Data cloning estimation for asymmetric stochastic volatility models

Patrícia de Zea Bermudez
Authors registered in the RePEc Author Service: Helena Veiga

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

Abstract: The paper proposes the use of data cloning (DC) to the estimation of general 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 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 robust, and can be an effective alternative to the exiting 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.

Keywords: Asymmetric; Volatility; Data; Cloning; Non-Gaussian; Nonlinear; Time; Series; Models; Skewed; and; Heavy-Tailed; distributions (search for similar items in EconPapers)
Date: 2019-03-19
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-ore and nep-rmg
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Journal Article: Data cloning estimation for asymmetric stochastic volatility models (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:28214

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