Forecasting multivariate realized stock market volatility

By Gregory H. Bauer, Keith P. Vorkink. 2011

154 citations recorded by the source corpus.

Subfields: Condensed Matter Physics, Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/f4df9cc12915228296ac96aa3ceb4b99328d6c15

How researchers see this paper

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

In Condensed Matter Physics

In Economics and Econometrics

In Finance

Citations in the corpus

Bayesian semiparametric modeling of realized covariance matrices (2016, methodology): “One is to use some form of a decomposition to the matrix of realized variances and covariances and then use standard time-series models for the transformed data (Bauer & Vorkink 2011, Chiriac & Voev 2011).”
The Conditional Autoregressive Wishart Model for Multivariate Stock Market Volatility (2010, unknown): “Extensions being currently explored include CAW speci cations with Markov switching regimes allowing the parameters of the GLARMA speci cation (4) to di er across two regimes.”
Multivariate High-Frequency-Based Volatility (HEAVY) Models (2012, methodology): “There is some recent research that focuses only on modelling and forecasting the realized covariance matrix; see, for example, Voev (2008), Chiriac and Voev (2011) and Bauer and Vorkink (2011). The focus in these studies is on developing parsimonious models to forecast the realized covariance matrix”
HAR Modeling for Realized Volatility Forecasting (2012, unknown): “10 For Σ t , [17] chose the bi-power covariance of [15], but the same principle can be applied to any other covariance estimators.”
Modeling and Forecasting Large Realized Covariance Matrices and Portfolio Choice (2017, unknown): “An alternative approach relies on properties of the matrix logarithm and the matrix exponential as explained in Chiu et al. (1996) or Bauer and Vorkink (2011).”
Modeling Realized Covariances and Returns (2013, methodology): “The conditional variance of element (i, j) is Var(Σt,ij|St−1, ν) = 1 ν [S̃(2) t−1,ij + S̃t−1,iiS̃t−1,jj] (12)”
New HEAVY Models for Fat-Tailed Realized Covariances and Returns (2017, background): “Likewise, the multivariate volatility models of Chiriac and Voev (2011) and Bauer and Vorkink (2011) also focus on (multivariate) realized measures only, as does the conditional autoregressive Wishart (CAW) model of Golosnoy, Gribisch, and Liesenfeld (2012).”
Factor Models for Portfolio Selection in Large Dimensions: The Good, the Better and the Ugly (2019, background): “covariance matrices; for example, see Bauer and Vorkink (2011); Chiriac and Voev (2011); Lunde, Shephard, and Sheppard (2016); Callot, Kock, and Medeiros (2017); and Aı̈t-”
Estimating the Wishart Affine Stochastic Correlation Model using the empirical characteristic function (2014, unknown): “6 For related econometric results on logarithm and positivity in the matrix sense, see Bauer and Vorkink (2011).”
High-Dimensional Copula-Based Distributions with Mixed Frequency Data (2016, unknown): “For example, GARCH-type models such as the multivariate GARCH model of Bollerslev et al. (1988), the BEKKmodel of Engle and Kroner (1995), and the dynamic conditional correlation (DCC)model of Engle (2002) naturally fit in Eqs.”
Realized Wishart-Garch: A Score-Driven Multi-Asset Volatility Model (2016, unknown): “Chiriac and Voev (2011) and Bauer and Vorkink (2011) have proposed models for realized covariances using appropriate transformations to ensure the positive definiteness of the covariance matrix.”
Econometric Analysis of Vast Covariance Matrices Using Composite Realized Kernels and Their Application to Portfolio Choice (2016, background): “There is some recent research that focuses only on modeling and forecasting the realized covariance matrix; see, for example, Voev (2008), Chiriac and Voev (2011), and Bauer and Vorkink (2011).”
A New Parametrization of Correlation Matrices (2020, methodology): “(2012), Bauwens et al. (2012), Noureldin et al.”