Large Dynamic Covariance Matrices
Bibliographic Data
| ID | 19419893 |
|---|---|
| Authors | Robert F Engle (0009-0009-9491-1510, New York University), Olivier Ledoit (0000-0002-2073-797X, University of Zurich), Michael Wolf (0000-0001-6700-2201, University of Zurich) |
| Year | 2019 |
| Volume | 37 |
| Issue | 2 |
| Pages | 363-375 |
| Publication date | 2019-04-03 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2017.1345683 |
| OpenAlex | W2515903151 |
| Language | EN |
| Citations received | 11 |
| References cited | 28 |
Second moments of asset returns are important for risk management and portfolio selection. The problem of estimating second moments can be approached from two angles: time series and the cross-section. In time series, the key is to account for conditional heteroscedasticity; a favored model is Dynamic Conditional Correlation (DCC), derived from the ARCH/GARCH family started by Engle (1982). In the cross-section, the key is to correct in-sample biases of sample covariance matrix eigenvalues; a favored model is nonlinear shrinkage, derived from Random Matrix Theory (RMT). The present article marries these two strands of literature to deliver improved estimation of large dynamic covariance matrices. Supplementary material for this article is available online
Autoregressive conditional heteroskedasticity · Conditional variance · Covariance · Covariance matrix · Econometrics · Economics · Estimation of covariance matrices · Financial economics · Generalized method of moments · Heteroscedasticity · Panel data · Portfolio · Statistics · Complex Systems and Time Series Analysis · Financial Markets and Investment Strategies · Financial Risk and Volatility Modeling · Mathematics · Applied Mathematics
Composite forecasting of vast-dimensional realized covariance matrices using factor state-space models
Hierarchical risk clustering versus traditional risk-based portfolios
Forecasting Conditional Covariance Matrices in High-Dimensional Time Series
Fitting Vast Dimensional Time-Varying Covariance Models
Singular Conditional Autoregressive Wishart Model for Realized Covariance Matrices
Modeling Multivariate Time Series With Copula-Linked Univariate D-Vines
Graphical Copula GARCH Modeling with Dynamic Conditional Dependence
High-Dimensional Dynamic Covariance Matrices With Homogeneous Structure
Cluster GARCH
Predicting the Global Minimum Variance Portfolio
Dynamic Conditional Correlations with Partial Information Pooling
Climate change 2001
Time Series Analysis
Optimal Versus Naive Diversification
Multivariate Simultaneous Generalized ARCH
Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
Fitting Vast Dimensional Time-Varying Covariance Models
Dynamic Conditional Correlation
Testing and Valuing Dynamic Correlations for Asset Allocation
Dynamic Conditional Correlation
Modelling the Coherence in Short-Run Nominal Exchange Rates
| Unique citing works | 11 |
|---|---|
| Citations per year | 2.2 |
| Citation span | 2021 - 2026 (6) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 11 |