The Conditional Autoregressive Wishart Model for Multivariate Stock Market Volatility

By Vasyl Golosnoy, Bastian Gribisch, Roman Liesenfeld. 2010

162 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/40191d583c0a51e49062d6b7cdb2dc393da59da9

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

1. Dick Van Dijk
2. André Lucas
3. Pawel Janus
4. A. Opschoor
5. Xin Jin
6. John M. Maheu
7. Fangfang Wang
8. Eric Ghysels

In Finance

1. Dick Van Dijk
2. Pawel Janus
3. A. Opschoor
4. André Lucas
5. John M. Maheu
6. Xin Jin
7. Fangfang Wang
8. Eric Ghysels

Citations in the corpus

• 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.”
• Modeling Realized Covariances and Returns (2013, methodology): “1 Vector-diagonal GARCH Model 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) (26) Ht = CC ′ + aa′ rt−1r t−1 + bb′ Ht−1, (27) where rt is a k-dimensional daily return series, and r1:t−1 = {r1, .”
• A New Parametrization of Correlation Matrices (2020, unknown): “Such as those in Liu (2009), Chiriac and Voev (2011), Golosnoy et al. (2012), Bauwens et al. (2012), Noureldin et al. (2012), Hansen et al. (2014), and Gorgi et al. (2019).”
• New HEAVY Models for Fat-Tailed Realized Covariances and Returns (2017, methodology): “Second, we consider the CAW model of Golosnoy, Gribisch, and Liesenfeld (2012).”
• Modeling and Forecasting Large Realized Covariance Matrices and Portfolio Choice (2017, unknown): “Golosnoy et al. (2012) propose a Conditional Autoregressive Wishart (CAW) model.”
• ECONOMETRIC ANALYSIS OF VOLATILITY COMPONENT MODELS (2014, background): “…and Inghelbrecht (2010), Bauwens, Hafner, and Pierret (2013), Chauvet, Senyuz, and Yoldas (2010), Chen, Choudhry, and Wu (2013), Dorion (2010), Golosnoy, Gribisch, and Liesenfeld (2012), Hafner and Linton (2010), Asgharian, Hou, and Javed (2013), Maheu and McCurdy (2007), and Rangel (2011),…”
• Realized Wishart-Garch: A Score-Driven Multi-Asset Volatility Model (2016, unknown): “The model-based forecasting framework for the realized covariance matrix is the conditional autoregressive Wishart (CAW) model of Golosnoy, Gribisch, and Liesenfeld (2012) while the nonparametric forecasting method is based on the exponentially weighted moving average (EWMA) scheme.”
• Forecasting volatility and co-volatility of crude oil and gold futures: Effects of leverage, jumps, spillovers, and geopolitical risks (2020, unknown): “The model (5) and (6) with h = 1 belongs to the conditional autoregressive Wishart (CAW) model of Golosnoy, Gribisch, and Liesenfeld (2012).”