Multivariate Stochastic Volatility via Wishart Processes

By Alexander Philipov, Mark E. Glickman. 2006

131 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/ead59d8c47ef832f4d91581d3cc39a6e7254ccbd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Finance

Citations in the corpus

Factor Multivariate Stochastic Volatility via Wishart Processes (2006, methodology): “Since we use the same Metropolis algorithm for drawing the factor covariance matrices as in Philipov and Glickman (2005), we point T A B L E 1 Si m ul at io n re su lt s.”
Multivariate Stochastic Volatility: A Review (2006, background): “However, Philipov and Glickman (2004) introduced time variation in the scale parameter”
Bayesian semiparametric modeling of realized covariance matrices (2016, methodology): “In Jin & Maheu (2013) the model is extensively compared to models from Gourieroux et al. (2009) and Chiriac & Voev (2011), as well as extensions of Gourieroux et al. (2009) and Philipov & Glickman (2006) to handle RCOV matrices.”
Continuous Time Wishart Process for Stochastic Risk (2006, background): “In this literature, the stochastic volatility is assumed to follow an inverse Wishart distribution with a scale parameter function of lagged volatility (see Philipov and Glickman, 2006a , for a comparison of such an approach with the DCC model).”
Modeling Realized Covariances and Returns (2013, methodology): “MSV Wishart specifications for the covariance of returns are proposed in Asai and McAleer (2009) and Philipov and Glickman (2006a).”
The structure of dynamic correlations in multivariate stochastic volatility models (2009, methodology): “As the WIC model proposed in this paper is different from the factor MSV models of Philipov and Glickman (2005, 2006), the sampling scheme estimator will also be different.”
Large Order-Invariant Bayesian VARs with Stochastic Volatility (2021, methodology): “For example, some multivariate SV models based on Wishart or inverse-Wishart processes are invariant to ordering; examples include Philipov and Glickman (2006), Asai and McAleer 3 It is worth noting that one major reason for triangularizing the system is to allow for equation by equation estimation…”
Multivariate stochastic volatility (2009, methodology): “Philipov and Glickman (2004b) estimated the parameters of the above model using the Bayesian MCMC technique. 2.5.4 Range-Based Model Tims and Mahieu (2003) proposed a range-based MSV model. As the range can be used as a measure of volatility, which is observed (or realized) when the high and low pri”