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Dynamic Adaptive Mixture Models with an Application to Volatility and Risk

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  • Leopoldo Catania

Abstract

In this paper we propose a new class of dynamic mixture models (DAMMs) being able to sequentially adapt the mixture components as well as the mixture composition using information coming from the data. The information driven nature of the proposed class of models allows to exactly compute the full likelihood and to avoid computer intensive simulation schemes. Specific models for financial data are developed starting from the general specification. These models nest many specifications already available in the literature. The properties of the new class of models are discussed through the paper and a large-scale application in quantitative risk management using U.S. equity data is reported.

Suggested Citation

  • Leopoldo Catania, 2021. "Dynamic Adaptive Mixture Models with an Application to Volatility and Risk," Journal of Financial Econometrics, Oxford University Press, vol. 19(4), pages 531-564.
    • Handle: RePEc:oup:jfinec:v:19:y:2021:i:4:p:531-564.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbz018
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    Citations

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    Cited by:

    1. Tingguo Zheng & Hongyin Zhang & Shiqi Ye, 2024. "Monetary Policies on Green Financial Markets: Evidence from a Multi-Moment Connectedness Network," Papers 2405.02575, arXiv.org, revised Oct 2024.
    2. Custodio João, Igor & Lucas, André & Schaumburg, Julia & Schwaab, Bernd, 2023. "Dynamic clustering of multivariate panel data," Journal of Econometrics, Elsevier, vol. 237(2).
    3. Alexander Georges Gretener & Matthias Neuenkirch & Dennis Umlandt, 2022. "Dynamic Mixture Vector Autoregressions with Score-Driven Weights," Working Paper Series 2022-02, University of Trier, Research Group Quantitative Finance and Risk Analysis.
    4. Blasques, Francisco & van Brummelen, Janneke & Gorgi, Paolo & Koopman, Siem Jan, 2024. "Maximum Likelihood Estimation for Non-Stationary Location Models with Mixture of Normal Distributions," Journal of Econometrics, Elsevier, vol. 238(1).
    5. Zheng, Tingguo & Zhang, Hongyin & Ye, Shiqi, 2024. "Monetary policies on green financial markets: Evidence from a multi-moment connectedness network," Energy Economics, Elsevier, vol. 136(C).
    6. Igor Custodio João & Julia Schaumburg & André Lucas & Bernd Schwaab, 2024. "Dynamic Nonparametric Clustering of Multivariate Panel Data," Journal of Financial Econometrics, Oxford University Press, vol. 22(2), pages 335-374.
    7. Igor Custodio João & Andre Lucas & Julia Schaumburg, 2021. "Clustering Dynamics and Persistence for Financial Multivariate Panel Data," Tinbergen Institute Discussion Papers 21-040/III, Tinbergen Institute.
    8. Andrew Harvey & Dario Palumbo, 2023. "Regime switching models for circular and linear time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(4), pages 374-392, July.
    9. Blasques, Francisco & van Brummelen, Janneke & Koopman, Siem Jan & Lucas, André, 2022. "Maximum likelihood estimation for score-driven models," Journal of Econometrics, Elsevier, vol. 227(2), pages 325-346.
    10. Igor Custodio João, 2024. "Testing for Clustering Under Switching," Tinbergen Institute Discussion Papers 24-052/III, Tinbergen Institute.

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