Nonlinear spillovers in precious metals markets: A TCN-based extension of the Diebold–Yilmaz Framework

IF 6.9 2区经济学 Q1 BUSINESS, FINANCE

Finance Research Letters Pub Date : 2025-09-20 DOI:10.1016/j.frl.2025.108486

Josef Novotny, Petr Hajek

引用次数: 0

Abstract

This study proposes a nonlinear spillover framework for precious metals markets by integrating Temporal Convolutional Networks (TCNs) into the Diebold–Yilmaz approach. We simulate shocks to approximate the Generalized Forecast Error Variance Decomposition (GFEVD) without relying on linear restrictions. Using daily data from 2014–2023 on metal ETFs, futures, oil, equities, exchange rates, and sentiment indices, we uncover strong nonlinear connectedness, with a total spillover index of 95.22%. The VIX and the Shapiro–Sudhof–Wilson (SSW) sentiment index emerge as dominant net transmitters, while rolling analysis reveals shifts in spillovers around major macroeconomic and geopolitical events.

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贵金属市场的非线性溢出效应：基于tcn的Diebold-Yilmaz框架扩展

本研究通过将时间卷积网络（TCNs）整合到Diebold-Yilmaz方法中，提出了贵金属市场的非线性溢出框架。我们模拟冲击来近似广义预测误差方差分解（GFEVD），而不依赖于线性限制。利用2014-2023年金属etf、期货、石油、股票、汇率和情绪指数的日常数据，我们发现了很强的非线性连通性，总溢出指数为95.22%。波动率指数和夏皮罗-苏德霍夫-威尔逊（SSW）情绪指数成为主要的净传递器，而滚动分析揭示了围绕重大宏观经济和地缘政治事件的溢出效应的变化。

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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