Option Pricing, Model Calibration, and Prediction with a Switchable Market: A Stochastic Approximation Algorithm

被引：0

作者：

Yin, G. ^{[1
]}

Yu, J. ^{[2
]}

Zhang, Q. ^{[3
]}

机构：

[1] Wayne State Univ, Dept Math, Detroit, MI 48202 USA

[2] Roosevelt Univ, Dept Math & Actuarial Sci, Chicago, IL 60605 USA

[3] Univ Georgia, Boyd GSRC, Dept Math, Athens, GA 30602 USA

来源：

49TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC) | 2010年

基金：

美国国家科学基金会;

关键词：

Option pricing; parameter estimation; market mode prediction; stochastic approximation; convergence; rate of convergence;

D O I：

10.1109/CDC.2010.5717667

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper considers option pricing under a regime-switching model. The switching process takes two different modes, and the underlying stock price evolves in accordance with the two modes dictated by a continuous-time, 2-state Markov chain. At a given instance, the price follows either a model of geometric Brownian motion or mean-reversion model on its market mode. We build stochastic approximation algorithms for model calibration. Convergence and rate of convergence are provided. Option market data are used to predict future market mode.

引用

页码：6997 / 7002

页数：6

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