Forecasting oil and gold volatilities with sentiment indicators under structural breaks

Jiawen Luo, Riza Demirer, Rangan Gupta and Qiang Ji

Energy Economics, 2022, vol. 105, issue C

Abstract: This paper contributes to the literature on forecasting the realized volatility of oil and gold by (i) utilizing the Infinite Hidden Markov (IHM) switching model within the Heterogeneous Autoregressive (HAR) framework to accommodate structural breaks in the data and (ii) incorporating, for the first time in the literature, various sentiment indicators that proxy for the speculative and hedging tendencies of investors in these markets as predictors in the forecasting models. We show that accounting for structural breaks and incorporating sentiment-related indicators in the forecasting model does not only improve the out-of-sample forecasting performance of volatility models but also has significant economic implications, offering improved risk-adjusted returns for investors, particularly for short-term and mid-term forecasts. We also find evidence of significant cross-market information spilling over across the oil, gold, and stock markets that also contributes to the predictability of short-term market fluctuations due to sentiment-related factors. The results highlight the predictive role of investor sentiment-related factors in improving the forecast accuracy of volatility dynamics in commodities with the potential to also yield economic gains for investors in these markets.

Keywords: Crude oil; Realized volatility forecast; Infinite hidden Markov model; Structural break; Speculation (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (26)

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Working Paper: Forecasting Oil and Gold Volatilities with Sentiment Indicators Under Structural Breaks (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:105:y:2022:i:c:s014098832100596x

DOI: 10.1016/j.eneco.2021.105751

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