Value at risk prediction of financial assets series based on generalized Pareto distribution method

Yaohui Bai; Huayang Li; Ping Bai; Keqian Wan; Gang Xiao; Huilian Xu

doi:10.1117/12.2675167

25 May 2023 Value at risk prediction of financial assets series based on generalized Pareto distribution method

Yaohui Bai, Huayang Li, Ping Bai, Keqian Wan, Gang Xiao, Huilian Xu

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 1263626 (2023) https://doi.org/10.1117/12.2675167
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

Value at Risk estimation and forecasting of financial asset series has always been a hot issue in economic and financial research. The RiskMetrics method is often used in the VaR estimation of traditional financial asset series. However, the RiskMetrics method cannot effectively capture the dynamic characteristics of extreme events related to the financial asset series VaR. In this paper, the generalized Pareto distribution method in extreme value theory is used to model the tail distribution characteristics of France’s CAC40 index and to analyze and predict its VaR. At the same time, to compare the performance of the models, the RiskMetrics method was selected for comparison. The calculation and test results show that the GPD method in VaR estimation, although not sensitive to risk changes, still gives good prediction performance. Conversely, the RiskMetrics method, although more responsive to risk changes, has insufficient performance for VaR estimation. Therefore, the overall performance of the GPD method is better than that of the RiskMetrics method, which is also shown by the test results of the predictions of the two models.

Citation Download Citation

Yaohui Bai, Huayang Li, Ping Bai, Keqian Wan, Gang Xiao, and Huilian Xu "Value at risk prediction of financial assets series based on generalized Pareto distribution method", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 1263626 (25 May 2023); https://doi.org/10.1117/12.2675167

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available