Fractional black-scholes models: complete mle with application to fractional option pricing

Geometric fractional Brownian motion (GFBM) is an extended model of the traditional geometric Brownian motion that is widely used for Black-Scholes option pricing. By considering GFBM, we are now able to capture the memory dependency. This method will enable us to derive the estimators of the drift,...

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Main Authors: Misiran, Masnita, Lu, Z., Teo, Kok Lay
Other Authors: Honglei Xu
Format: Conference Paper
Published: Guizhou University 2010
Subjects:
Online Access:http://hdl.handle.net/20.500.11937/37460
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author Misiran, Masnita
Lu, Z.
Teo, Kok Lay
author2 Honglei Xu
author_facet Honglei Xu
Misiran, Masnita
Lu, Z.
Teo, Kok Lay
author_sort Misiran, Masnita
building Curtin Institutional Repository
collection Online Access
description Geometric fractional Brownian motion (GFBM) is an extended model of the traditional geometric Brownian motion that is widely used for Black-Scholes option pricing. By considering GFBM, we are now able to capture the memory dependency. This method will enable us to derive the estimators of the drift, μ, volatility, !2, and also the index of self similarity, H, simultaneously. This will enable us to use the fractional Black-Scholes model with all the needed parameters. Simulation outcomes illustrate that our methodology is efficient and reliable. Empirical application to stock exchange index with option pricing under GFBM is also made.
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format Conference Paper
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T08:50:12Z
publishDate 2010
publisher Guizhou University
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spelling curtin-20.500.11937-374602023-01-18T08:46:44Z Fractional black-scholes models: complete mle with application to fractional option pricing Misiran, Masnita Lu, Z. Teo, Kok Lay Honglei Xu Xinmin Yang Wei Wei maximum likelihood estimation option pricing long memory geometric fractional Brownian motion Geometric fractional Brownian motion (GFBM) is an extended model of the traditional geometric Brownian motion that is widely used for Black-Scholes option pricing. By considering GFBM, we are now able to capture the memory dependency. This method will enable us to derive the estimators of the drift, μ, volatility, !2, and also the index of self similarity, H, simultaneously. This will enable us to use the fractional Black-Scholes model with all the needed parameters. Simulation outcomes illustrate that our methodology is efficient and reliable. Empirical application to stock exchange index with option pricing under GFBM is also made. 2010 Conference Paper http://hdl.handle.net/20.500.11937/37460 Guizhou University fulltext
spellingShingle maximum likelihood estimation
option pricing
long memory
geometric fractional Brownian motion
Misiran, Masnita
Lu, Z.
Teo, Kok Lay
Fractional black-scholes models: complete mle with application to fractional option pricing
title Fractional black-scholes models: complete mle with application to fractional option pricing
title_full Fractional black-scholes models: complete mle with application to fractional option pricing
title_fullStr Fractional black-scholes models: complete mle with application to fractional option pricing
title_full_unstemmed Fractional black-scholes models: complete mle with application to fractional option pricing
title_short Fractional black-scholes models: complete mle with application to fractional option pricing
title_sort fractional black-scholes models: complete mle with application to fractional option pricing
topic maximum likelihood estimation
option pricing
long memory
geometric fractional Brownian motion
url http://hdl.handle.net/20.500.11937/37460