Delay geometric Brownian motion in financial option valuation

Xuerong Mao, Sotirios Sabanis

Research output: Contribution to journalArticle

9 Citations (Scopus)
161 Downloads (Pure)

Abstract

Motivated by influential work on complete stochastic volatility models, such as Hobson and Rogers [11], we introduce a model driven by a delay geometric Brownian motion (DGBM) which is described by the stochastic delay differential equation dSðtÞ ¼ mðSðt 2tÞÞSðtÞdt þ VðSðt 2tÞÞSðtÞdWðtÞ. We show that the equation has a unique positive solution under a very general condition, namely that the volatility function V is a continuous mapping from Rþ to itself. Moreover, we show that the delay effect is not too sensitive to time lag changes. The desirable robustness of the delay effect is demonstrated on several important financial derivatives as well as on the value process of the underlying asset. Finally, we introduce an Euler–Maruyama numerical scheme for our proposed model and show that this numerical method approximates option prices very well. All these features show that the proposedDGBMserves as a rich alternative in modelling financial instruments in a complete market framework.
Original languageEnglish
Pages (from-to)295-320
Number of pages26
JournalStochastics: An International Journal of Probability and Stochastic Processes
Volume85
Issue number2
Early online date15 Mar 2012
DOIs
Publication statusPublished - 2013

Fingerprint

Option Valuation
Geometric Brownian Motion
Brownian movement
Stochastic models
Stochastic Delay Differential Equations
Financial Derivatives
Financial Modeling
Numerical methods
Differential equations
Stochastic Volatility Model
Time Lag
Derivatives
Volatility
Numerical Scheme
Positive Solution
Numerical Methods
Robustness
Alternatives
Model

Keywords

  • stochastic delay differential equations
  • derivative pricing
  • Euler-Maruyama
  • local Lipschitz condition
  • strong covnergence

Cite this

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Delay geometric Brownian motion in financial option valuation. / Mao, Xuerong; Sabanis, Sotirios.

In: Stochastics: An International Journal of Probability and Stochastic Processes , Vol. 85, No. 2, 2013, p. 295-320.

Research output: Contribution to journalArticle

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