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Within-regime volatility dynamics for observable- and Markov-switching score-driven models

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  • Blazsek, Szabolcs
  • Kong, Dejun
  • Shadoff, Samantha R.

Abstract

We study the novel Markov-switching (MS) Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) model, using within-regime volatility dynamics, similar to the recent observable-switching (OS) Beta-t-EGARCH model. We report in-sample results on the Standard & Poor’s 500 (S&P 500) and a random sample of 50 firms from the S&P 500 from March 1986 to July 2024. We compare the out-of-sample forecasting performances of OS-Beta-t-EGARCH and MS-Beta-t-EGARCH from May 2005 to July 2024 and confirm that OS-Beta-t-EGARCH is superior to MS-Beta-t-EGARCH.

Suggested Citation

  • Blazsek, Szabolcs & Kong, Dejun & Shadoff, Samantha R., 2025. "Within-regime volatility dynamics for observable- and Markov-switching score-driven models," Finance Research Letters, Elsevier, vol. 73(C).
    • Handle: RePEc:eee:finlet:v:73:y:2025:i:c:s154461232401660x
    DOI: 10.1016/j.frl.2024.106631
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    References listed on IDEAS

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    More about this item

    Keywords

    Dynamic conditional score (DCS); Generalized autoregressive score (GAS); Regime-switching volatility models; Standard & Poor’s 500 (S&P 500);
    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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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