Univariate and multivariate stochastic volatility
Simple techniques for likelihood analysis of univariate and multivariate stable distributions: with extensions to multivariate stochastic volatility and dynamic factor models
... of multivariate stable Paretian ...for multivariate distributions is found quite simple to implement and perform well, we generalize the stable Paretian distributions in two important ...of ...
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Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models
... of volatility shocks. These approximations lead to multivariate Gaussian densities which are used as proposal densities to draw volatility blocks within an AR-MH ...
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Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models
... of volatility shocks. These approximations lead to multivariate Gaussian densities which are used as proposal densities to draw volatility blocks within an AR-MH ...
24
Simple techniques for likelihood analysis of univariate and multivariate stable distributions: with extensions to multivariate stochastic volatility and dynamic factor models
... In the case of general (non-symmetric) multivariate stable distributions the samples. , so estimates , 0 can be obtained along with and.[r] ...
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"Multivariate stochastic volatility"
... the multivariate modeling of conditional volatility of financial time series within the framework of stochastic ...of multivariate stochastic volatility models have emerged, one ...
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Multivariate Stochastic Volatility
... A univariate discrete time SV model with the leverage effect was first proposed by Harvey and Shephard (1996), although Wiggins (1987) and Chesney and Scott (1989), among others, considered a continuous time model ...
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Asymmetric Multivariate Stochastic Volatility
... asymmetric multivariate stochastic volatility (SV) models, namely: (i) SV with leverage (SV-L) model, which is based on the negative correlation between the innovations in the returns and ...
34
Multivariate Stochastic Volatility: A Review
... latent volatility and predictive distributions for volatility (see Jacquier et ...basic univariate SV include Jacquier et ...given multivariate density (the posterior density in Bayesian ...
31
Multivariate Stochastic Volatility with Co-Heteroscedasticity
... modelling multivariate SV uses Wishart or inverted Wishart processes (see, for example, Uhlig (1997), Philipov and Glickman (2006), Casarin and Sartore (2007), Asai and McAleer (2009), Fox and West (2011), ...
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Bayesian semiparametric multivariate stochastic volatility with application
... Cholesky-type multivariate stochastic volatility estimation framework, in which we let the innovation vector follow a Dirichlet process mixture (DPM), thus enabling us to model highly flexible return ...
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Bayesian semiparametric multivariate stochastic volatility with application
... Cholesky-type multivariate stochastic volatility estimation framework, in which we let the innovation vector follow a Dirichlet process mixture (DPM), thus enabling us to model highly flexible return ...
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Bayesian Testing for Asset Volatility Persistence on Multivariate Stochastic Volatility Models
... the univariate SV models, Zhang, Li and Zhang [13] showed that the deci- sional Bayesian approach by Li and Yu [12] can achieve better finite-sample behaviors than Bayes ...return volatility persistence on ...
7
Selection of Multivariate Stochastic Volatility Models via Bayesian Stochastic Search
... a univariate model with m explanatory variables involves comparing 2 m competing ...MCMC stochastic search algorithm in univariate regression framework that greatly reduces the amount of computation, ...
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Multivariate Stochastic Volatility Estimation with Sparse Grid Integration
... a univariate quadrature rule, Q l ( ) 1 , among many al- ternatives such as trapezoidal rule, rectangle rule, Simpson’s rule, Clenshaw-Curtis and Gaussian quadrature rules including but not limited to ...
14
Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison
... ∼ iid N (0, 1), with h 0 = 0. This model was proposed by Jacquier et al. (1995, 1999). The first component in the return equation has a smaller number of factors that capture the information relevant to the pricing of all ...
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Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison
... ∼ iid N (0, 1), with h 0 = 0. This model was proposed by Jacquier et al. (1995, 1999). The first component in the return equation has a smaller number of factors that capture the information relevant to the pricing of all ...
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Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison
... A variety of estimation methods have been proposed to estimate the SV models. Less effi- cient methods include GMM (Melino and Turnbull, 1990 and Andersen and Sorensen, 1996), the quasi maximum likelihood method (Harvey, ...
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Stochastic Volatility: Univariate and Multivariate Extensions
... Corresponding Author: Éric Jacquier, Finance Department, Fulton Hall 224B, Boston College, Chestnut Hill, MA 02167, USA Tel: 617 552-2943 Fax: 617 552-0431 email: [email protected][r] ...
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On the Univariate Representation of Multivariate Volatility Models with Common Factors
... of multivariate models. We have not covered stochastic volatility models, or alternative multivariate models (GO-GARCH, ...in multivariate weak GARCH ...
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Multivariate Stochastic Volatility with Co-Heteroscedasticity
... ABSTRACT This paper develops a new methodology that decomposes shocks into homoscedastic and het- eroscedastic components. This specification implies there exist linear combinations of heteroscedas- tic variables that ...
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