On stochastic approximation and American option pricing
Conference
64th ISI World Statistics Congress
Format: IPS Abstract
Session: IPS 216 - Recent theoretical and methodological advances in business, finance, and actuarial science
Wednesday 19 July 2 p.m. - 3:40 p.m. (Canada/Eastern)
Abstract
We consider almost sure convergence rates of averaged linear stochastic approximation algorithms, when applied to data with triangular dependence structure. We find that when the data is replaced by its running average in the algorithm, convergence may be faster. We then obtain rates of convergence of price estimates in the context of American option pricing via a dynamic programming algorithm with stochastic approximation. From a methodological point of view, our results show that using averaged data in the pricing algorithm leads speeds of convergence that are more robust to the choice of parameters.