Bayesian estimation for a semiparametric nonlinear volatility model

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.

Automated summary

This paper introduces a new volatility model that extends the nonstationary nonparametric volatility model by including an ARCH(1) component. The model allows errors to be independent and follow an unknown distribution. A Bayesian sampling algorithm is used to estimate the ARCH coefficient and smoothing parameters, and empirical results show that the proposed model outperforms its competitors.