Comparing the forecastability of alternative quantitative models: A trading simulation approach in financial engineering

Abstract

In this article, we build Box-Jenkins ARMA model and ARMA-GARCH model to forecast the returns of shanghai stock exchange composite index in financial engineering. Out-of-sample forecasting performances are evaluated to compare the forecastability of the two models. Traditional engineering type of models aim to minimize statistical errors, however, the model with minimum engineering type of statistical errors does not necessarily guarantee maximized trading profits, which is often deemed as the ultimate objective of financial application. The best way to evaluate alternative financial model is therefore to evaluate their trading performance by means of trading simulation.

We find that both quantitative models are able to forecast the future movements of the market accurately, which yields significant risk adjusted returns compared to the overall market during the out-of-sample period. In addition, although the ARMA-GARCH model is better than the ARMA model theoretically and statistically, the latter outperforms the former with significantly higher trading performances.