On the estimation of dynamic conditional correlation models

Hafner, Christian; Reznikova, Olga

doi:10.1016/j.csda.2010.09.022

Cited by 87 publications

(49 citation statements)

References 19 publications

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“…This approach is obviously more complicated but due to the highly nonlinear nature of the problem, it is also more powerful. Hafner and Reznikova (2012) propose the application of linear shrinkage to the estimation of DCC models. However, they restrict themselves to smaller dimensions and throw shrinkage into a horse race against composite likelihood, instead of harnessing the combined powers of the two methods.…”

Section: Linear Shrinkagementioning

confidence: 99%

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Large Dynamic Covariance Matrices

2016

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract 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 heteroskedasticity; 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 paper marries these two strands of literature in order to deliver improved estimation of large dynamic covariance matrices. Terms of use: Documents in

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Section: Linear Shrinkagementioning

confidence: 99%

“…Related to our proposal is the work of Hafner and Reznikova (2012). The approach that they champion does not use the first tool, which is composite likelihood, and uses linear shrinkage instead of nonlinear shrinkage for the estimation of the intercept matrix.…”

Section: Introductionmentioning

confidence: 99%

Large Dynamic Covariance Matrices

2016

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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%

“…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%

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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“…In a Bayesian context, Ledoit and Wolf (2004) proposed the covariance matrix estimator obtained by shrinking the sample correlation matrix to an equicorrelated matrix for the purpose of the portfolio optimization. Hafner and Reznikova (2012) applied the shrinkage methods to the DCC models and improved the estimation results of the DCC model. Lucas, Schwaab, and Zhang (2012) proposed the dynamic generalized hyperbolic (GH) skew-t-error model with generalized autoregressive score (GAS) equicorrelation structure.…”

Section: Introductionmentioning

confidence: 99%

Dynamic equicorrelation stochastic volatility

Kurose

Omori

2016

Computational Statistics & Data Analysis

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A multivariate stochastic volatility model with dynamic equicorrelation and cross leverage effect is proposed and estimated. Using a Bayesian approach, an efficient Markov chain Monte Carlo algorithm is described where we use the multi-move sampler, which generates multiple latent variables simultaneously. Numerical examples are provided to show its sampling efficiency in comparison with the simple algorithm that generates one latent variable at a time given other latent variables. Furthermore, the proposed model is applied to the multivariate daily stock price index data. The model comparisons based on the portfolio performances and DIC show that our model overall outperforms competing models.

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On the estimation of dynamic conditional correlation models

Cited by 87 publications

References 19 publications

Large Dynamic Covariance Matrices

Large Dynamic Covariance Matrices

Risk-Based Portfolios with Large Dynamic Covariance Matrices

Dynamic equicorrelation stochastic volatility

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