Large Dynamic Covariance Matrices

Engle, Robert F.; Ledoit, Olivier; Wolf, Michael

doi:10.2139/ssrn.2814555

Cited by 60 publications

(128 citation statements)

References 47 publications

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“…To combine these two aspects, Hafner and Reznikova (2012) proposed an estimation method combining linear shrinkage and the DCC-GARCH model (DCC-LS), and Engle et al (2017) proposed an estimation method combining nonlinear shrinkage and the DCC-GARCH model (DCC-NLS). We propose a highly accurate estimation method that combines nonlinear shrinkage and the cDCC-GARCH model, called the cDCC-NLS method.…”

Section: Combining Nonlinear Shrinkage and The Cdcc-garch Modelmentioning

confidence: 99%

“…Engle et al (2017) later replaced this with the nonlinear shrinkage estimator (DCC-NLS). We combine nonlinear shrinkage and the cDCC-GARCH model and confirm its advantages in the next section.…”

Section: Combining Nonlinear Shrinkage and The Cdcc-garch Modelmentioning

confidence: 99%

“…We evaluate the covariance matrix estimation accuracy following Engle et al (2017). Next, we examine the effect of the difference in the estimation accuracy on the performance of the risk-based portfolios.…”

Section: Simulation Studymentioning

confidence: 99%

“…Engle et al (2017) defines an error function between the true covariance matrix generated by a (c)DCC model and the estimation. On the other hand, because of the sample estimator, the LS and NLS methods do not handle true covariance matrices at each time point, which means the errors cannot be calculated in the same way as in the (c)DCC model.…”

Section: Monte Carlo Studymentioning

confidence: 99%

“…In order to compare our results with those of previous studies (Hafner and Reznikova 2012;Engle et al 2017), we run a simulation study using the same setup. Let the covariance matrix at each time point of the DCC model, given a certain unconditional covariance and parameters, be a true covariance matrix.…”

Section: Monte Carlo Studymentioning

confidence: 99%

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Risk-Based Portfolios with Large Dynamic Covariance Matrices

Nakagawa

Imamura

Yoshida

2018

IJFS

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Abstract:In the field of portfolio management, practitioners are focusing increasingly on risk-based portfolios rather than on mean-variance portfolios. Risk-based portfolios are constructed based solely on covariance matrices, and include methods such as minimum variance (MV), risk parity (RP), and maximum diversification (MD). It is well known that the performance of a mean-variance portfolio depends on the accuracy of the estimations of the inputs. However, no studies have examined the relationship between the performance of risk-based portfolios and the estimated accuracy of covariance matrices. In this research, we compare the performance of risk-based portfolios for several estimation methods of covariance matrices in the Japanese stock market. In addition, we propose a highly accurate estimation method called cDCC-NLS, which incorporates nonlinear shrinkage into the cDCC-GARCH model. The results confirm that (1) the cDCC-NLS method shows the best estimation accuracy, (2) the RP and MD do not depend on the estimation accuracy of the covariance matrix, and (3) the MV does depend on the estimation accuracy of the covariance matrix.

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Section: Combining Nonlinear Shrinkage and The Cdcc-garch Modelmentioning

confidence: 99%

Section: Combining Nonlinear Shrinkage and The Cdcc-garch Modelmentioning

confidence: 99%

Section: Simulation Studymentioning

confidence: 99%

Section: Monte Carlo Studymentioning

confidence: 99%

Section: Monte Carlo Studymentioning

confidence: 99%

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Risk-Based Portfolios with Large Dynamic Covariance Matrices

Nakagawa

Imamura

Yoshida

2018

IJFS

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Unprofitability of food market investments

Vinzelberg

Auer

2022

Manage Decis Econ

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Using more than two decades of disaggregated return data, we provide a comprehensive study of the investment benefits offered by food futures. In addition to comparing them to established passive and active investment vehicles, we investigate whether diversified investors can capitalize on including food futures into their portfolios. Our analysis is conducted within traditional and modern mean‐risk portfolio frameworks (measuring risk in terms of variance and tail size, respectively) supplemented by robust resampling techniques. Reward‐to‐risk investment performance is evaluated out of sample and by means of state‐of‐the‐art bootstrap testing. Overall, we find that food futures neither surpass standalone investments in stock, bond, and nonfood commodity markets nor do they significantly improve optimized multi‐asset portfolios.

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High‐dimensional covariance matrix estimation

Lam

2019

WIREs Computational Stats

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Covariance matrix estimation plays an important role in statistical analysis in many fields, including (but not limited to) portfolio allocation and risk management in finance, graphical modeling, and clustering for genes discovery in bioinformatics, Kalman filtering and factor analysis in economics. In this paper, we give a selective review of covariance and precision matrix estimation when the matrix dimension can be diverging with, or even larger than the sample size. Two broad categories of regularization methods are presented. The first category exploits an assumed structure of the covariance or precision matrix for consistent estimation. The second category shrinks the eigenvalues of a sample covariance matrix, knowing from random matrix theory that such eigenvalues are biased from the population counterparts when the matrix dimension grows at the same rate as the sample size. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data Statistical and Graphical Methods of Data Analysis > Multivariate Analysis Statistical and Graphical Methods of Data Analysis > Nonparametric Methods

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Large Dynamic Covariance Matrices

Cited by 60 publications

References 47 publications

Risk-Based Portfolios with Large Dynamic Covariance Matrices

Risk-Based Portfolios with Large Dynamic Covariance Matrices

Unprofitability of food market investments

High‐dimensional covariance matrix estimation

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