尾部风险预测及其在商品期货市场保证金要求中的应用

IF 3.4 3区 经济学 Q1 ECONOMICS
Yun Feng, Weijie Hou, Yuping Song
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引用次数: 0

摘要

本研究以螺纹钢期货为例,提出了一种名为自回归条件极值(AEV)的动态分析框架,旨在对商品期货市场的每日最大跌幅进行建模。研究表明,就样本内拟合和样本外预测精度而言,AEV 优于 AR 或广义自回归条件异方差(GARCH)型基准模型。值得注意的是,AEV 的时变形状参数(尾部指数)能灵敏地捕捉尾部风险的聚类性质,并区分多头和空头市场。研究还提出了基于 AEV 的风险值(VaR)和预期缺口(ES)的理论结论,并对螺纹钢期货市场的尾部风险进行了实证测量和预测。此外,研究还扩展了方法论,创建了中国商品期货的动态保证金模型,表明基于 AEV 的模型能有效实现指定的风险覆盖目标,并显著降低当前的交易所保证金要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tail risk forecasting and its application to margin requirements in the commodity futures market

This study presents a dynamic analysis framework called autoregressive conditional extreme value (AEV), designed for modeling the daily maximum drawdowns of commodity futures markets, using steel rebar futures as an illustrative example. The research demonstrates that AEV outperforms AR or generalized autoregressive conditional heteroskedasticity (GARCH)-type benchmark models in terms of in-sample fitting and out-of-sample forecasting accuracy. Notably, AEV's time-varying shape parameter (tail index) sensitively captures the clustering nature of tail risk and differentiates between long- and short-side markets. The study also presents theoretical findings regarding AEV-based value at risk (VaR) and expected shortfall (ES), and empirically measures and predicts the tail risk of the steel rebar futures market. Moreover, the research extends the methodology to create a dynamic margin model for Chinese commodity futures, showing that the AEV-based model effectively achieves the specified risk coverage targets and significantly reduces current exchange margin requirements.

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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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