 Power of decomposition in volatility forecasting for BitcoinsPrakash Raj, Koushik Bera and N. Selvaraju Pacific-Basin Finance Journal, 2025, vol. 93, issue C Abstract: This article aims to show the power of decomposition techniques in estimating Bitcoin returns volatility with a time series volatility model. The realized generalized autoregressive heteroscedasticity (RGARCH) model, employing high-frequency data in the form of realized measures, is integrated with empirical mode decomposition (EMD) and variational mode decomposition (VMD) to estimate volatility. The high fluctuations in Bitcoin prices suggest using jump-robust estimators. The superior forecasting accuracy of proposed models compared to RGARCH and GARCH models across various metrics underscores the utility of decomposition in the volatility modeling of Bitcoin returns. VMD reigns supreme over EMD as it preserves the estimators’ ranking. In particular, the RGARCH-VMD model estimated using jump-robust estimators, namely realized tri-power variation and realized bi-power variation, outperforms all competing models. Since the Chicago Mercantile Exchange officially offers Bitcoin options, the strong performance of our models can be valuable for option pricing and risk management. Keywords: Bitcoin; EMD; VMD; RGARCH; Jump-robust realized measures; Volatility forecasting (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:pacfin:v:93:y:2025:i:c:s0927538x25001763 DOI: 10.1016/j.pacfin.2025.102839 Access Statistics for this article Pacific-Basin Finance Journal is currently edited by K. Chan and S. Ghon Rhee More articles in Pacific-Basin Finance Journal from Elsevier |
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