Can intraday data improve the joint estimation and prediction of risk measures? Evidence from a variety of realized measures

IF 3.4 3区 经济学 Q1 ECONOMICS Journal of Forecasting Pub Date : 2024-03-07 DOI:10.1002/for.3111
Zhimin Wu, Guanghui Cai
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Abstract

In recent years, the semiparametric methods for the joint estimation and prediction of value at risk (VaR) and expected shortfall (ES) have triggered great interests and attention. Compared to existing literature which usually incorporates realized volatility (RV) into the dynamic semiparametric risk models, this paper considers three more robust proxies (medRV, BPV, and RK) of intraday volatility in the models to verify whether high-frequency information can improve the joint prediction ability of risk measures. To strengthen the persuasion of conclusions, four international stock indices (S&P500, Nikkei225, GDAXI, and DJIA) are applied to these models to estimate and forecast VaR and ES at different probability levels (1%, 2.5%, 5%, and 10%). Then, the predicted VaR and ES are backtested by several methods individually, and the popular score function FZ0 and MCS test are used to compare the effects of jointly predicting risk measures. Our results confirm that these semiparametric models containing intraday information outperform the benchmark models for four stocks and various probability levels, and medRV is the best volatility measure in improving the effects of models.

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盘中数据能否改善风险度量的联合估计和预测?来自各种已实现衡量指标的证据
近年来,用于联合估计和预测风险价值(VaR)和预期缺口(ES)的半参数方法引发了人们的极大兴趣和关注。与现有文献通常将已实现波动率(RV)纳入动态半参数风险模型相比,本文在模型中考虑了三种更稳健的日内波动率替代指标(medRV、BPV 和 RK),以验证高频信息是否能提高风险度量的联合预测能力。为了加强结论的说服力,将四个国际股票指数(S&P500、日经 225、GDAXI 和道琼斯工业平均指数)应用于这些模型,以估计和预测不同概率水平(1%、2.5%、5% 和 10%)的 VaR 和 ES。然后,用几种方法分别对预测的 VaR 和 ES 进行回溯测试,并使用流行的评分函数 FZ0 和 MCS 测试来比较联合预测风险度量的效果。我们的结果证实,对于四种股票和各种概率水平,这些包含盘中信息的半参数模型优于基准模型,而 medRV 是改善模型效果的最佳波动率指标。
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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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