Analysis of high dimensional multivariate stochastic volatility models

By Siddhartha Chib, Federico Nardari, N. Shephard. 2006

361 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/c5e2e587ffbcd15023c95873a6ac30e04b411ce4

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Citations in the corpus

Implications of oil prices shocks for the major emerging economies: A comparative analysis of BRICS (2018, background): “(1998) and Chib et al. (2002) promoted the notion of multivariate stochastic volatility into models 10 .”
Macroeconomic Uncertainty Through the Lens of Professional Forecasters (2019, neutral): “As in a standard FSV model (e.g., Pitt and Shephard 1999; Chib, Nardari, and Shephard 2006), we further assume that ut and ft are conditionally independent Gaussian random vectors.”
Volatility Transmission Models: A Survey (2006, neutral): “Among them, So et al. (1997) study stock market volatility in seven Asian countries.”
Fitting Vast Dimensional Time-Varying Covariance Models (2020, neutral): “ARTICLE HISTORY Received September 2017 Accepted October 2019 KEYWORDS Composite likelihood; Dynamic conditional correlations; Multivariate ARCH models; Volatility”
Bayesian forecasting in economics and finance: A modern review (2022, methodology): “An early application of this approach can be seen in Cottet and Smith (2003). Since electricity data are increasingly available not only at a high temporal frequency but also at a high spatial resolution, there are further examples in the literature of using priors to exploit neighbourhood structure”
Factor Multivariate Stochastic Volatility via Wishart Processes (2006, neutral): “MSV models with factor structure have been proposed by Aguilar and West (2000), Chib et al. (2006), and Han (2002).”
Multivariate Stochastic Volatility: A Review (2006, methodology): “Matrix Exponential Transformation Motivated by the requirement for the positivity of the covariance, Chiu et al. (1996) proposed a general framework for the logarithmic covariance matrix based on the matrix exponential transformation, which is well known in the mathematics literature (see, for examp”
Gaussian Process Regression Networks (2011, methodology): “In both of these regressions, the GPRN is accounting for multivariate volatility; this is the ﬁrst time a multivariate stochastic volatility model has been estimated for p > 50 (Chib et al., 2006).”
Semiparametric Bayesian Inference of Long‐Memory Stochastic Volatility Models (2004, methodology): “It is unlikely that our efficient reduced-block sampler of longmemory volatility and its use of the linear offset method of Kim et al. (1998) and Chib et al. (2002a) can address leverage effects.”
Multivariate Stochastic Volatility via Wishart Processes (2006, positive): “Mounting evidence shows that the predictive performance of many standard models is improved by relaxing the assumption of constant volatility; examples include work by Andersen, Benzoni, and Lund (2002), Chib, Nardari, and Shephard (2002), and Han (2002).”
A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations (2011, methodology): “The same test for evaluating forecast performance was used in a similar context by Bauwens and Laurent (2005) and Chib, Nardari, and Shephard (2006).”
Dynamic Factor Models for Multivariate Count Data: An Application to Stock-Market Trading Activity (2011, neutral): “…for a common dynamic market factor— akin to the common factors for multivariate stock-price intensities identified by Bauwens and Hautsch (2006) and for volatility processes across asset returns found by Diebold and Nerlove (1989), Aguilar and West (2000), and Chib, Nardari, and Shephard (2006).”
Estimating High Dimensional Covariance Matrices and its Applications (2011, background): “One advantage of observable factor models is that the model requires much fewer parameters than the latent factor models to be discussed below.”
Sparse Bayesian time-varying covariance estimation in many dimensions (2016, background): “Λij ∼ N ( 0, τ 2 ij ) independently for each i and j, where τ 2 ij ≡ τ 2 is a constant specified a priori (e.g. Pitt and Shephard 1999; Aguilar and West 2000; Chib et al. 2006; Ishihara and Omori 2017; Kastner et al. 2017).”