Publication: Data cloning estimation for asymmetric stochastic volatility models
Loading...
Identifiers
Publication date
2020-06-10
Defense date
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
Taylor & Francis
Impact
Export
Abstract
The paper proposes the use of data cloning (DC) to the estimation of general univariate asymmetric stochastic volatility (ASV) models with flexible distributions for the standardized returns. These models are able to capture the asymmetric volatility, the leptokurtosis and the skewness of the distribution of returns. Data cloning is a general technique to compute maximum likelihood estimators, along with their asymptotic variances, by means of a Markov chain Monte Carlo (MCMC) methodology. The main aim of this paper is to illustrate how easily general univariate ASV models can be estimated and consequently studied via data cloning. Changes of specifications, priors and sampling error distributions are done with minor modifications of the code. Using an intensive simulation study, the finite sample properties of the estimators of the parameters are evaluated and compared to those of a benchmark estimator that is also user-friendly. The results show that the proposed estimator is computationally efficient, and can be an effective alternative to the existing estimation methods applied to ASV models. Finally, we use data cloning to estimate the parameters of general ASV models and forecast the one-step-ahead volatility of S&P 500 and FTSE-100 daily returns.
Description
Keywords
Asymmetric volatility, Data cloning, Non-gaussian nonlinear time series models, Skewed and heavy-tailed distributions
Bibliographic citation
de Zea Bermudez, P., Marín, J. M., & Veiga, H. (2020). Data cloning estimation for asymmetric stochastic volatility models. Econometric Reviews, 39 (10), pp. 1057-1074.