregression and EVT together improve efficiency in estimation of extreme quantiles, by borrowing information from neighbor quantiles. Monte Carlo simulation indicates that, the proposed method is promising to provide more accurate estimates for VaR of a financial portfolio, where non-Gaussian tail is present.
Author(s)
Yi, Yanping Sign in to follow this author
Feng, Xingdong Sign in to follow this author
Huang, Zhuo Sign in to follow this author
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