Bayesian estimation for a semiparametric nonlinear volatility model

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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.

Original languageEnglish
Number of pages10
JournalEconomic Modelling
Publication statusAccepted/In press - 2020


  • Backtesting
  • Cross-validation
  • Nadaraya-Watson estimator
  • Unknown error distribution
  • Value-at-risk

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