Limitation of ARIMA models in financial and monetary economics

Abandoning the classical econometric modeling approach which consists in using explanatory variables (suggested by economic theory for prediction), we choose instead to use a sophisticated method developed by Box and Jenkins (1970) based solely on the past behavior of the variable being modeled/forecast. As we are in a data-rich environment and the economies and financial markets are more integrated than ever before, the quantitative methods in business and finance has increased substantially in recent years. This paper investigates the limitation of autoregressive integrated moving average (ARIMA) models in financial and monetary economics using the behavior of BET Index and EUR/RON exchange rates, respectively. Two important features discovered in the analysis of financial time series in this paper are fat-tails (large losses or gains are coming at a higher probability than the normal distribution would suggest) and volatility clustering, these empirical properties can’t be captured by integrated ARMA models, hence the limitation of these models.