Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/149480
Authors: 
Asai, Manabu
Chang, Chia-Lin
McAleer, Michael
Year of Publication: 
2016
Series/Report no.: 
Tinbergen Institute Discussion Paper 16-076/III
Abstract: 
The paper develops a novel realized matrix-exponential stochastic volatility model of multivariate returns and realized covariances that incorporates asymmetry and long memory (hereafter the RMESV-ALM model). The matrix exponential transformation guarantees the positive-definiteness of the dynamic covariance matrix. The contribution of the paper ties in with Robert Basmann’s seminal work in terms of the estimation of highly non-linear model specifications (“Causality tests and observationally equivalent representations of econometric models”, <I>Journal of Econometrics</I>, 1988, 39(1-2), 69–104), especially for developing tests for leverage and spillover effects in the covariance dynamics. Efficient importance sampling is used to maximize the likelihood function of RMESV-ALM, and the finite sample properties of the quasi-maximum likelihood estimator of the parameters are analysed. Using high frequency data for three US financial assets, the new model is estimated and evaluated. The forecasting performance of the new model is compared with a novel dynamic realized matrix-exponential conditional covariance model. The volatility and co-volatility spillovers are examined via the news impact curves and the impulse response functions from returns to volatility and co-volatility.
Subjects: 
Matrix-exponential transformation
Realized stochastic covariances
Realized conditional covariances
Asymmetry
Long memory
Spillovers
Dynamic covariance matrix
Finite sample properties
Forecasting performance
JEL: 
C22
C32
C58
G32
Document Type: 
Working Paper

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