Disaggregating VIX

Stavros Degiannakis and Eleftheria Kafousaki
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Eleftheria Kafousaki: Panteion University

No 335, Working Papers from Bank of Greece

Abstract: The present study highlights the economic profits of markets’ participants, accumulated from the disaggregated forecasts of the stock market’s implied volatility, generated from an ensemble modelling architecture. We incorporate six decomposition techniques, namely, the EMD, the EEMD, the SSA, the HVD, the EWT and the VMD and four different model frameworks that of AR, HAR, HW and LSTM, which are tested against a trading strategy. We diverge from quantifying forecast accuracy solely on statistical loss functions and report the cumulative returns of short or long exposure on roll adjusted VIX futures. The findings show that decomposing a time series into its intrinsic modes prior to modelling and forecasting, can result in generating forecast gains that are translated into improved profits for trading horizons of 1 to 22 days ahead. Important trading implications are drawn from these results.

Keywords: decomposition techniques; implied volatility forecasting; ensemble learning; objective-based evaluation criteria; VIX futures (search for similar items in EconPapers)
JEL-codes: C22 C53 C61 G11 G17 (search for similar items in EconPapers)
Pages: 68
Date: 2025-01
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Journal Article: Disaggregating VIX (2025)
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