Modelling and Forecasting Multivariate Realized Volatility

By Roxana Chiriac, Valeri Voev. 2011

306 citations recorded by the source corpus.

Subfields: Finance

View Paper: https://www.semanticscholar.org/paper/16e3c383d4165fe0db7ad202fc649e04d3730e36

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Citations in the corpus

Factor Models for Portfolio Selection in Large Dimensions: The Good, the Better and the Ugly (2019, background): “In contrast, an AFM only assumes that Ru is a matrix with bounded L 1 or L(2) norm; for example, see Connor and Korajczyk (1993); Bai and Ng (2002); Fan, Fan, and Lv (2008); and the references therein.”
New HEAVY Models for Fat-Tailed Realized Covariances and Returns (2017, methodology): “Following Chiriac and Voev (2011), we consider global minimum variance portfolios (GMVP), motivated by the meanvariance optimization setting of Markowitz (1952).”
Realized Wishart-Garch: A Score-Driven Multi-Asset Volatility Model (2016, unknown): “The empirical study is based on consolidated trades (transaction prices) extracted from the Trade and Quote (TAQ) database through the Wharton Research Data Services (WRDS) system. The time stamp precision is one second. The sample period spans ten years, from January 2, 2001 to December 31, 2010, w”
Can Internet search queries help to predict stock market volatility (2016, unknown): “Ghysels (2011) and Chiriac and Voev (2011). The HAR model augmented with lagged search queries reads as follows:”
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).”
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).”
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).”
A blocking and regularization approach to high dimensional realized covariance estimation (2012, unknown): “To apply this procedure in a multivariate setting and to ensure positive definiteness of the resulting matrix, we use the multivariate adjustment suggested in Chiriac and Voev (2010), where ω1 and ω2 are the cross-sectional average scaling factor of the variances.”
High-Dimensional Copula-Based Distributions with Mixed Frequency Data (2016, unknown): “Research on forecasting models for multivariate covariance matrices with low-frequency data is pervasive, see Andersen, et al. (2006) for a review, and research on forecasting models using high frequency data is growing, e.g. Chiriac and Voev (2011), Noureldin, et al. (2012) among others.”
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).”
Do High-Frequency Data Improve High-Dimensional Portfolio Allocations? (2013, methodology): “We follow the latter strategy, which is in the spirit of Chiriac and Voev (2011) applying HAR dynamics to the Cholesky factors of realized covariance estimates.”
Forecasting Volatility Using High-Frequency Data (2011, methodology): “5 Nonlinearity The ARFIMA modeling framework for realized variance has also be modified in various non-linear directions. Maheu and McCurdy (2002) develop a Markov switching model for the integrated variance which they apply to ten years of realized variances for DM/$ exchange rates.”