Closed-form approximations of moments and densities of continuous–time Markov models

IF 1.9 3区 经济学 Q2 ECONOMICS Journal of Economic Dynamics & Control Pub Date : 2024-09-19 DOI:10.1016/j.jedc.2024.104948
Dennis Kristensen , Young Jun Lee , Antonio Mele
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Abstract

This paper develops power series expansions of a general class of moment functions, including transition densities and option prices, of continuous-time Markov processes, including jump–diffusions. The proposed expansions extend the ones in Kristensen and Mele (2011) to cover general Markov processes, and nest transition density and option price expansions recently developed in the literature, thereby connecting seemingly different ideas in a unified framework. We show how the general expansion can be implemented for fully general jump–diffusion models. We provide a new theory for the validity of the expansions which shows that series expansions are not guaranteed to converge as more terms are added in general once the time span of interest gets larger than some model–specific threshold. Thus, these methods should be used with caution when applied to problems with a larger time span of interest, such as long-term options or data observed at a low frequency. At the same time, the numerical studies in this paper demonstrate good performance of the proposed implementation in practice when applied to pricing options with time to maturity below three months. Thus, our expansions are particularly well suited for pricing ultra-short-term (such as “zero–day”) options.

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连续时间马尔可夫模型的矩和密度的闭式近似值
本文开发了连续时间马尔可夫过程(包括跳跃扩散)的一般矩函数(包括过渡密度和期权价格)的幂级数展开。本文提出的扩展扩展了 Kristensen 和 Mele (2011) 的扩展,涵盖了一般马尔可夫过程,并嵌套了最近在文献中提出的过渡密度和期权价格扩展,从而在一个统一的框架中连接了看似不同的观点。我们展示了如何为完全一般的跳跃扩散模型实现一般扩展。我们为扩展的有效性提供了一种新的理论,表明一旦感兴趣的时间跨度大于特定模型的某个阈值,一般来说,随着项的增加,数列扩展并不能保证收敛。因此,在应用这些方法解决时间跨度较大的问题(如长期期权或低频观测数据)时,应谨慎使用。同时,本文中的数值研究表明,当应用于对到期时间低于三个月的期权进行定价时,所提出的实现方法在实践中表现良好。因此,我们的扩展特别适合为超短期(如 "零日")期权定价。
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来源期刊
CiteScore
3.10
自引率
10.50%
发文量
199
期刊介绍: The journal provides an outlet for publication of research concerning all theoretical and empirical aspects of economic dynamics and control as well as the development and use of computational methods in economics and finance. Contributions regarding computational methods may include, but are not restricted to, artificial intelligence, databases, decision support systems, genetic algorithms, modelling languages, neural networks, numerical algorithms for optimization, control and equilibria, parallel computing and qualitative reasoning.
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