Pattern recognition in financial surveillance with the ARMA-GARCH time series model using support vector machine

Abstract

As the intersection of finance and statistics, financial surveillance is a new interdisciplinary field of research. In this field, statistical process control methods are applied to monitor financial indices. The final aim is to detect out-of-control conditions and trigger a signal as soon as possible. These early signals can help practitioners in making on-time decisions. In this paper, a new method based on a support vector machine is proposed to detect upward and downward shifts with step and trend patterns in auto-correlated financial processes. These processes are modeled by the autoregressive moving average (ARMA) and generalized autoregressive conditional heteroskedasticity (GARCH) variance (ARMA-GARCH) time series model. Autocorrelation structure in data with changing volatility makes pattern recognition difficult. As such, some features are selected to extract different properties of the considered patterns. Moreover, a new feature named the maximum degree between the horizontal line and the consecutive observations line is used for the better distinction of the step and trend shift patterns. To evaluate the performance of the proposed method, we performed a comprehensive simulation study. Moreover, to illustrate the proposed method's application, it was applied to classify patterns in the OPEC crude oil basket return and the Tehran stock exchange index.