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 positivedefiniteness 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”, Journal of Econometrics, 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.

Additional Metadata
Keywords Matrix-exponential transformation, Realized stochastic covariances, Realized conditional, covariances, Asymmetry, Long memory, Spillovers, Dynamic covariance matrix, Finite, sample properties, Forecasting performance.
JEL Time-Series Models; Dynamic Quantile Regressions (jel C22), Time-Series Models; Dynamic Quantile Regressions (jel C32), Financial Econometrics (jel C58), Financing Policy; Capital and Ownership Structure (jel G32)
Persistent URL
Series Econometric Institute Research Papers
Asai, M, Chang, C-L, & McAleer, M.J. (2016). Realized Matrix-Exponential Stochastic Volatility with Asymmetry, Long Memory and Spillovers. Econometric Institute Research Papers. Retrieved from