The Wishart Autoregressive Process of Multivariate Stochastic Volatility

By Christian Gouriéroux, Joann Jasiak, R. Sufana. 2009

318 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/81ee94f80ccedc420922f1f2ad93d7435266d73a

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Citations in the corpus

A multivariate Ornstein-Uhlenbeck type stochastic volatility model (2009, methodology): “Among these, the Wishart model proposed by Gouriéroux (2006) and Gouriéroux, Jasiak, and Sufana (2009), is at least to our knowledge, the only model that also specifies the dynamics of the stochastic volatility process directly in the space of positive semidefinite matrices.”
Modelling and Forecasting Multivariate Realized Volatility (2011, methodology): “The Wishart autoregressive (WAR) model of Gourieroux et al. (2009), and the model of Bauer and Vorkink (2007), who employ the matrix log transformation to guarantee positive definiteness of the forecast, are among the few proposed approaches for the dynamics of the whole realized covariance matrix.”
A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations (2011, background): “The second class comprises parameter-driven models, including the multivariate stochastic volatility models of Chib, Nardari, and Shephard (2006) and Gourieroux, Jasiak, and Sufana (2009).”
Realized Wishart-Garch: A Score-Driven Multi-Asset Volatility Model (2016, unknown): “The importance of tick-by-tick data cleaning is highlighted by Hansen and Lunde (2006) and Barndorff-Nielsen et al.”
Correlation Risk and Optimal Portfolio Choice (2010, unknown): “the volatility and an autonomous specification for the correlation process, as in Driessen, Maenhout, and Vilkov (2007) .”
Bayesian semiparametric modeling of realized covariance matrices (2016, methodology): “Gourieroux et al. (2009), Bonato et al. (2008), Golosnoy et al. (2012), Jin & Maheu (2013) and Bauwens et al. (2014) develop dynamic Wishart and noncentral-Wishart models for RCOV.”
Dynamic Covariance Models for Multivariate Financial Time Series (2013, background): “Stochastic Volatility models (Harvey et al., 1994; Chib et al., 2009; Gouriéroux et al., 2009; Philipov & Glickman, 2006).”
The Conditional Autoregressive Wishart Model for Multivariate Stock Market Volatility (2010, unknown): “The speci cation proposed by Gourieroux et al. (2009) extends the Wishart distribution of the sample covariance for i.i.d. multivariate Gaussian random variables by allowing the multivariate Gaussian random variables to be serially correlated.”
On the Forecasting Accuracy of Multivariate GARCH Models (2010, methodology): “It would be interesting to compare the performances of this class of volatility models with other approaches such as heterogeneous autoregression based on historical values of ex-post measures of the conditional variance as in the model proposed by Corsi (2009), models that combine ARMA structures f”
Modeling Realized Covariances and Returns (2013, methodology): “Ding and Engle (2001) introduce the vector-diagonal GARCH (VD-GARCH-t) model to which we add Student-t innovations as follows rt|r1:t−1 ∼ t(0, Ht, ζ) (28) Ht = CC ′ + aa′ rt−1r′t−1 + bb′ Ht−1, (29) where rt is a k-dimensional daily return series, and r1:t−1 = {r1, . . . , rt−1}.”
Gaussian Process Regression Networks (2011, methodology): “We compare to full BEKK MGARCH (Engle & Kroner, 1995), the generalised Wishart process (Wilson & Ghahramani, 2010a), and the original Wishart process (Bru, 1991; Gouriéroux et al., 2009).”
Derivative Pricing With Wishart Multivariate Stochastic Volatility (2010, methodology): “It can be solved by considering WAR factor models or by introducing appropriate parameter restrictions (Bonato, Caporin, and Ranaldo 2008; Gourieroux, Jasiak, and Sufana 2009).”
Multivariate stochastic volatility (2009, methodology): “Gourieroux et al. (2004) and Gourieroux (2006) used an alternative approach and derived a Wishart autoregressive process.”
Estimation of Affine Term Structure Models with Spanned or Unspanned Stochastic Volatility (2014, methodology): “1In this paper, we do not consider the class of non-Gaussian ATSMs built from the non-central Wishart process of Gouriéroux, Jasiak, and Sufana(2009).”
New HEAVY Models for Fat-Tailed Realized Covariances and Returns (2017, methodology): “Typically, one assumes a Wishart distribution for RCt ; see, for example, Noureldin, Shephard, and Sheppard (2012); Gourieroux, Jasiak, and Sufana (2009); and Golosnoy, Gribisch, and Liesenfeld (2012).”