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Combining Sharp and Smooth Transitions in Volatility Dynamics: a Fuzzy Regime Approach

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Abstract

Volatility in financial markets is characterized by alternating persistent turmoil and quiet periods, but also by a slowly-varying average level. This slow moving component keeps open the question of whether some of its features are better represented as abrupt or smooth changes between local averages of volatility. We provide a new class of models with a set of parameters subject to abrupt changes in regime (Markov Switching -- MS) and another set subject to smooth transition (ST) changes. These models capture the possibility that regimes may overlap with one another ( fuzzy ). The empirical application is carried out on the volatility of four US indices. It shows that the flexibility of the new model allows for a better overall performance over either MS or ST, and provides a Local Average Volatility measure as a parametric estimation of the low frequency component.

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  • Giampiero M. Gallo & Edoardo Otranto, 2017. "Combining Sharp and Smooth Transitions in Volatility Dynamics: a Fuzzy Regime Approach," Econometrics Working Papers Archive 2017_05, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    • Handle: RePEc:fir:econom:wp2017_05
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    Cited by:

    1. Cipollini, Fabrizio & Gallo, Giampiero M. & Otranto, Edoardo, 2021. "Realized volatility forecasting: Robustness to measurement errors," International Journal of Forecasting, Elsevier, vol. 37(1), pages 44-57.
    2. Abdelhakim Aknouche & Bader Almohaimeed & Stefanos Dimitrakopoulos, 2022. "Periodic autoregressive conditional duration," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(1), pages 5-29, January.
    3. Luca Scaffidi Domianello & Giampiero M. Gallo & Edoardo Otranto, 2024. "Smooth and Abrupt Dynamics in Financial Volatility: The MS‐MEM‐MIDAS," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(1), pages 21-43, February.
    4. Bauwens, Luc & Otranto, Edoardo, 2020. "Nonlinearities and regimes in conditional correlations with different dynamics," Journal of Econometrics, Elsevier, vol. 217(2), pages 496-522.
    5. Demetrio Lacava & Luca Scaffidi Domianello, 2021. "The Incidence of Spillover Effects during the Unconventional Monetary Policies Era," JRFM, MDPI, vol. 14(6), pages 1-18, May.
    6. Aknouche, Abdelhakim & Almohaimeed, Bader & Dimitrakopoulos, Stefanos, 2020. "Periodic autoregressive conditional duration," MPRA Paper 101696, University Library of Munich, Germany, revised 08 Jul 2020.
    7. Pietro Coretto & Michele La Rocca & Giuseppe Storti, 2020. "Improving Many Volatility Forecasts Using Cross-Sectional Volatility Clusters," JRFM, MDPI, vol. 13(4), pages 1-23, March.

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    More about this item

    Keywords

    Volatility modeling; Volatility forecasting; Multiplicative Error Model; Markov Switching; Smooth Transition; Common Trend;
    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
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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