Financial interval time series modelling and forecasting using threshold autoregressive models

Abstract

Financial interval time series (ITS) describe the evolution of the high and low prices of an asset throughout time. Their accurate forecasts play a key role in risk management, derivatives pricing and asset allocation, demanding the development of models able to properly predict these prices. This paper evaluates threshold autoregressive models for financial ITS forecasting as a nonlinear approach for ITS considering as empirical application the main index of the Brazilian stock market, the IBOVESPA. One step ahead interval forecasts are compared against linear and nonlinear time series benchmark methods in terms of traditional accuracy metrics and quality measures designed for ITS. The results indicated the predictability of IBOVESPA ITS and that significant forecast contribution are achieved when nonlinear approaches are considered. Further, nonlinear models do provide higher accuracy when forecasting Brazilian financial ITS.