非因果仿射过程及其在导数定价中的应用

IF 1.6 3区经济学 Q3 BUSINESS, FINANCE

Mathematical Finance Pub Date : 2023-03-31 DOI:10.1111/mafi.12384

Christian Gouriéroux, Yang Lu

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引用次数: 2

摘要

线性因子模型是仿射过程，在金融中发挥着关键作用，因为它们允许风险期限结构的准闭合形式表达。通过考虑逆时间仿射因子，我们引入了一类非因果仿射线性因子模型。这些模型与投机泡沫的定价序列尤其相关。我们证明了它们在日历时间上具有非仿射动力学特征，同时仍然提供（准）闭合形式的期限结构和导数定价公式。该框架通过利率期限结构和欧洲看涨期权定价实例进行了说明。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Noncausal affine processes with applications to derivative pricing

Linear factor models, where the factors are affine processes, play a key role in Finance, since they allow for quasi-closed form expressions of the term structure of risks. We introduce the class of noncausal affine linear factor models by considering factors that are affine in reverse time. These models are especially relevant for pricing sequences of speculative bubbles. We show that they feature nonaffine dynamics in calendar time, while still providing (quasi) closed form term structures and derivative pricing formulas. The framework is illustrated with term structure of interest rates and European call option pricing examples.

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来源期刊

Mathematical Finance 数学-数学跨学科应用

CiteScore

4.10

自引率

6.20%

发文量

审稿时长

>12 weeks

期刊介绍： Mathematical Finance seeks to publish original research articles focused on the development and application of novel mathematical and statistical methods for the analysis of financial problems. The journal welcomes contributions on new statistical methods for the analysis of financial problems. Empirical results will be appropriate to the extent that they illustrate a statistical technique, validate a model or provide insight into a financial problem. Papers whose main contribution rests on empirical results derived with standard approaches will not be considered.