An integrated framework for visualizing and forecasting realized covariance matrices
type: Item
instance of: scholarly article
title: An integrated framework for visualizing and forecasting realized covariance matrices
zbMATH Open document ID: 1477.62303
author: Hideto Shigemoto
author: Takayuki Morimoto
published in: Japanese Journal of Statistics and Data Science
publication date: 2021-12-17T00:00:00Z
Mathematics Subject Classification ID: 62P05
Mathematics Subject Classification ID: 62M10
Mathematics Subject Classification ID: 62J07
Mathematics Subject Classification ID: 62H12
Mathematics Subject Classification ID: 91G10
zbMATH DE Number: 7447496
zbMATH Keywords: high-frequency data
zbMATH Keywords: multivariate realized kernel
zbMATH Keywords: graphical Lasso
zbMATH Keywords: realized covariance
zbMATH Keywords: conditional autoregressive Wishart (CAW) model
zbMATH Keywords: Stein loss
zbMATH Keywords: model-confidence-set (MCS)
zbMATH Keywords: optimal portfolio
MaRDI profile type: Publication
full work available at URL: s42081-020-00100-0
OpenAlex ID: W3109127262
cites work: Modeling and forecasting realized covariance matrices with accounting for leverage
cites work: Multivariate realised kernels: consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading
cites work: Optimization Methods in Finance
cites work: Sparse inverse covariance estimation with the graphical lasso
cites work: The conditional autoregressive Wishart model for multivariate stock market volatility
cites work: The Wishart autoregressive process of multivariate stochastic volatility
cites work: Dynamic principal component CAW models for high-dimensional realized covariance matrices
cites work: The Model Confidence Set
cites work: Q3185327
cites work: Large volatility matrix estimation with factor-based diffusion model for high-frequency financial data
cites work: Asymptotic theory for large volatility matrix estimation based on high-frequency financial data
cites work: Evaluating Volatility and Correlation Forecasts
cites work: Optimal sparse volatility matrix estimation for high-dimensional Itô processes with measurement errors
cites work: Model selection and estimation in the Gaussian graphical model
DOI: 10.1007/S42081-020-00100-0
description: scientific article; zbMATH DE number 7447496