An integrated framework for visualizing and forecasting realized covariance matrices

2020 ◽  
Author(s):  
Hideto Shigemoto ◽  
Takayuki Morimoto
Keyword(s):  
Covariance Matrices ◽  
Integrated Framework ◽  
Realized Covariance

scholarly journals Forecasting Multivariate Volatility using the VARFIMA Model on Realized Covariance Cholesky Factors

2011 ◽  
Vol 231 (1) ◽  
Author(s):  
Roxana Halbleib ◽  
Valeri Voev
Keyword(s):  
Evaluation Criteria ◽  
Forecast Accuracy ◽  
Realized Volatility ◽  
Covariance Matrices ◽  
Risk Averse ◽  
Return Distribution ◽  
Volatility Model ◽  
Realized Covariance ◽  
Dominance Tests ◽  
Multivariate Volatility

SummaryThis paper analyzes the forecast accuracy of the multivariate realized volatility model introduced by Chiriac and Voev (2010), subject to different degrees of model parametrization and economic evaluation criteria. Bymodelling the Cholesky factors of the covariance matrices, the model generates positive definite, but biased covariance forecasts. In this paper, we provide empirical evidence that parsimonious versions of the model generate the best covariance forecasts in the absence of bias correction. Moreover, we show by means of stochastic dominance tests that any risk averse investor, regardless of the type of utility function or return distribution, would be better-off from using this model than from using some standard approaches.

scholarly journals Modeling and Forecasting Large Realized Covariance Matrices and Portfolio Choice

2016 ◽  
Vol 32 (1) ◽  
pp. 140-158 ◽  
Author(s):  
Laurent A. F. Callot ◽  
Anders B. Kock ◽  
Marcelo C. Medeiros
Keyword(s):  
Portfolio Choice ◽  
Covariance Matrices ◽  
Modeling And Forecasting ◽  
Realized Covariance
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