Computational exploration of the biological basis of black-scholes expected utility function

Sukanto Bhattacharya, Kuldeep Kumar

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Abstract

It has often been argued that there exists an underlying biological basis of utility functions. Taking this line of argument a step further in this paper, we have aimed to computationally demonstrate the biological basis of the Black-Scholes functional form as applied to classical option pricing and hedging theory. The evolutionary optimality of the classical Black-Scholes function has been computationally established by means of a haploid genetic algorithm model. The objective was to minimize the dynamic hedging error for a portfolio of assets that is built to replicate the payoff from a European multi-asset option. The functional form that is seen to evolve over successive generations which best attains this optimization objective is the classical Black-Scholes function extended to a multiasset scenario.

Original languageEnglish
Article number39460
Number of pages15
JournalJournal of Applied Mathematics and Decision Sciences
Volume2007
DOIs
Publication statusPublished - 2007

Fingerprint

Black-Scholes
Expected Utility
Utility Function
Hedging
Option Pricing
Optimality
Genetic algorithms
Genetic Algorithm
Minimise
Scenarios
Optimization
Line
Demonstrate
Utility function
Expected utility
Costs
Form
Assets
Functional form
Model

Cite this

@article{b312be7ff5374f4ca0949b46992cbafe,
title = "Computational exploration of the biological basis of black-scholes expected utility function",
abstract = "It has often been argued that there exists an underlying biological basis of utility functions. Taking this line of argument a step further in this paper, we have aimed to computationally demonstrate the biological basis of the Black-Scholes functional form as applied to classical option pricing and hedging theory. The evolutionary optimality of the classical Black-Scholes function has been computationally established by means of a haploid genetic algorithm model. The objective was to minimize the dynamic hedging error for a portfolio of assets that is built to replicate the payoff from a European multi-asset option. The functional form that is seen to evolve over successive generations which best attains this optimization objective is the classical Black-Scholes function extended to a multiasset scenario.",
author = "Sukanto Bhattacharya and Kuldeep Kumar",
year = "2007",
doi = "10.1155/2007/39460",
language = "English",
volume = "2007",
journal = "Journal of Applied Mathematics and Decision Sciences",
issn = "2090-3359",
publisher = "Hindawi Publishing Corporation",

}

Computational exploration of the biological basis of black-scholes expected utility function. / Bhattacharya, Sukanto; Kumar, Kuldeep.

In: Journal of Applied Mathematics and Decision Sciences, Vol. 2007, 39460, 2007.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Computational exploration of the biological basis of black-scholes expected utility function

AU - Bhattacharya, Sukanto

AU - Kumar, Kuldeep

PY - 2007

Y1 - 2007

N2 - It has often been argued that there exists an underlying biological basis of utility functions. Taking this line of argument a step further in this paper, we have aimed to computationally demonstrate the biological basis of the Black-Scholes functional form as applied to classical option pricing and hedging theory. The evolutionary optimality of the classical Black-Scholes function has been computationally established by means of a haploid genetic algorithm model. The objective was to minimize the dynamic hedging error for a portfolio of assets that is built to replicate the payoff from a European multi-asset option. The functional form that is seen to evolve over successive generations which best attains this optimization objective is the classical Black-Scholes function extended to a multiasset scenario.

AB - It has often been argued that there exists an underlying biological basis of utility functions. Taking this line of argument a step further in this paper, we have aimed to computationally demonstrate the biological basis of the Black-Scholes functional form as applied to classical option pricing and hedging theory. The evolutionary optimality of the classical Black-Scholes function has been computationally established by means of a haploid genetic algorithm model. The objective was to minimize the dynamic hedging error for a portfolio of assets that is built to replicate the payoff from a European multi-asset option. The functional form that is seen to evolve over successive generations which best attains this optimization objective is the classical Black-Scholes function extended to a multiasset scenario.

UR - http://www.scopus.com/inward/record.url?scp=34247394086&partnerID=8YFLogxK

U2 - 10.1155/2007/39460

DO - 10.1155/2007/39460

M3 - Article

VL - 2007

JO - Journal of Applied Mathematics and Decision Sciences

JF - Journal of Applied Mathematics and Decision Sciences

SN - 2090-3359

M1 - 39460

ER -