Forecasting Comparison of Long Term Component Dynamic Models for Realized Covariance Matrices

Bauwens, Luc; Braione, Manuela; Storti, Giuseppe

doi:10.15609/annaeconstat2009.123-124.0103

Cited by 16 publications

(9 citation statements)

References 22 publications

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“…This model decomposes the volatility of high frequency asset returns into the product of three components: a daily, a diurnal and a stochastic intraday component. In a multivariate setting, Bauwens et al (2016) and Bauwens et al (2017) developed and discussed several component specifications for time series of realized covariance matrices.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous component multiplicative error models for forecasting trading volumes

Naimoli

Storti

2019

International Journal of Forecasting

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We propose a novel approach to modelling and forecasting high frequency trading volumes. The new model extends the Component Multiplicative Error Model of Brownlees et al. (2011) by introducing a more flexible specification of the long-run component. This uses an additive cascade of MIDAS polynomial filters, moving at different frequencies, in order to reproduce the changing long-run level and the persistent autocorrelation structure of high frequency trading volumes. After investigating its statistical properties, the merits of the proposed approach are illustrated by means of an application to six stocks traded on the XETRA market in the German Stock Exchange.

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Section: Introductionmentioning

confidence: 99%

Heterogeneous component multiplicative error models for forecasting trading volumes

Naimoli

Storti

2019

International Journal of Forecasting

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“…Another branch of the literature links RCOV to multivariate GARCH models in Noureldin, Shephard, and Sheppard () and Hansen, Lunde, and Voev (). The strong persistence patterns in RCOV matrix elements are recognized in Bauwens, Braione, and Storti (, ), while the importance of fat tails is shown in Jin and Maheu () and Opschoor, Janus, Lucas, and Dijk ().…”

Section: Introductionmentioning

confidence: 99%

Bayesian parametric and semiparametric factor models for large realized covariance matrices

Jin

Maheu

Yang

2019

J of Applied Econometrics

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Summary This paper introduces a new factor structure suitable for modeling large realized covariance matrices with full likelihood‐based estimation. Parametric and nonparametric versions are introduced. Because of the computational advantages of our approach, we can model the factor nonparametrically as a Dirichlet process mixture or as an infinite hidden Markov mixture, which leads to an infinite mixture of inverse‐Wishart distributions. Applications to 10 assets and 60 assets show that the models perform well. By exploiting parallel computing the models can be estimated in a matter of a few minutes.

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“…Another branch of the literature links RCOV to multivariate GARCH models in Noureldin et al (2012) and Hansen et al (2014). The strong persistence patterns in RCOV matrix elements are recognized in Bauwens et al (2016Bauwens et al ( , 2017 while the importance of fatter tails is shown in Jin & Maheu (2016) and Opschoor et al (2017).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Parametric and Semiparametric Factor Models for Large Realized Covariance Matrices

2017

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This paper introduces a new factor structure suitable for modeling large realized covariance matrices with full likelihood based estimation. Parametric and nonparametric versions are introduced. Due to the computational advantages of our approach we can model the factor nonparametrically as a Dirichlet process mixture or as an infinite hidden Markov mixture which leads to an infinite mixture of inverse-Wishart distributions. Applications to 10 assets and 60 assets show the models perform well. By exploiting parallel computing the models can be estimated in a matter of a few minutes. JEL Classification: G17, C11, C14, C32, C58 key words: infinite hidden Markov model, Dirichlet process mixture, inverse-Wishart, predictive density, high-frequency data * We are grateful for helpful comments from participants at the CFIRM conference Western University and the RCEA Bayesian Econometric Workshop University of Melbourne. Maheu thanks SSHRC for financial support.

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Forecasting Comparison of Long Term Component Dynamic Models for Realized Covariance Matrices

Cited by 16 publications

References 22 publications

Heterogeneous component multiplicative error models for forecasting trading volumes

Heterogeneous component multiplicative error models for forecasting trading volumes

Bayesian parametric and semiparametric factor models for large realized covariance matrices

Bayesian Parametric and Semiparametric Factor Models for Large Realized Covariance Matrices

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