Predictive autoregressive models using macroeconomic variables: the role of oil prices in the Russian stock market

Abstract

This article evaluates the relationship of macroeconomic variables of the domestic market with the stock index on the Moscow exchange and selects forecast specifications based on an integrated autoregressive model - the moving average. The methods used are included in an integrated autoregressive-moving average model with exogenous variables and seasonal component, Box and Jenkins approach, auto.arima in R function, Hyndman and Athanasopoulos approach, and maximum likelihood method. The results demonstrate that the inclusion of external regressors in the one-dimensional ARIMAX model improves its predictive characteristics. Time series of macro-indicators of the domestic market – the consumer price index, the index of output of goods and services for basic activities are not interrelated with the index of the Moscow exchange, with the exception of the dollar exchange rate. The positive correlation between the Moscow exchange index and macro indicators of the world economy - the S&P stock index, the price of Brent oil, was confirmed. In models with minimal AIC, a rare presence of the MA component was found, which shows that the prevailing dependence of the stock market yield on previous values of the yield (AR component) and thus, better predictability of the yield. It has shown that for stock market forecasting, manual selection of the ARIMA model type can give better results (minimum AIC and minimum RMSE) than the built-in auto.arima algorithm in R. It is shown that from a practical point of view, when selecting forecast models, the RMSE criterion is more useful for investors, which measures the standard error of the forecast in points of the stock index. For the scientific novelty, using Russian financial data for the period from March 2000 to March 2018 to measure the connection of macro indicators of domestic and global markets with the Moscow exchange stock index, considering seasonality can be noticed. The comparison of the forecast model’s accuracy of the ARIMA type obtained by automatic and manual “selection by AIC and RMSE is performed in favor of manual selection. It could be noted that the main conclusions of the article can be used in scientific and practical activities in the stock markets as a practical significance.