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Estimation and application of semiparametric stochastic volatility models based on kernel density estimation and hidden Markov models

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  • Hong‐Xia Hao
  • Jin‐Guan Lin
  • Xing‐Fang Huang
  • Hong‐Xia Wang
  • Yan‐Yong Zhao

Abstract

Discrete‐time stochastic volatility models play a key role in the analysis of financial time series. However, the parametric assumption of conditional distribution for asset returns, given the volatility, has been questioned. When the conditional distribution is unknown and unspecified, in this paper, a maximum‐likelihood estimation approach for the semiparametric stochastic volatility models is proposed based on kernel density estimation and hidden Markov models. Several numerical studies are conducted to evaluate the finite sample performance of the proposed estimation method. Implementation on empirical studies also illustrates the validity of the proposed method in practice.

Suggested Citation

  • Hong‐Xia Hao & Jin‐Guan Lin & Xing‐Fang Huang & Hong‐Xia Wang & Yan‐Yong Zhao, 2018. "Estimation and application of semiparametric stochastic volatility models based on kernel density estimation and hidden Markov models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 34(3), pages 355-375, May.
    • Handle: RePEc:wly:apsmbi:v:34:y:2018:i:3:p:355-375
    DOI: 10.1002/asmb.2305
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