Recovering implied risk-neutral probability density function using SVR  

基于SVR的隐含风险中性概率密度函数提取(英文)

作　　者：胡小平[1] 崔海蓉[1,2] 朱丽华[1] 王新燕[1] 

机构地区：[1]东南大学经济管理学院,南京211189 [2]南京农业大学工学院,南京210032

出　　处：《Journal of Southeast University(English Edition)》2010年第3期489-493,共5页东南大学学报（英文版）

基　　金：The National Natural Science Foundation of China (No.70671025)

摘　　要：Using support vector regression （SVR）, a novel non-parametric method for recovering implied risk-neutral probability density function （IRNPDF） is investigated by solving linear operator equations. First, the SVR principle for function approximation is introduced, and an SVR method for solving linear operator equations with knowing some values of the right-hand function and without knowing its form is depicted. Then, the principle for solving the IRNPDF based on SVR and the method for constructing cross-kernel functions are proposed. Finally, an empirical example is given to verify the validity of the method. The results show that the proposed method can overcome the shortcomings of the traditional parametric methods, which have strict restrictions on the option exercise price; meanwhile, it requires less data than other non-parametric methods, and it is a promising method for the recover of IRNPDF.利用支持向量回归机(SVR),通过求解线性算子方程,提出了一种全新的非参数类恢复隐含风险中性概率密度函数的方法.首先,介绍了支持向量回归机应用于函数逼近的基本原理,当仅知算子方程右边函数的一些函数值而不知其函数形式时,描述了基于支持向量回归机的线性算子方程求解方法.然后,给出了基于支持向量回归机的隐含风险中性概率密度函数求解原理及交叉核函数的构建方法.最后,通过实证研究,验证了该方法的有效性.研究结果表明,所提方法克服了传统参数类方法对期权执行价格有严格限制的缺陷,同时对数据量的要求也比其他非参数类方法少,是一种很有前景的还原隐含风险中性概率方法与手段.

关 键 词：support vector regression option prices implied risk-neutral probability linear operator equation non-parametric method 

分 类 号：F830.9[经济管理—金融学]