Please use this identifier to cite or link to this item:
Title: A radial basis function approach to pricing and hedging options incorporating transaction costs
Keywords: Learning Networks, Radial Basis Function, Optimization, Option Hedging and Pricing, Transaction Costs
Issue Date: 17-Apr-2006
Citation: TING JEUM NGIT (2006-04-17). A radial basis function approach to pricing and hedging options incorporating transaction costs. ScholarBank@NUS Repository.
Abstract: Nonparametric methods of pricing options have been available for some time, providing a viable alternative to traditional parametric methods. A general class of methods known as learning networks has been making significant inroads in option pricing literature. This thesis will adopt McLoone's hybrid linear/nonlinear training algorithm in developing RBF network models for the purpose of pricing and hedging options. An empirical study was first conducted to determine the suitability of our RBF network models in recovering simulated Black-Scholes option prices. We then assessed their ability to replicate options without transaction costs using some predefined backward induction performance measures.In addition, we further implemented our RBF network models on the new self-financing hedging strategy developed by Lai & Lim (2004), which is based on minimizing the expected cumulative hedging error and additional transaction rebalancing costs. Leland's strategy of discrete, regular revision time replications via a readjusted delta was used to gauge the effectiveness of this new strategy using both RBF network and Black-Scholes deltas.
Appears in Collections:Master's Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
Final2.pdf6.03 MBAdobe PDF



Page view(s)

checked on Apr 19, 2019


checked on Apr 19, 2019

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.