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Forecasting VaR and ES using the joint regression combined forecasting model in the Chinese stock market

Xunfa Lu (School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, China)
Kang Sheng (School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, China)
Zhengjun Zhang (Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, USA)

International Journal of Emerging Markets

ISSN: 1746-8809

Article publication date: 29 December 2022

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Abstract

Purpose

This paper aims to better jointly estimate Value at Risk (VaR) and expected shortfall (ES) by using the joint regression combined forecasting (JRCF) model.

Design/methodology/approach

Combining different forecasting models in financial risk measurement can improve their prediction accuracy by integrating the individual models’ information. This paper applies the JRCF model to measure VaR and ES at 5%, 2.5% and 1% probability levels in the Chinese stock market. While ES is not elicitable on its own, the joint elicitability property of VaR and ES is established by the joint consistent scoring functions, which further refines the ES’s backtest. In addition, a variety of backtesting and evaluation methods are used to analyze and compare the alternative risk measurement models.

Findings

The empirical results show that the JRCF model outperforms the competing models. Based on the evaluation results of the joint scoring functions, the proposed model obtains the minimum scoring function value compared to the individual forecasting models and the average combined forecasting model overall. Moreover, Murphy diagrams’ results further reveal that this model has consistent comparative advantages among all considered models.

Originality/value

The JRCF model of risk measures is proposed, and the application of the joint scoring functions of VaR and ES is expanded. Additionally, this paper comprehensively backtests and evaluates the competing risk models and examines the characteristics of Chinese financial market risks.

Keywords

Acknowledgements

Funding: This work was supported by the Social Science Fund of Jiangsu Province (No. 20GLB008), the National Natural Science Foundation of China (No. 71701104) and the MOE Project of Humanities and Social Sciences (No. 17YJC790102).

Citation

Lu, X., Sheng, K. and Zhang, Z. (2022), "Forecasting VaR and ES using the joint regression combined forecasting model in the Chinese stock market", International Journal of Emerging Markets, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJOEM-06-2022-0941

Publisher

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Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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