Efficient method of moments (EMM) is used to fit the standard stochastic volatility model of various extensions to several daily financial time series. EMM matches to the score of the model determined by data analysis called the score generator. Discrepancies reveal characteristics of data that stochastic volatility models cannot approximate. The two score generators employed here are ‘semiparametric ARCH’ and ‘nonlinear nonparametric’. With the first, the standard model is rejected, although some extensions are accepted. With the second, all versions are rejected. The extensions required for an adequate fit are so elaborate that nonparametric specifications are probably more convenient
One- and two-factor stochastic volatility models are assessed over three sets of stock returns data:...
Publicado además en: Recent developments in Time Series, 2003, vol. 2, ISBN13: 9781840649512, pp....
The main concern of financial time series analysis is how to forecast future values of financialvari...
Efficient method of moments (EMM) is used to fit the standard stochastic volatility model and variou...
We consider Taylor's stochastic volatility model (SVM) when the innovations of the hidden log-volati...
Thesis (Ph. D.)--University of Washington, 2006.Academic researchers and investment institutions hav...
Estimation of stochastic volatility (SV) models is a formidable task because the presence of the lat...
Two competing analytical approaches, namely, the generalized method of moments (GMM) and quasi-maxim...
We propose a nonparametric method to determine the functional form of the noise density in discrete...
Although stochastic volatility (SV) models have an intuitive appeal, their empirical application has...
Gallant and Tauchen (1996) describe an estimation technique, known as Efficient Method of Moments (...
We develop and implement a new method for maximum likelihood estimation in closed-form of stochastic...
This paper proposes a procedure to test for the correct specification of the functional form of the ...
In this paper we present an exact maximum likelihood treatment for the estimation of a Stochastic Vo...
While the stochastic volatility (SV) generalization has been shown to improve the explanatory power ...
One- and two-factor stochastic volatility models are assessed over three sets of stock returns data:...
Publicado además en: Recent developments in Time Series, 2003, vol. 2, ISBN13: 9781840649512, pp....
The main concern of financial time series analysis is how to forecast future values of financialvari...
Efficient method of moments (EMM) is used to fit the standard stochastic volatility model and variou...
We consider Taylor's stochastic volatility model (SVM) when the innovations of the hidden log-volati...
Thesis (Ph. D.)--University of Washington, 2006.Academic researchers and investment institutions hav...
Estimation of stochastic volatility (SV) models is a formidable task because the presence of the lat...
Two competing analytical approaches, namely, the generalized method of moments (GMM) and quasi-maxim...
We propose a nonparametric method to determine the functional form of the noise density in discrete...
Although stochastic volatility (SV) models have an intuitive appeal, their empirical application has...
Gallant and Tauchen (1996) describe an estimation technique, known as Efficient Method of Moments (...
We develop and implement a new method for maximum likelihood estimation in closed-form of stochastic...
This paper proposes a procedure to test for the correct specification of the functional form of the ...
In this paper we present an exact maximum likelihood treatment for the estimation of a Stochastic Vo...
While the stochastic volatility (SV) generalization has been shown to improve the explanatory power ...
One- and two-factor stochastic volatility models are assessed over three sets of stock returns data:...
Publicado además en: Recent developments in Time Series, 2003, vol. 2, ISBN13: 9781840649512, pp....
The main concern of financial time series analysis is how to forecast future values of financialvari...