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

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  • Novotny, Josef
  • Hajek, Petr

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.

Suggested Citation

  • Novotny, Josef & Hajek, Petr, 2025. "Nonlinear spillovers in precious metals markets: A TCN-based extension of the Diebold–Yilmaz Framework," Finance Research Letters, Elsevier, vol. 86(PC).
    • Handle: RePEc:eee:finlet:v:86:y:2025:i:pc:s1544612325017404
    DOI: 10.1016/j.frl.2025.108486
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