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Cryptocurrency policy uncertainty and gold return forecasting: A dynamic Occam's window approach

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  • Shang, Yue
  • Wei, Yu
  • Chen, Yongfei

Abstract

The inherent relationship between gold and cryptocurrency has been verified for a long time. However, no research has explored the possible predictive ability of cryptocurrency market information on the gold market returns. Using a newly developed cryptocurrency policy uncertainty index (UCRY Policy) and an efficient forecasting method, named Dynamic Occam's Window (DOW), this paper identifies and compares the predictive power of UCRY Policy with many traditional predictors for the gold market. Our empirical results show that UCRY Policy does have good predictive power in forecasting weekly gold returns, and it is superior to many commonly used predictors throughout a data sample from 2014 to 2022. Moreover, the DOW method with various thresholds can outperform dynamic model averaging/selection (DMA/DMS) and many other conventional econometric models in forecasting weekly gold returns.

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  • Shang, Yue & Wei, Yu & Chen, Yongfei, 2022. "Cryptocurrency policy uncertainty and gold return forecasting: A dynamic Occam's window approach," Finance Research Letters, Elsevier, vol. 50(C).
    • Handle: RePEc:eee:finlet:v:50:y:2022:i:c:s1544612322004482
    DOI: 10.1016/j.frl.2022.103251
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    Cited by:

    1. Wei, Yu & Wang, Yizhi & Lucey, Brian M. & Vigne, Samuel A., 2023. "Cryptocurrency uncertainty and volatility forecasting of precious metal futures markets," Journal of Commodity Markets, Elsevier, vol. 29(C).
    2. Mercik, Aleksander & Słoński, Tomasz & Karaś, Marta, 2024. "Understanding crypto-asset exposure: An investigation of its impact on performance and stock sensitivity among listed companies," International Review of Financial Analysis, Elsevier, vol. 92(C).
    3. Yousaf, Imran & Goodell, John W., 2023. "Linkages between CBDC and cryptocurrency uncertainties, and digital payment stocks," Finance Research Letters, Elsevier, vol. 54(C).

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    More about this item

    Keywords

    Gold return forecast; Cryptocurrency uncertainty; Dynamic model averaging; Dynamic Occam's window;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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