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Forecasting Volatility of Commodity, Currency, and Stock Markets: Evidence From Markov‐Switching Multifractal Models

Author

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  • Ruipeng Liu
  • Mawuli Segnon
  • Oguzhan Cepni
  • Rangan Gupta

Abstract

This paper adopts a bivariate Markov‐switching multifractal (BMSM) model to reexamine comovement in SV between commodity, foreign exchange (FX), and stock markets. After the 2007–2008 global financial crisis understanding volatility linkages and the correlation structure between these markets becomes very important for risk analysts, portfolio managers, traders, and governments. Using daily data on stock indices and FX rates from developed and emerging countries and a range of commodities such crude oil, natural gas, aluminum, copper, gold, silver, platinum, wheat, corn, soybean, and soybean oil, we find evidence of (re)correlation between commodity, FX, and stock markets. The BMSM model is very competitive to the DCC‐GARCH and the MSM models at short forecasting horizons (1 up to 10 days ahead) but outperforms them at long forecasting horizons (20 days ahead and beyond). Furthermore, we show that an investor with mean‐variance preferences gains in most cases the highest utility benefits based on the BMSM model.

Suggested Citation

  • Ruipeng Liu & Mawuli Segnon & Oguzhan Cepni & Rangan Gupta, 2026. "Forecasting Volatility of Commodity, Currency, and Stock Markets: Evidence From Markov‐Switching Multifractal Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(6), pages 2905-2941, September.
    • Handle: RePEc:wly:jforec:v:45:y:2026:i:6:p:2905-2941
    DOI: 10.1002/for.70145
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