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Bayesian estimation for a semiparametric nonlinear volatility model
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DOI：10.1016/j.econmod.2020.11.005.png)
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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.
Keyword:
Backtesting
Cross-validation
Nadaraya-Watson estimator
Unknown error distribution
Value-at-risk
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被引数:
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