题　目：Bayesian Nonparametric Approaches for Financial Option Pricing 主讲人：邓惠文教授(中央大学统计所) 时　间：100年5月20日(星期五)上午10:40-11:30 (上午10:20-10:40茶会於交大统计所429室举行) 地　点：交大综合一馆427室 -- Abstract 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 state price density. 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 any time prior to maturity, and are therefore more frequently traded in practice. However, to the best of our knowledge, there exist no non-parametric approaches for calibrating the state price density using American options. Motivated by this problem, we propose a Bayesian implied random tree model which is capable of pricing American and other complex path-dependent options. The benefits of our approach are demonstrated via simulation study and empirical studies using S&P 100 index option data. --

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