Tail Risk in the Chinese Vegetable Oil Market: Based on the EGAS-EVT Model

This paper uses extreme value theory and exponential generalised autoregressive score models to estimate the tail extremes of financial return series. The peak-over-threshold method based on the generalised pareto distribution is combined with the EGAS models and the nonparametric quantile method is...

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Bibliographic Details
Published in:Discrete dynamics in nature and society 2022, Vol.2022 (1)
Main Authors: Zhang, Yueqiang, Han, Guanghui, Xie, Hui, Wang, Zixing
Format: Article
Language:English
Subjects:
Autoregressive models
Commodity futures
Deflation
Empirical analysis
Extreme value theory
Extreme values
Financial markets
Forecasts and trends
Futures market
International finance
Methods
Normal distribution
Oils & fats
Oils and fats industry
Oilseeds
Palm oil
Rapeseed
Risk
Securities markets
Soybeans
Stochastic models
Stock exchanges
Thresholds
Time series
Vegetable oils
Volatility
Waxes industry
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Summary:This paper uses extreme value theory and exponential generalised autoregressive score models to estimate the tail extremes of financial return series. The peak-over-threshold method based on the generalised pareto distribution is combined with the EGAS models and the nonparametric quantile method is used to determine the thresholds in the POT method, which is used to calculate the value-at-risk of financial markets and to perform backtesting. The empirical analysis was conducted on the soybean oil, rapeseed oil, and palm oil futures indices in the Chinese futures market. The study demonstrated that the EGAS-POT models based on nonparametric quantile thresholds can effectively characterise tail risk and provide a feasible measure of risk for investors.
ISSN:1026-0226
1607-887X
DOI:10.1155/2022/1904490