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Estimation of the Cholesky Multivariate Stochastic Volatility Model Using Iterated Filtering

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  • Szczepocki Piotr

    (University of Lodz, Lodz, Poland)

Abstract

Aim The paper aims to propose a new estimation method for the Cholesky Multivariate Stochastic Volatility Model based on the iterated filtering algorithm (Ionides et al., 2006, 2015). Methodology The iterated filtering method is a frequentist-based technique that through multiple repetitions of the filtering process, provides a sequence of iteratively updated parameter estimates that converge towards the maximum likelihood estimate. Results The effectiveness of the proposed estimation method was shown in an empirical example in which the Cholesky Multivariate Stochastic Volatility Model was used in a study on safe-haven assets of one market index: Standard and Poor’s 500 and three safe-haven candidates: gold, Bitcoin and Ethereum. Implications and recommendations: In further research, the iterating filtering method may be used for more advanced multivariate stochastic volatility models that take into account, for example, the leverage effect (as in Ishihara et al., 2016) and heavy-tailed errors (as in Ishihara and Omori, 2012). Originality/Value The main contribution of the paper is the proposition of a new estimation method for the Cholesky Multivariate Stochastic Volatility Model based on iterated filtering algorithm This is one of the few frequentist-based statistical inference methods for multivariate stochastic volatility models.

Suggested Citation

  • Szczepocki Piotr, 2023. "Estimation of the Cholesky Multivariate Stochastic Volatility Model Using Iterated Filtering," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 27(4), pages 44-58, December.
    • Handle: RePEc:vrs:eaiada:v:27:y:2023:i:4:p:44-58:n:4
    DOI: 10.15611/eada.2023.4.04
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    References listed on IDEAS

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    1. Bhadra, Anindya & Ionides, Edward L. & Laneri, Karina & Pascual, Mercedes & Bouma, Menno & Dhiman, Ramesh C., 2011. "Malaria in Northwest India: Data Analysis via Partially Observed Stochastic Differential Equation Models Driven by Lévy Noise," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 440-451.
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    More about this item

    Keywords

    multivariate stochastic volatility; iterated filtering; particle filters; the Cholesky Multivariate Stochastic Volatility (ChMSV) Model;
    All these keywords.

    JEL classification:

    • 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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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