Data cloning estimation of GARCH and COGARCH models

Marín, J. Miguel; Rodríguez-Bernal, María Teresa

doi:10.1080/00949655.2014.903948

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…According to Kinoshita (2015) with the GARCH effects, a simple approach to estimate conditional higher moments is given. GARCH models include most of the stylized facts of financial time series and they have been largely used to analyse discrete financial time series (Marin et al 2015).…”

Section: Methodsmentioning

confidence: 99%

Mean Reversion: An Investigation From Karachi Stock Exchange Sectors

Vveinhardt

Štreimikienė

Ahmed

et al. 2016

Technological and Economic Development of Economy

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This article analyses the sectors of Karachi stock exchange in order to determine if there is any presence of mean reversion phenomenon in the stock market sectors and also an attempt to determine the pace of mean reversion. To conduct this research, secondary data is collected from the State Bank Bulletin. The frequency of the data is monthly. The variables include the individual; the data was obtained from 24 sectors returns over the period of 17 years from January 1992 to June 2008. The GARCH (1, 1) model was used to find the outcomes and the effects. In the two sectors out of 24 sectors, the GARCH and ARCH effects were significant, namely, in the Jute and Banks & Investment Companies. We studied the mean reverting process in the KSE sectors over a specific time period. Since, the mean reversion varies over different time periods. Therefore, it would be a good area for future research to study the reasons, why the market reacts differently over different time periods and to determine the reasons for such variations. The paper contributes to Stock Prices returns and investment opportunities by studying the Mean Reversion Phenomenon.

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Section: Methodsmentioning

confidence: 99%

Mean Reversion: An Investigation From Karachi Stock Exchange Sectors

Vveinhardt

Štreimikienė

Ahmed

et al. 2016

Technological and Economic Development of Economy

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“…Log-likelihood function is It will be assumed that 0 has gamma prior and the joint density function of 1 and 1 is the Dirichlet probability function that are given in the equations (10) and (11) respectively. Since 0 , 1 and 1 are independent, their joint pdf is given by in eq (12) The joint posterior function of 0 , 1 and 1…”

Section: Bayesian Estimation Of the Parameters Of Garch (11) Model Wmentioning

confidence: 99%

“…This method can be applied for any kind of parametric ARCHtype models. Marín et al (2015) use the data cloning methodology to estimate the parameters of the GARCH and Continuous GARCH model. In the data cloning methodology, a Bayesian approach is used to obtain approximate maximum likelihood estimators of GARCH and continuous GARCH models avoiding numerically maximization of the pseudo-likelihood function.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Estimation of Student-t GARCH Model Using Lindley’s Approximation

Arı¹,

Alex²

2019

ECECSR

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The dependency of conditional second moments of financial time series is modelled by Generalized Autoregressive conditionally heteroscedastic (GARCH) processes. The maximum likelihood estimation (MLE) procedure is most commonly used for estimating the unknown parameters of a GARCH model. In this study, the parameters of the GARCH models with student-t innovations are discussed for estimations using the Bayesian approach. It is assumed that the parameters of the GARCH model are random variables having known prior probability density functions. Lindley's approximation will be used to estimate the Bayesian estimators since they are not in a closed form. The Bayesian estimators are derived under squared error loss function. Finally, a simulation study is performed in order to compare the ML estimates to the Bayesian ones and in addition to simulations an example is given in order to illustrate the findings. MLE's and Bayesian estimates are compared according to the expected risks in the simulation study which shows that as the sample size increases the expected risks decrease and also it is observed that Bayesian estimates have performed better than MLE's.

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“…We propose using this methodology to estimate the parameters of SV and SVM models as it has been shown to be particularly useful for complex models, as discussed in studies by authors in [34][35][36][37]. Recently, this method has been successfully used to estimate the parameters of other complex fnancial models in [38,39]. Although it is beyond the scope of this article, models have recently been developed to estimate volatility in the valuation of fnancial options using two volatility components [40,41].…”

Section: Introductionmentioning

confidence: 99%

Testing Data Cloning as the Basis of an Estimator for the Stochastic Volatility in Mean Model

Romero,

Ropero-Moriones

2023

Discrete Dynamics in Nature and Society

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Developed as a refinement of stochastic volatility (SV) models, the stochastic volatility in mean (SVM) model incorporates the latent volatility as an explanatory variable in both the mean and variance equations. It, therefore, provides a way of assessing the relationship between returns and volatility, albeit at the expense of complicating the estimation process. This study introduces a Bayesian methodology that leverages data-cloning algorithms to obtain maximum likelihood estimates for SV and SVM model parameters. Adopting this Bayesian framework allows approximate maximum likelihood estimates to be attained without the need to maximize pseudo likelihood functions. The key contribution this paper makes is that it proposes an estimator for the SVM model, one that uses Bayesian algorithms to approximate the maximum likelihood estimate with great effect. Notably, the estimates it provides yield superior outcomes than those derived from the Markov chain Monte Carlo (MCMC) method in terms of standard errors, all while being unaffected by the selection of prior distributions.

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Data cloning estimation of GARCH and COGARCH models

Cited by 8 publications

References 26 publications

Mean Reversion: An Investigation From Karachi Stock Exchange Sectors

Mean Reversion: An Investigation From Karachi Stock Exchange Sectors

Bayesian Estimation of Student-t GARCH Model Using Lindley’s Approximation

Testing Data Cloning as the Basis of an Estimator for the Stochastic Volatility in Mean Model

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