We prove that a time series satisfying a (linear) multivariate autoregressive moving average (VARMA) model satisfies the same model assumption in the reversed time direction, too, if all innovations are normally distributed. This reversibility breaks down if the innovations are non-Gaussian. This means that under the assumption of a VARMA process with nonGaussian noise, the arrow of time become...

Although the scalar components methodology used to build VARMA models is rather difficult, the VAR models application being easier in practice, the forecasts based on the first models have a higher degree of accuracy. This statement is demonstrated for variables like the 3-month Treasury bill rate and the spread between the 10 year government bond yield, where the quarterly data are from the U....

We employ a VARMA DCC-GARCH model to search for portfolio diversification with Bitcoin in global industry portfolios and bond index. find lower dynamic conditional correlations between & index, allowing an investment hedge the risk against bonds. The most effective Bitcoin/industry (bond) is short Utilities sector. Results are robust use of US cryptocurrency index instead Bitcoin, respectively....

This paper estimates the dynamic conditional correlations in the returns on Tapis oil spot and onemonth forward prices for the period 2 June 1992 to 16 January 2004, using recently developed multivariate conditional volatility models, namely the Constant Conditional Correlation Multivariate GARCH (CCCMGARCH) model of Bollerslev [1990], Vector Autoregressive Moving Average – GARCH (VARMAGARCH) m...

Asia is presently the most important market for the production and consumption of natural rubber. World prices of rubber are not only subject to changes in demand, but also to speculation regarding future markets. Japan and Singapore are the major futures markets for rubber, while Thailand is one of the world's largest producers of rubber. As rubber prices are influenced by external markets, it...

We propose the use of multivariate orthogonal space transformations and Vector Autoregressive Moving-Average (VARMA) models in combination with data-driven system identification models to improve residual-based approaches to fault detection in rolling mills. Introducing VARMA models allows us to build k-step ahead multi-dimensional prediction models including the time lags that best explain the...

International tourism demand, or tourist arrivals, to Australia has recently experienced dramatic fluctuations due to changes in the economic, financial and political environment. However, variations in tourism demand, specifically the conditional variance, or volatility, have not previously been investigated. An analysis of such volatility is essential for investigating the effects of shocks i...