抵押贷款证券化产品之早偿率与定价方法研究

题目：抵押贷款证券化产品之早偿率与定

价方法研究

摘要

资产证券化对于提高银行资本充足率和资产的流动性、降低融资成本和增加中间业务收入都具有重要作用。2005 年岁末,开行和建行分别推出信贷资产支持证券（ABS）和个人住房抵押贷款支持证券（MBS）标志着我国真正意义上的资产证券化终于走向市场，并得到投资者和国内外研究机构的认可。随着资产证券化市场的逐步推进，其核心部分——定价技术的重要性不断提高，越来越得到各研究机构的重视。

本文主要对资产证券化的基本理论、运行框架和定价进行了阐述和研究，重点就影响定价的关键性因素—早偿率以及证券化产品的定价方法进行了系统性分析，结合了期权调整利差法（OAS）、蒙特卡罗模拟方法及相关参数选择策略进行了实证性定价研究。

本文通过研究国内外的早偿率模型及影响因素，结合行为金融学中前景理论进行因素分析，采用了动态模型分析方法，使用了地域、季节性、贷款信息、借款人信息等多个变量，设定LOGISTIC回归模型进行建模。本文选用了某一家商业银行的个人抵押贷款样本数据，采用了基于贷款层次计量早偿速度的方法，把早偿率的分析从传统单一的早偿率模型拆分为部分早偿率模型和全部早偿率模型分析。通过LOGISTIC回归分析后发现，影响部分早偿率和全部早偿率的因素确实存在差别，同时通过验证得到的预测综合早偿率指标与实际早偿率指标偏差为0.1135%,偏差率为4.16%。本文在综合比较分析抵押贷款证券定价常用的静态现金流量收益率法（SCFS）、静态利差法（SS）和期权调整利差法(OAS)等方法后，采用了OAS方法进行抵押贷款证券的定价分析，并选用Vasicek单因子模型作为利率的动态模型，通过蒙特卡罗模拟产生2000条动态利率路径，结合早偿率指

标等数据，来进行抵押贷款证券的定价。在定价过程中本文用VB语言在EXCEL 基础上编写了定价计算的程序，通过把本文综合早偿率模型计算之结果与MBS 债券发行说明书公布的固定早偿率分别代入程序进行运算，最终得到两者计算的期权利差的差额为48-60个基点。由此可见，由于对早偿率预测过低，MBS债券在发行时设置的利率上下限确实压低了应有的溢价。

本文通过对抵押贷款证券化产品的早偿率模型及OAS定价方法的分析和研究，提出了一个初步的定价框架，尤其是通过对早偿率的创新性研究和实证分析，为我国资产证券化的进一步创新和发展提供有益的借鉴。

关键词：抵押贷款证券化早偿率模型期权调整利差法蒙特卡罗模拟

The studies on Prepayment model and Pricing method of

Mortgage-backed Securitization

ABSTRACT

Asset securitization is of great importance in concern with improving the capital adequacy, improving assets liquidity, increasing the intermediary operation yield, and reducing the financing cost. In 2005, China Developing Bank and China Construction Bank all Bring forward ABS-- Asset-backed Securities and MBS -- Mortgage-backed Securities which means the Assets Securitization in china has already walked into the financial market and is been approved by investors and research institutions through out the world. And as we go further in the process, the importance of Pricing method which is one of the core Technologies in Assets Securitization is been emphasized more and more.

This article mainly explains the basic theory of Asset Securitization, the operating frame and the Pricing method. We focused on the factor Prepayment which affects Pricing greatly and the systematic explanation of Pricing methods. We combined the OAS (option adjusted spread), Monte Carlo Simulation Approach and some other parameter choosing methods in order to give a empirical study demonstration of Pricing Tech in this article.

By studying the world’s Prepayment models and factors using Prospect Theory in Behavior Finance, we build the model using dynamic analyzing method according to Logistic regression model. This model is using many variables such as distribution, seasoning, loan information and debtors’ inform. In the article we choose the personal mortgage data of a commercial bank as the resource data set. And while analyzing the Prepayment behavior using the method that calculates the Prepayment rate based on the levels of loan, we transform the analyzing method of Prepayment rate from module analyzing into partial module analyzing and thorough module analyzing. After performing the Logistic Regression analysis, we discovered the factor that affects Partial Prepayment Model and the Thorough Prepayment Model are different, we also approved that the measured Partial Prepayment Rate discriminate from the Real Prepayment Rate by 0.1135%, the differentiate ratio is 4.16%. In the article, we compared the normal used methods of Pricing the Mortgage-backed Securities, such methods are SCFY (Static Cash Flow Yield), SS (Static Spread), and OAS. And we used the OAS to do the pricing. During the process ,we picked the Vasicek single factor model as the interest rate dynamic model, and by using the Monte Carlo Simulation Approach we simulate 2000 dynamic interest rate way. In combination of the Prepayment rate and other variables, we developed a Pricing method. Furthermore, we programmed the Pricing Method using VB based on EXCEL, and while comparing the program given Prepayment rate and the MBS’s fixed Prepayment rate, we noticed that the two numbers are varied by 48 to 60 basic