Title :
Using Stochastic Approximation for Parameter Estimation in Option Pricing
Author :
Yin, G. ; Zhang, Q. ; Zhuang, C.
Author_Institution :
Wayne State Univ., Detroit
Abstract :
This paper focuses on option pricing using a stochastic optimization algorithm. The underlying stock price changes according to a set of geometric Brownian motions coupled by a continuous- time finite state Markov chain. A recursive stochastic optimization algorithm is constructed to estimate the implied volatility. Convergence analysis of the algorithm is provided together with rate of convergence. Real market data is used to compare our algorithm with other schemes.
Keywords :
Brownian motion; Markov processes; continuous time systems; convergence; optimisation; parameter estimation; pricing; stock markets; continuous-time finite state Markov chain; convergence analysis; geometric Brownian motions; option pricing; parameter estimation; recursive stochastic optimization algorithm; stochastic approximation; stock price; Approximation algorithms; Cities and towns; Convergence; Differential equations; Least squares methods; Mathematics; Parameter estimation; Pricing; Recursive estimation; Stochastic processes;
Conference_Titel :
American Control Conference, 2007. ACC '07
Conference_Location :
New York, NY
Print_ISBN :
1-4244-0988-8
Electronic_ISBN :
0743-1619
DOI :
10.1109/ACC.2007.4283130
