5 Stochastic volatility

Ghysels, Éric; Harvey, Andrew; Renault, Éric

doi:10.1016/s0169-7161(96)14007-4

Cited by 669 publications

(413 citation statements)

References 119 publications

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“…Examples of specifications within this framework include stochastic volatility models as in Tauchen and Pitts (1983), Taylor (1986), Melino and Turnbull (1990) and Ghysels, Harvey, and Renault (1996), stochastic conditional duration models as in Bauwens and Veredas (2004), stochastic conditional intensity models as in Bauwens and Hautsch (2006), stochastic copulas as in Hafner and Manner (2011), and non-Gaussian unobserved components time series models as in Durbin and Koopman (2000).…”

Section: State Space Modelsmentioning

confidence: 99%

“…Likelihood evaluation therefore becomes more involved for parameter-driven models, typically requiring the use of efficient simulation methods. Special cases of this class are stochastic volatility models as discussed by Tauchen and Pitts (1983) and Ghysels, Harvey, and Renault (1996), the stochastic conditional duration model of Bauwens and Veredas (2004), and the stochastic copula models of Hafner and Manner (2011).…”

Section: Introductionmentioning

confidence: 99%

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Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models

Koopman

Scharth

2012

SSRN Journal

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Section: State Space Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models

Koopman

Scharth

2012

SSRN Journal

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“…With respect to the statistical properties of SV models, Ghysels, Harvey, and Renault (1996) show that the series y t is a martingale difference, but it is not an independent sequence. In fact, the autocorrelation function (ACF) of jy t j c is given by…”

Section: Statistical Properties Of Lmsv Processesmentioning

confidence: 99%

Properties of the Sample Autocorrelations of Nonlinear Transformations in Long-Memory Stochastic Volatility Models

Pérez¹

2003

Journal of Financial Econometrics

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The autocorrelations of log-squared, squared, and absolute financial returns are often used to infer the dynamic properties of the underlying volatility. This article shows that, in the context of long-memory stochastic volatility models, these autocorrelations are smaller than the autocorrelations of the log volatility and so is the rate of decay for squared and absolute returns. Furthermore, the corresponding sample autocorrelations could have severe negative biases, making the identification of conditional heteroscedasticity and long memory a difficult task. Finally, we show that the power of some popular tests for homoscedasticity is larger when they are applied to absolute returns.keywords: absolute transformation, Box-Ljung text, conditional heteroscedasticity, log-squared transformation, Peña Rodriguez test, squared observations. It is by now rather popular to measure the volatility of financial returns by some nonlinear transformations as, for example, log-squared, squared, or absolute returns. Consequently the autocorrelations of these transformations have often been used to test for the presence of conditional heteroscedasticity and to model and estimate the volatility [see, e

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“…Heteroskedasticity is known to be commonplace and there are various techniques for modelling the variance (see e.g. Bollerslev, 1986;Ghysels et al, 1996;Kim et al, 1998). However, previously mean has either been estimated separately from variance in order to avoid problems related to infinite probability densities or computationally expensive sampling techniques have been used.…”

Section: Discussionmentioning

confidence: 99%

Hierarchical models of variance sources

2004

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In many models, variances are assumed to be constant although this assumption is often unrealistic in practice. Joint modelling of means and variances is difficult in many learning approaches, because it can lead into infinite probability densities. We show that a Bayesian variational technique which is sensitive to probability mass instead of density is able to jointly model both variances and means. We consider a model structure where a Gaussian variable, called variance node, controls the variance of another Gaussian variable. Variance nodes make it possible to build hierarchical models for both variances and means. We report experiments with artificial data which demonstrate the ability of the learning algorithm to find variance sources explaining and characterizing well the variances in the multidimensional data. Experiments with biomedical MEG data show that variance sources are present in real-world signals.

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5 Stochastic volatility

Cited by 669 publications

References 119 publications

Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models

Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models

Properties of the Sample Autocorrelations of Nonlinear Transformations in Long-Memory Stochastic Volatility Models

Hierarchical models of variance sources

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