Portfolio tail risk forecasting for international financial assets: A GARCH-MIDAS-R-Vine copula model

IF 3.8 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance Pub Date : 2025-02-10 DOI:10.1016/j.najef.2025.102385

Yinhong Yao , Xiuwen Chen , Zhensong Chen

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引用次数: 0

Abstract

The increasingly complex international environment poses more challenges in accurately forecasting the portfolio risk of international financial assets. Therefore, this paper proposes a generalized autoregressive conditional heteroscedasticity mixed data sampling (GARCH-MIDAS)-R-Vine copula model to forecast the portfolio tail risks, Value at Risk (VaR) and Expected Shortfall (ES), of international financial assets by comprehensively considering the internal complex dependences and external impact of low-frequency macroeconomic factors. Based on the daily prices of Bitcoin, crude oil, gold, seven international stock assets, one global and seven specific monthly economic policy uncertainty (EPU) indexes ranging from January 2011 to August 2022, we find that the proposed model could increase the forecasting accuracy of portfolio tail risk under the optimal information ratio (IR) criterion. Internal high-dimensional dependences can be captured by the flexible R-Vine copula model with 16 kinds of bivariate copula functions, and the external EPU factors observe a significant impact on the corresponding financial assets. Moreover, the CAC 40, the DAX, and the S&P 500 are three dominant financial assets, and Bitcoin and gold are suitable for risk investment and risk hedging assets respectively. These results are beneficial for both risk management and portfolio optimization in the global financial market.

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国际金融资产组合尾部风险预测：GARCH-MIDAS-R-Vine联结模型

日益复杂的国际环境对国际金融资产组合风险的准确预测提出了更大的挑战。为此，本文提出广义自回归条件异方差混合数据抽样(GARCH-MIDAS)-R-Vine copula模型，综合考虑内部复杂依赖关系和低频宏观经济因素的外部影响，对国际金融资产的投资组合尾部风险、风险价值（VaR）和预期缺口（ES）进行预测。基于2011年1月至2022年8月比特币、原油、黄金、7项国际股票资产、1项全球和7项具体月度经济政策不确定性（EPU）指数的日价格，我们发现该模型在最优信息比（IR）准则下可以提高投资组合尾部风险的预测精度。具有16种二元联结函数的柔性R-Vine copula模型可以捕获内部高维依赖关系，外部EPU因素对相应金融资产的影响显著。CAC 40指数、DAX指数和标普500指数是三大主导金融资产，比特币和黄金分别适合风险投资和风险对冲资产。这些结果对于全球金融市场的风险管理和投资组合优化都是有益的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

North American Journal of Economics and Finance Multiple-

CiteScore

7.30

自引率

8.30%

发文量

168

期刊介绍： The focus of the North-American Journal of Economics and Finance is on the economics of integration of goods, services, financial markets, at both regional and global levels with the role of economic policy in that process playing an important role. Both theoretical and empirical papers are welcome. Empirical and policy-related papers that rely on data and the experiences of countries outside North America are also welcome. Papers should offer concrete lessons about the ongoing process of globalization, or policy implications about how governments, domestic or international institutions, can improve the coordination of their activities. Empirical analysis should be capable of replication. Authors of accepted papers will be encouraged to supply data and computer programs.