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Discrete Dynamics in Nature and Society
Volume 2017 (2017), Article ID 5183914, 9 pages
Research Article

Dynamic VaR Measurement of Gold Market with SV-T-MN Model

1Library, Chongqing University of Technology, Chongqing, China
2School of Science, Chongqing University of Technology, Chongqing, China
3School of Accounting, Chongqing University of Technology, Chongqing, China

Correspondence should be addressed to Bao Yang

Received 16 May 2017; Revised 18 September 2017; Accepted 26 September 2017; Published 23 October 2017

Academic Editor: Francisco R. Villatoro

Copyright © 2017 Fenglan Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


VaR (Value at Risk) in the gold market was measured and predicted by combining stochastic volatility (SV) model with extreme value theory. Firstly, for the fat tail and volatility persistence characteristics in gold market return series, the gold price return volatility was modeled by SV-T-MN (SV-T with Mixture-of-Normal distribution) model based on state space. Secondly, future sample volatility prediction was realized by using approximate filtering algorithm. Finally, extreme value theory based on generalized Pareto distribution was applied to measure dynamic risk value (VaR) of gold market return. Through the proposed model on the price of gold, empirical analysis was investigated; the results show that presented combined model can measure and predict Value at Risk of the gold market reasonably and effectively and enable investors to further understand the extreme risk of gold market and take coping strategies actively.