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Score-driven multi-regime Markov-switching EGARCH: empirical evidence using the Meixner distribution

Author

Listed:
  • Blazsek Szabolcs

    (School of Business, Universidad Francisco Marroquín, Guatemala City, 01010, Guatemala)

  • Haddad Michel Ferreira Cardia

    (School of Business and Management, Queen Mary University of London, London, E1 4NS, UK)

Abstract

In this paper, statistical and volatility forecasting performances of the non-path-dependent score-driven multi-regime Markov-switching (MS) exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models are explored. Three contributions to the existing literature are provided. First, we use all relevant score-driven distributions from the literature - namely, the Student’s t-distribution, general error distribution (GED), skewed generalized t-distribution (Skew-Gen-t), exponential generalized beta distribution of the second kind (EGB2), and normal-inverse Gaussian (NIG) distribution. We then introduce the score-driven Meixner (MXN) distribution-based EGARCH model to the literature on score-driven models. Second, proving the sufficient conditions of the asymptotic properties of the maximum likelihood (ML) estimator for non-path-dependent score-driven MS-EGARCH models is an unsolved problem. We provide a partial solution to that problem by proving necessary conditions for the asymptotic theory of the ML estimator. Third, to the best of our knowledge, this work includes the largest number of international stock indices from the G20 countries in the literature, covering the period of 2000–2022. We provide a discussion on the major events which caused common or non-common switching to the high-volatility regime for the G20 countries. The statistical performance and volatility forecasting results support the adoption of score-driven MS-EGARCH for the G20 countries.

Suggested Citation

  • Blazsek Szabolcs & Haddad Michel Ferreira Cardia, 2023. "Score-driven multi-regime Markov-switching EGARCH: empirical evidence using the Meixner distribution," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 27(4), pages 589-634, September.
    • Handle: RePEc:bpj:sndecm:v:27:y:2023:i:4:p:589-634:n:6
    DOI: 10.1515/snde-2021-0101
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    More about this item

    Keywords

    dynamic conditional score; exponential generalized autoregressive conditional heteroskedasticity (EGARCH); generalized autoregressive score; Markov regime-switching;
    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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