※ [本文轉錄自 NCTU-STAT98G 看板 #1DrB_mHU ]
作者: wanting0605 (小雨) 看板: NCTU-STAT98G
標題: 5/20(五) 統研所專題演講
時間: Thu May 19 14:59:58 2011
題 目：Bayesian Nonparametric Approaches for Financial Option Pricing
主講人：鄧惠文教授(中央大學統計所)
時 間：100年5月20日(星期五)上午10:40-11:30
(上午10:20-10:40茶會於交大統計所429室舉行)
地 點：交大綜合一館427室
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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.
※ 編輯: wanting0605 來自: 140.113.252.176 (05/19 15:02)