Bayesian analysis of stochastic volatility models with fat-tails and correlated errors

Jacquier, Éric; Polson, Nicholas G.; Rossi, Peter E.

doi:10.1016/j.jeconom.2003.09.001

Cited by 509 publications

(376 citation statements)

References 41 publications

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“…As a result, the leverage effect is not warranted, and hence it is difficult to interpret the leverage effect (Yu, 2005). Danielsson (1998) and Chan et al (2005) considered a multivariate extension of the model of Jacquier et al (2004). The model is given by Equations (3) and (4), together with…”

Section: Leverage Effectmentioning

confidence: 99%

Multivariate Stochastic Volatility: A Review

Asai

McAleer

2006

Econometric Reviews

301

181

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The literature on multivariate stochastic volatility (MSV) models has developed significantly over the last few years. This paper reviews the substantial literature on specification, estimation, and evaluation of MSV models. A wide range of MSV models is presented according to various categories, namely, (i) asymmetric models, (ii) factor models, (iii) time-varying correlation models, and (iv) alternative MSV specifications, including models based on the matrix exponential transformation, the Cholesky decomposition, and the

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Section: Leverage Effectmentioning

confidence: 99%

Multivariate Stochastic Volatility: A Review

Asai

McAleer

2006

Econometric Reviews

301

181

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“…Subsequently SV models with contemporaneous correlation (ρ) between t and η t has been discussed by, e.g. (Jacquier, Polson & Rossi 2004) among others. SV model with the feedback effect ρ relates the changes in volatility to the sign and magnitude of price changes which helps in pricing the options more accurately.…”

Section: Dynamic Feedback Sv Model With Common Market Factormentioning

confidence: 99%

Non-Stationary Stochastic Volatility Model for Dynamic Feedback and Skewness

Mukhoti

2014

SSRN Journal

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In this paper I present a new single factor stochastic volatility model for asset return observed in discrete time and its latent volatility. This model unifies the feedback effect and return skewness using a common factor for return and its volatility. Further, it generalizes the existing stochastic volatility framework with constant feedback to one with time varying feedback and as a consequence time varying skewness follows. However, presence of dynamic feedback effect violates the weak-stationarity assumption usually considered for the latent volatility process. The concept of bounded stationarity has been proposed in this paper to address the issue of non-stationarity. A characterization of the error distributions for returns and volatility is provided on the basis of existence of conditional moments. Finally, an application of the model has been explained using *

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“…Further comparisons were made with two SV models with Student-t innovations one without (SV(1)-t), and one with 'leverage effect' (SV(1)-t leverage), using the algorithms of Jacquier et al (1994Jacquier et al ( , 2004. Finally, we compared our IUM model with a DPM model with the same GARCH-type representations for the volatility, the SV-DPM model of Jensen and Maheu (2010), and the πDDP model of Griffin and Steel (2006).…”

Section: Empirical Examplesmentioning

confidence: 99%

Modelling the Conditional Distribution of Daily Stock Index Returns: An Alternative Bayesian Semiparametric Model

2011

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Please cite this publication as follows:Kalli, M., Damien, P. and Walker, S. (2013) Abstract This paper introduces a new family of Bayesian semi-parametric models for the conditional distribution of daily stock index returns. The proposed models capture key stylized facts of such returns, namely heavy tails, asymmetry, volatility clustering, and the 'leverage effect'. A Bayesian nonparametric prior is used to generate random density functions that are unimodal and asymmetric.

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Bayesian analysis of stochastic volatility models with fat-tails and correlated errors

Cited by 509 publications

References 41 publications

Multivariate Stochastic Volatility: A Review

Multivariate Stochastic Volatility: A Review

Non-Stationary Stochastic Volatility Model for Dynamic Feedback and Skewness

Modelling the Conditional Distribution of Daily Stock Index Returns: An Alternative Bayesian Semiparametric Model

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