Estimation of Extreme Value-at-Risk: An EVT Approach for Quantile GARCH Model

10 Pages Posted: 12 Apr 2014 Last revised: 16 Apr 2014

See all articles by Yanping Yi

Yanping Yi

Shanghai University of Finance and Economics - School of Economics

Xingdong Feng

Shanghai University of Finance and Economics - School of Statistics and Management

Zhuo Huang

National School of Development, Peking University

Date Written: April 10, 2014

Abstract

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.

Keywords: Extreme value theory; GARCH; Quantile regression; Semiparametric; Value at Risk

Suggested Citation

Yi, Yanping and Feng, Xingdong and Huang, Zhuo, Estimation of Extreme Value-at-Risk: An EVT Approach for Quantile GARCH Model (April 10, 2014). Available at SSRN: https://ssrn.com/abstract=2423445 or http://dx.doi.org/10.2139/ssrn.2423445

Yanping Yi

Shanghai University of Finance and Economics - School of Economics ( email )

777 Guoding Road
Shanghai, 200433
China

Xingdong Feng

Shanghai University of Finance and Economics - School of Statistics and Management ( email )

777 Guoding Road
Shanghai, Shanghai 200433
China

Zhuo Huang (Contact Author)

National School of Development, Peking University ( email )

No. 38 Xueyuan Road
Haidian District
Beijing, Beijing 100871
China

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