Arbeitspapier
Singular conditional autoregressive Wishart model for realized covariance matrices
Realized covariance matrices are often constructed under the assumption that richness of intra-day return data is greater than the portfolio size, resulting in non-singular matrix measures. However, when for example the portfolio size is large, assets suffer from illiquidity issues, or market microstructure noise deters sampling on very high frequencies, this relation is not guaranteed. Under these common conditions, realized covariance matrices may obtain as singular by construction. Motivated by this situation, we introduce the Singular Conditional Autoregressive Wishart (SCAW) model to capture the temporal dynamics of time series of singular realized covariance matrices, extending the rich literature on econometric Wishart time series models to the singular case. This model is furthermore developed by covariance targeting adapted to matrices and a sectorwise BEKK-specification, allowing excellent scalability to large and extremely large portfolio sizes. Finally, the model is estimated to a 20 year long time series containing 50 stocks, and evaluated using out-ofsample forecast accuracy. It outperforms the benchmark Multivariate GARCH model with high statistical significance, and the sectorwise specification outperforms the baseline model, while using much fewer parameters.
- Language
-
Englisch
- Bibliographic citation
-
Series: Working Paper ; No. 1/2021
- Classification
-
Wirtschaft
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Large Data Sets: Modeling and Analysis
Financial Econometrics
Financial Forecasting and Simulation
- Subject
-
Covariance targeting
High-dimensional data
Realized covariance matrix
Stock co-volatility
Time series matrix-variate model
- Event
-
Geistige Schöpfung
- (who)
-
Alfelt, Gustav
Bodnar, Taras
Javed, Farrukh
Tyrcha, Joanna
- Event
-
Veröffentlichung
- (who)
-
Örebro University School of Business
- (where)
-
Örebro
- (when)
-
2020
- Handle
- Last update
-
10.03.2025, 11:41 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Alfelt, Gustav
- Bodnar, Taras
- Javed, Farrukh
- Tyrcha, Joanna
- Örebro University School of Business
Time of origin
- 2020
Other Objects (12)
Modelling and Forecasting of Realized Covariance Matrices
lCARE: localizing conditional autoregressive expectiles
Evaluating models of autoregressive conditional duration
Multivariate autoregressive conditional heteroskedasticity with smooth transitions in conditional correlations
lCARE – localizing Conditional AutoRegressive Expectiles
Statistical inference in conditional heteroskedastic autoregressive models
A blocking and regularization approach to high dimensional realized covariance estimation
A blocking and regularization approach to high dimensional realized covariance estimation
Estimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice
New HEAVY Models for Fat-Tailed Returns and Realized Covariance Kernels
A blocking and regularization approach to high dimensional realized covariance estimation
Fractional Integration and Fat Tails for Realized Covariance Kernels and Returns
Modelling and Forecasting of Realized Covariance Matrices
lCARE: localizing conditional autoregressive expectiles
Evaluating models of autoregressive conditional duration
Multivariate autoregressive conditional heteroskedasticity with smooth transitions in conditional correlations
lCARE – localizing Conditional AutoRegressive Expectiles
Statistical inference in conditional heteroskedastic autoregressive models
A blocking and regularization approach to high dimensional realized covariance estimation
A blocking and regularization approach to high dimensional realized covariance estimation
Estimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice
New HEAVY Models for Fat-Tailed Returns and Realized Covariance Kernels
A blocking and regularization approach to high dimensional realized covariance estimation
Fractional Integration and Fat Tails for Realized Covariance Kernels and Returns
Modelling and Forecasting of Realized Covariance Matrices
lCARE: localizing conditional autoregressive expectiles
Evaluating models of autoregressive conditional duration
Multivariate autoregressive conditional heteroskedasticity with smooth transitions in conditional correlations
lCARE – localizing Conditional AutoRegressive Expectiles
Statistical inference in conditional heteroskedastic autoregressive models
A blocking and regularization approach to high dimensional realized covariance estimation
A blocking and regularization approach to high dimensional realized covariance estimation
Estimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice
New HEAVY Models for Fat-Tailed Returns and Realized Covariance Kernels
A blocking and regularization approach to high dimensional realized covariance estimation