EconPapers    
Economics at your fingertips  
 

Data cloning estimation for asymmetric stochastic volatility models

P. de Zea Bermudez, J. Miguel Marín and Helena Veiga ()

Econometric Reviews, 2020, vol. 39, issue 10, 1057-1074

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.

Date: 2020
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://hdl.handle.net/10.1080/07474938.2020.1770997 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Data cloning estimation for asymmetric stochastic volatility models (2019) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:emetrv:v:39:y:2020:i:10:p:1057-1074

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/LECR20

DOI: 10.1080/07474938.2020.1770997

Access Statistics for this article

Econometric Reviews is currently edited by Dr. Essie Maasoumi

More articles in Econometric Reviews from Taylor & Francis Journals
Bibliographic data for series maintained by ().

 
Page updated 2022-01-11
Handle: RePEc:taf:emetrv:v:39:y:2020:i:10:p:1057-1074