Using support vector machine to develop an early warning system for the risks of derivative financial instruments

It is proposed a classification approach for building an early warning system (EWS) against the potential risks of derivative financial instruments. This EWS classification approach has been developed mainly for monitoring daily financial market against its abnormal movement and is based on the newly-developed crisis hypothesis that the risks of derivative financial instruments is often self-fulfilling because of herding behavior of the investors. This article extends the EWS classification approach to the traditional-type risk, i.e., the risks of derivative financial instruments is an outcome of the long-term deterioration of the financial fundamentals. It is shown that support vector machine (SVM) is an efficient classifier in such case.