Integrated vector autoregressive model with multivariate stochastic volatility applied to the united states economy in the period 1948-2019
Keywords:
Stochastic Volatility, VAR-MSV, Multi-Move SamplerAbstract
Vector autoregressive (VAR) models have proven to be efficient in capturing the dynamic relationships of multivariate time series. Multivariate stochastic volatility (MSV) models have shown to be useful for modeling the variance as it changes over time. Therefore, this article proposes the integration of a VAR model with an MSV model (VAR-MSV). The choice of the most suitable VAR-MSV is carried out by means of the Deviance Information Criterion (DIC). An application was made to two key macroeconomic variables for the United States. It added the SP500 stock market index and interpreted the results. To estimate the parameters, Monte Carlo methods via Markov Chains (MCMC) are used. The results indicate that the VAR-MSV model captures dynamic relationships as well as variance changing over time effectively.
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