在大数据框架下基于吉布斯抽样的随机搜索方法在金融风险特征提取中的应用

The Framework for the Extraction of Risk Factors by using Stochastic Search Method based on Gibbs Sampling Algorithm in Fintech

本文的目的是在大数据框架下,系统地陈述了如何利用吉布斯抽样(Gibbs sam pling)方法作为工具,以样本误差容忍度为标准的大数据关联特征因子提取的推断原理为基础,在金融衍生品场景下对关联风险特征进行有效提取的思维和路径.具体来讲,采用马尔可夫链蒙特卡罗(MCMC)框架下的吉布斯抽样(Gibbs sampling)算法为工具,通过"OR值"(odds ratio)(也称为"比值比"或"优势比")为验证标准,在大数据(包含传统的结构化和非结构化数据)一般框架下对高度关联特征因子提取推断的原理下,系统性地陈述了如何从海量数据中提取与金融衍生品价格或者风险关联度高的风险特征因子的随机搜索方法.为了能够比较全面地展示如何利用吉布斯抽样方法通过随机搜索算法来实现对金融衍生品风险特征的提取,在本文中,我们对三种金融产品的关联特征提取进行了比较全面和系统的讨论,他们是:1)对支持"基金中的基金"(fund of funds,简称"FOF")组建的影响基金业绩关联特征的挖掘;2)对影响大宗商品期货螺纹钢价格趋势变化的关联特征指标的挖掘;3)对影响大宗商品期货铜价格趋势分析的关联特征刻画的提取.本文的分析和实证结果表明,我们在大数据框架下建立的特征提取方法除了能够有效地筛选出刻画影响基金业绩的关联特征外,也够提取出影响螺纹钢期货和铜期货价格趋势变化的关联特征,这为业界对FOF的组建与管理,对应金融衍生品价格变化走势,特别是大宗期货交易和风险管理方面提供了一种新的分析维度和风险特征因子应用方向.另外,本文讨论的从大数据的视角筛选金融衍生品风险特征因子的方法,也与过去传统的计量分析方法不同,是金融科技在大数据金融方面分析和应用的创新点.

The purpose of this paper is to systematically state how to use the Gibbs sampling method as a tool,based on the inference principle of big data association feature factors to conduct the extraction of related risk characteristics in the context of financial derivatives.Specifically,using stochastic search method based on Gibbs sampling algorithm under the framework of Markov chain Monte Carlo(MCMC),incorporating the"odd ratio"as criteria,taking FOF,commodity futures as examples,this report systematically discusses how to extract related risk factors for financial derivatives based on big data(including traditional and non-structure data)approach as shown by using so-called stochastic research method.Furthermore,we like to point out the way to identify the risk factors based on big data method established in this paper is quite now and should be useful for the financial service in the practice of Fintech.