A SURVEY OF SEQUENTIAL MONTE CARLO METHODS FOR ECONOMICS AND FINANCE
A SURVEY OF SEQUENTIAL MONTE CARLO METHODS FOR ECONOMICS AND FINANCE
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DOI：
10.1080/07474938.2011.607333
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发表时间：
2012-01-01
影响因子：
1.2
通讯作者：
Creal, Drew
中科院分区：
文献类型：
--
作者：
Creal, Drew
本文是经济学家对顺序蒙特卡罗方法（也称为粒子滤波器）领域的介绍和调查。顺序蒙特卡罗方法是基于模拟的算法，用于计算应用工作中经常出现的高维和/或复杂积分。这些方法在经济和金融领域变得越来越流行；从宏观经济学中的动态随机一般均衡模型到期权定价。本文的目的是解释该方法的基础知识，提供文献参考，并涵盖一些在实践中证明该方法合理的理论结果。
This article serves as an introduction and survey for economists to the field of sequential Monte Carlo methods which are also known as particle filters. Sequential Monte Carlo methods are simulation-based algorithms used to compute the high-dimensional and/or complex integrals that arise regularly in applied work. These methods are becoming increasingly popular in economics and finance; from dynamic stochastic general equilibrium models in macro-economics to option pricing. The objective of this article is to explain the basics of the methodology, provide references to the literature, and cover some of the theoretical results that justify the methods in practice.