Arbeitspapier
A blocking and regularization approach to high dimensional realized covariance estimation
We introduce a regularization and blocking estimator for well-conditioned high-dimensional daily covariances using high-frequency data. Using the Barndorff-Nielsen, Hansen, Lunde, and Shephard (2008a) kernel estimator, we estimate the covariance matrix block-wise and regularize it. A data-driven grouping of assets of similar trading frequency ensures the reduction of data loss due to refresh time sampling. In an extensive simulation study mimicking the empirical features of the S&P 1500 universe we show that the 'RnB' estimator yields efficiency gains and outperforms competing kernel estimators for varying liquidity settings, noise-to-signal ratios, and dimensions. An empirical application of forecasting daily covariances of the S&P 500 index confirms the simulation results.
- Sprache
-
Englisch
- Erschienen in
-
Series: CFS Working Paper ; No. 2009/20
- Klassifikation
-
Wirtschaft
Semiparametric and Nonparametric Methods: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- Thema
-
Covariance Estimation
Blocking
Realized Kernel
Regularization
Microstructure
Asynchronous Trading
Varianzanalyse
Schätztheorie
Core
Multivariate Analyse
Theorie
Schätzung
Börsenkurs
Wertpapierhandel
Aktienmarkt
Mikrostrukturanalyse
USA
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Hautsch, Nikolaus
Kyj, Lada M.
Hautsch, Nikolaus
- Ereignis
-
Veröffentlichung
- (wer)
-
Goethe University Frankfurt, Center for Financial Studies (CFS)
- (wo)
-
Frankfurt a. M.
- (wann)
-
2009
- Handle
- URN
-
urn:nbn:de:hebis:30-72694
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Hautsch, Nikolaus
- Kyj, Lada M.
- Goethe University Frankfurt, Center for Financial Studies (CFS)
Entstanden
- 2009
Ähnliche Objekte (12)
A blocking and regularization approach to high dimensional realized covariance estimation
A blocking and regularization approach to high dimensional realized covariance estimation
A blocking and regularization approach to high dimensional realized covariance estimation
HARNet: A convolutional neural network for realized volatility forecasting
Testing multiplicative error models using conditional moment tests
Analyzing the Time between Trades with a Gamma Compounded Hazard Model. An Application to LIFFE Bund Future Transactions
Modelling Intraday Trading Activity Using Box-Cox-ACD Models
Capturing common components in high-frequency financial time series: a multivariate stochastic multiplicative error model
Capturing common components in high-frequency financial time series: A multivariate stochastic multiplicative error model
Determinants of Inter-Trade Durations and Hazard Rates Using Proportional Hazard ARMA Model
Bayesian Inference in a Stochastic Volatility Nelson-Siegel Model
Analyzing interest rate risk: Stochastic volatility in the term structure of government bond yields
A blocking and regularization approach to high dimensional realized covariance estimation
A blocking and regularization approach to high dimensional realized covariance estimation
A blocking and regularization approach to high dimensional realized covariance estimation
HARNet: A convolutional neural network for realized volatility forecasting
Testing multiplicative error models using conditional moment tests
Analyzing the Time between Trades with a Gamma Compounded Hazard Model. An Application to LIFFE Bund Future Transactions
Modelling Intraday Trading Activity Using Box-Cox-ACD Models
Capturing common components in high-frequency financial time series: a multivariate stochastic multiplicative error model
Capturing common components in high-frequency financial time series: A multivariate stochastic multiplicative error model
Determinants of Inter-Trade Durations and Hazard Rates Using Proportional Hazard ARMA Model
Bayesian Inference in a Stochastic Volatility Nelson-Siegel Model
Analyzing interest rate risk: Stochastic volatility in the term structure of government bond yields
A blocking and regularization approach to high dimensional realized covariance estimation
A blocking and regularization approach to high dimensional realized covariance estimation
A blocking and regularization approach to high dimensional realized covariance estimation
HARNet: A convolutional neural network for realized volatility forecasting
Testing multiplicative error models using conditional moment tests
Analyzing the Time between Trades with a Gamma Compounded Hazard Model. An Application to LIFFE Bund Future Transactions
Modelling Intraday Trading Activity Using Box-Cox-ACD Models
Capturing common components in high-frequency financial time series: a multivariate stochastic multiplicative error model
Capturing common components in high-frequency financial time series: A multivariate stochastic multiplicative error model
Determinants of Inter-Trade Durations and Hazard Rates Using Proportional Hazard ARMA Model
Bayesian Inference in a Stochastic Volatility Nelson-Siegel Model