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Disaggregating VIX

Author

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  • Stavros Degiannakis

    (Bank of Greece and Panteion University)

  • Eleftheria Kafousaki

    (Panteion University)

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.

Suggested Citation

  • Stavros Degiannakis & Eleftheria Kafousaki, 2025. "Disaggregating VIX," Working Papers 335, Bank of Greece.
    • Handle: RePEc:bog:wpaper:335
    DOI: 10.52903/wp2025335
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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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