For more information about this meeting, contact Anna Mazzucato, Manfred Denker, Victor Nistor.
|Title:||Bayesian nonparametric approaches for financial option pricing|
|Seminar:||Probability and Financial Mathematics Seminar|
|Speaker:||Wen Teng, National Central University, Taiwan|
|The price of a financial option equals the discounted expected payoff of the option under the risk-neutral measure. The density that reproduces the observed option price is called the risk-neutral or state price density and is used for a variety of important activities in finance, including providing an arbitrage-free tool for pricing complex and less liquid securities. The importance of understanding this density with respect to asset pricing and risk management has led to a competing number of approaches for making inference about the state price density. We start by proposing a finite-dimensional model for the state price density in a Bayesian framework. This modeling approach can be viewed as a Bayesian Quadrature model, where the locations and weights of support points in the finite-dimensional representation of the risk-neutral density are random variables. We assess the performance of the proposed model using simulation studies based on synthetic data and then by contrasting the method with a number of competing methods using S&P 500 index option data. In contrast to European options, American options can be exercised anytime prior to maturity. We show how our Bayesian Quadrature approach can be extended to make inference for American options. To tackle this problem, we propose a Bayesian implied random tree model as an extension of the Bayesian Quadrature approach by building a unique binomial tree similar to Rubinstein (1994). The benefits of our approach are demonstrated via simulation study and empirical studies using S&P 100 index option data.|
Room Reservation Information
|Date:||02 / 01 / 2011|
|Time:||04:00pm - 05:00pm|