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Estimation and application of semiparametric stochastic volatility models based on kernel density estimation and hidden Markov models
Author(s) -
Hao HongXia,
Lin JinGuan,
Huang XingFang,
Wang HongXia,
Zhao YanYong
Publication year - 2018
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2305
Subject(s) - stochastic volatility , econometrics , kernel density estimation , conditional probability distribution , conditional variance , mathematics , parametric statistics , hidden markov model , volatility (finance) , semiparametric model , computer science , statistics , autoregressive conditional heteroskedasticity , nonparametric statistics , artificial intelligence , estimator
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.