Bayesian estimation for a semiparametric nonlinear volatility model

Shuowen Hu; D. S. Poskitt; Xibin Zhang

doi:10.1016/j.econmod.2020.11.005

Bayesian estimation for a semiparametric nonlinear volatility model

Shuowen Hu, D. S. Poskitt, Xibin Zhang

Econometrics & Business Statistics

Research output: Contribution to journal › Article › Research › peer-review

2 Citations (Scopus)

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.

Original language	English
Pages (from-to)	361-370
Number of pages	10
Journal	Economic Modelling
Volume	98
DOIs	https://doi.org/10.1016/j.econmod.2020.11.005
Publication status	Published - May 2021

Keywords

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

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10.1016/j.econmod.2020.11.005

Cite this

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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.",

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author = "Shuowen Hu and Poskitt, {D. S.} and Xibin Zhang",

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AU - Poskitt, D. S.

AU - Zhang, Xibin

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AB - 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.

KW - Backtesting

KW - Cross-validation

KW - Nadaraya-Watson estimator

KW - Unknown error distribution

KW - Value-at-risk

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VL - 98

SP - 361

EP - 370

JO - Economic Modelling

JF - Economic Modelling

ER -

Bayesian estimation for a semiparametric nonlinear volatility model

Abstract

Keywords

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