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

摘要：

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.

关键词： derivative financial instruments EWS classification Support vector machine

作者: ZHANG Jie SUN Yue-yao

作者单位: School of Economics, Shandong University, Jinan 250100, P.R. China School of Economic and Management School of Economics, Shandong University, Jinan 250100, P.R. China

会议类型: 国际会议

会议名称: 2011 Eighth International Conference on Fuzzy System and Knowledge Discovery(第八届模糊系统与知识发现国际会议 FSKD 2011)

会议地点: 上海

会议语种:英文

页码: 321-325

在线出版日期: 2011-07-26（万方平台首次上网日期，不代表论文的发表时间）