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A Multivariate GARCH Model with Aggregate Threshold Dynamics

This paper proposes a threshold multivariate GARCH model (Threshold MGARCH) which integrates threshold nonlinearity, mean and volatility asymmetries and time-varying correlation in financial markets. The main feature of this model is that the threshold variables are formulated as average or aggregate previous returns. Estimation is performed using Markov Chain Monte Carlo methods. Forecasts of volatility and value at risk can be generated from the predictive distributions. The proposed methodology is illustrated using both simulated and real market data.