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Bayesian estimation for a semiparametric nonlinear volatility model

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  • Hu, Shuowen
  • Poskitt, D.S.
  • Zhang, Xibin

Abstract

This paper presents a new volatility model which extends the nonstationary nonparametric volatility model of Han and Zhang (2012) by including an ARCH(1) component. This model also allows the errors to be independent and follow an unknown distribution. A Bayesian sampling algorithm is presented to estimate the ARCH coefficient and smoothing parameters. Empirical results show that the proposed model outperforms its competitors under several evaluation criteria.

Suggested Citation

  • Hu, Shuowen & Poskitt, D.S. & Zhang, Xibin, 2021. "Bayesian estimation for a semiparametric nonlinear volatility model," Economic Modelling, Elsevier, vol. 98(C), pages 361-370.
    • Handle: RePEc:eee:ecmode:v:98:y:2021:i:c:p:361-370
    DOI: 10.1016/j.econmod.2020.11.005
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    Cited by:

    1. Bucci, Andrea & Palomba, Giulio & Rossi, Eduardo, 2023. "The role of uncertainty in forecasting volatility comovements across stock markets," Economic Modelling, Elsevier, vol. 125(C).
    2. Wang, Nianling & Lou, Zhusheng, 2023. "Sequential Bayesian analysis for semiparametric stochastic volatility model with applications," Economic Modelling, Elsevier, vol. 123(C).

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    More about this item

    Keywords

    Backtesting; Cross-validation; Nadaraya-Watson estimator; Unknown error distribution; Value-at-risk;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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