Composite forecasting of vast-dimensional realized covariance matrices using factor state-space models
Bibliographic Data
| ID | 21541980 |
|---|---|
| Authors | Jan Patrick Hartkopf (0000-0002-3704-1856, University of Cologne, corresponding author) |
| Year | 2023 |
| Volume | 64 |
| Issue | 1 |
| Pages | 393-436 |
| Publication date | 2023-01-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Empirical Economics (JOURNAL) |
| Journal identifiers | ISSN: 0377-7332 • E-ISSN: 1435-8921 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s00181-022-02245-1 |
| OpenAlex | W4228998898 |
| Language | EN |
| References cited | 48 |
We propose a dynamic factor state-space model for the prediction of high-dimensional realized covariance matrices of asset returns. Using a block LDL decomposition of the joint covariance matrix of assets and factors, we express the realized covariance matrix of the individual assets similar to an approximate factor model. We model the individual parts, i.e., the factor and residual covariances as well as the factor loadings, independently via a tractable state-space approach. This results in closed-form Matrix- F predictive densities for the distinct covariance elements and Student’s t predictive densities for the factor loadings. In an out-of-sample forecasting and portfolio selection exercise we compare the performance of the proposed factor model under different specifications of the residual dynamics. These includes block diagonal residuals based on the GICS sector classifications and strict diagonality assumptions as well as combinations of both using linear shrinkage. We find that the proposed model performs very well in an empirical application to realized covariance matrices for 225 NYSE-traded stocks using the well-known Fama–French factors and sector-specific factors represented by exchange traded funds
Algorithm · Covariance · Covariance matrix · Diagonal · Econometrics · Economics · Factor analysis · Financial economics · Portfolio · Residual · State space · State-space representation · Statistics · Complex Systems and Time Series Analysis · Computer Science · Financial Risk and Volatility Modeling · Mathematics · Stock Market Forecasting Methods · Applied Mathematics
Common risk factors in the returns on stocks and bonds
Modeling and Forecasting Realized Volatility
Econometric Analysis of Vast Covariance Matrices Using Composite Realized Kernels and Their Application to Portfolio Choice
Large Dynamic Covariance Matrices
Dynamic Equicorrelation
New Heavy Models for Fat-Tailed Realized Covariances and Returns
Incorporating Global Industrial Classification Standard Into Portfolio Allocation
Flexible Multivariate GARCH Modeling with an Application to International Stock Markets
| Highly cited | No |
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