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Modelling Conditional Volatility And Asymmetry In Gold Futures Returns

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Listed:
  • SHAHIL RAZA

    (DEPARTMENT OF COMMERCE, ALIGARH MUSLIM UNIVERSITY, ALIGARH, UTTAR PRADESH 202001, INDIA)

  • AMAN SHREEVASTAVA

    (P.G. DEPARTMENT OF COMMERCE AND MANAGEMENT, PURNEA UNIVERSITY, PURNEA, BIHAR, INDIA-854301)

  • BHARAT KUMAR MEHER

    (P.G. DEPARTMENT OF COMMERCE AND MANAGEMENT, PURNEA UNIVERSITY, PURNEA, BIHAR, INDIA-854301)

  • RAMONA BIRAU

    (CONSTANTIN BRANCUSI UNIVERSITY OF TARGU JIU, FACULTY OF ECONOMIC SCIENCE, TG-JIU, ROMANIA)

  • VIRGIL POPESCU

    (UNIVERSITY OF CRAIOVA, FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION, CRAIOVA, ROMANIA)

  • STEFAN MARGARITESCU

    (UNIVERSITY OF CRAIOVA, EUGENIU CARADA DOCTORAL SCHOOL OF ECONOMIC SCIENCES, CRAIOVA, ROMANIA)

  • GABRIELA ANA MARIA LUPU (FILIP)

    (UNIVERSITY OF CRAIOVA, EUGENIU CARADA DOCTORAL SCHOOL OF ECONOMIC SCIENCES, CRAIOVA, ROMANIA)

  • CRISTINA SULTANOIU (PATULARU)

    (UNIVERSITY OF CRAIOVA, EUGENIU CARADA DOCTORAL SCHOOL OF ECONOMIC SCIENCES, CRAIOVA, ROMANIA)

Abstract

This study investigates the time-varying volatility dynamics of Gold Futures (GCZ5) daily returns over a ten-year period (October 28, 2015, to October 28, 2025). Given gold's critical role as a safe-haven asset, accurate volatility modeling is essential for contemporary risk management and derivative pricing. We employ the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family of models, including symmetric and asymmetric specifications (GJR-GARCH and EGARCH), using heavy-tailed distributions (Student's t and GED) to capture the observed leptokurtosis. Preliminary diagnostics confirmed the presence of volatility clustering and non-normality in the returns series. Model selection, based on the Akaike Information Criterion (AIC) and Schwarz Criterion (SIC), identified the EGARCH(1,1) model with a Student's t error distribution as the superior specification. The key findings reveal extremely high volatility persistence (beta approx 0.984), indicating that volatility shocks have a long-lasting impact on the GCZ5 risk profile. Furthermore, we detect a statistically significant contravariant asymmetry, where positive returns (gains) increase future volatility more than negative returns (losses) of the same magnitude. These quantitative insights are vital for institutional investors seeking to optimize dynamic hedging ratios, accurately price options, and set appropriate risk limits in the highly volatile gold market.

Suggested Citation

  • Shahil Raza & Aman Shreevastava & Bharat Kumar Meher & Ramona Birau & Virgil Popescu & Stefan Margaritescu & Gabriela Ana Maria Lupu (Filip) & Cristina Sultanoiu (Patularu), 2025. "Modelling Conditional Volatility And Asymmetry In Gold Futures Returns," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 6, pages 111-125, December.
    • Handle: RePEc:cbu:jrnlec:y:2025:v:6:p:111-125
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    References listed on IDEAS

    as
    1. Simran & Anil Kumar Sharma, 2025. "Role of Economic Policy Uncertainty in Forecasting Gold Futures Volatility: Evidence From India," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 45(8), pages 1006-1022, August.
    2. Awartani, Basel & Maghyereh, Aktham, 2025. "The value of cross market volatility in improving the forecast accuracy of risk in the gold, the dollar and the oil futures markets," Finance Research Letters, Elsevier, vol. 83(C).
    3. Li Zhang & Lu Wang & Yu Ji & Zhigang Pan, 2025. "Forecasting Gold Volatility in an Uncertain Environment: The Roles of Large and Small Shock Sizes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(4), pages 1478-1500, July.
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