Copula-based dynamic networks for forecasting stock market volatility

IF 7.4 2区经济学 Q1 BUSINESS, FINANCE

Finance Research Letters Pub Date : 2025-07-09 DOI:10.1016/j.frl.2025.107918

Shahab Nankali, Laleh Tafakori, Mahdi Jalili, Xiaolu Hu

引用次数: 0

Abstract

This study enhances volatility forecasting by integrating copula-based methods with a dynamic network log- ARCH model. The D-vine copula quantile regression and copula entropy methods, which capture nonlinear dependencies and select key predictors, are used to construct a high-dimensional financial network. The model incorporates time-lagged volatility and information from neighboring stocks, enabling efficient risk propagation. Applied to Dow Jones Index constituents (2012–2024), our approach improves forecasting accuracy while reducing reliance on hyperparameters. We also find that the low-volatility premium coincides with lower downside risk, with low- and high-volatility stocks exhibiting equal or lower return standard deviations across all networks. Notably, the copula-based network identifies stocks that enhance alpha while mitigating portfolio risk, offering a robust tool for portfolio construction and risk management.

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基于copula的动态网络预测股票市场波动

该研究通过将基于copula的方法与动态网络日志ARCH模型相结合来增强波动性预测。利用D-vine copula分位数回归和copula熵方法捕获非线性依赖关系并选择关键预测因子，构建了一个高维金融网络。该模型结合了时滞波动率和邻近股票的信息，实现了有效的风险传播。应用于道琼斯指数成分股（2012-2024），我们的方法提高了预测准确性，同时减少了对超参数的依赖。我们还发现，低波动性溢价与较低的下行风险相吻合，在所有网络中，低波动性和高波动性股票表现出相等或更低的回报标准差。值得注意的是，基于copula的网络识别出在降低投资组合风险的同时提高alpha的股票，为投资组合构建和风险管理提供了一个强大的工具。

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

Finance Research Letters BUSINESS, FINANCE-

CiteScore

11.10

自引率

14.40%

发文量

863

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