Parallelized Local Volatility Estimation Using GP-GPU Hardware Acceleration

Craig C. Douglas; Hyoseop Lee; Dongwoo Sheen

doi:10.1109/ICISA.2010.5480362

Parallelized Local Volatility Estimation Using GP-GPU Hardware Acceleration

Craig C. Douglas, Hyoseop Lee, Dongwoo Sheen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We introduce an inverse problem for the local volatility model in option pricing. We solve the problem using the Levenberg-Marquardt algorithm and use the notion of the Fréchet derivative when calculating the Jacobian matrix. We analyze the existence of the Fréchet derivative and its numerical computation. To reduce the computational time of the inverse problem, a GP-GPU environment is considered for parallel computation. Numerical results confirm the validity and efficiency of the proposed method. ©2010 IEEE.

Original language	English (US)
Title of host publication	2010 International Conference on Information Science and Applications
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
ISBN (Print)	9781424459421
DOIs	https://doi.org/10.1109/ICISA.2010.5480362
State	Published - 2010
Externally published	Yes

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10.1109/ICISA.2010.5480362

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@inproceedings{a05cbeaecbd84ddf82687eb4fe3bd28c,

title = "Parallelized Local Volatility Estimation Using GP-GPU Hardware Acceleration",

abstract = "We introduce an inverse problem for the local volatility model in option pricing. We solve the problem using the Levenberg-Marquardt algorithm and use the notion of the Fr{\'e}chet derivative when calculating the Jacobian matrix. We analyze the existence of the Fr{\'e}chet derivative and its numerical computation. To reduce the computational time of the inverse problem, a GP-GPU environment is considered for parallel computation. Numerical results confirm the validity and efficiency of the proposed method. {\textcopyright}2010 IEEE.",

author = "Douglas, {Craig C.} and Hyoseop Lee and Dongwoo Sheen",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): KUS-C1-016-04 Acknowledgements: This research was supported in part by NSF grants ACI-0305466 and CNS-0720454 and Award No. KUS-C1-016-04,made by King Abdullah University of Science and Technology(KAUST) and NRF grants 2009-0080533 and NRF 2008-C00043. This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

year = "2010",

doi = "10.1109/ICISA.2010.5480362",

language = "English (US)",

isbn = "9781424459421",

booktitle = "2010 International Conference on Information Science and Applications",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

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T1 - Parallelized Local Volatility Estimation Using GP-GPU Hardware Acceleration

AU - Douglas, Craig C.

AU - Lee, Hyoseop

AU - Sheen, Dongwoo

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): KUS-C1-016-04 Acknowledgements: This research was supported in part by NSF grants ACI-0305466 and CNS-0720454 and Award No. KUS-C1-016-04,made by King Abdullah University of Science and Technology(KAUST) and NRF grants 2009-0080533 and NRF 2008-C00043. This publication acknowledges KAUST support, but has no KAUST affiliated authors.

PY - 2010

Y1 - 2010

N2 - We introduce an inverse problem for the local volatility model in option pricing. We solve the problem using the Levenberg-Marquardt algorithm and use the notion of the Fréchet derivative when calculating the Jacobian matrix. We analyze the existence of the Fréchet derivative and its numerical computation. To reduce the computational time of the inverse problem, a GP-GPU environment is considered for parallel computation. Numerical results confirm the validity and efficiency of the proposed method. ©2010 IEEE.

AB - We introduce an inverse problem for the local volatility model in option pricing. We solve the problem using the Levenberg-Marquardt algorithm and use the notion of the Fréchet derivative when calculating the Jacobian matrix. We analyze the existence of the Fréchet derivative and its numerical computation. To reduce the computational time of the inverse problem, a GP-GPU environment is considered for parallel computation. Numerical results confirm the validity and efficiency of the proposed method. ©2010 IEEE.

UR - http://hdl.handle.net/10754/599137

UR - http://ieeexplore.ieee.org/document/5480362/

UR - http://www.scopus.com/inward/record.url?scp=77954451098&partnerID=8YFLogxK

U2 - 10.1109/ICISA.2010.5480362

DO - 10.1109/ICISA.2010.5480362

M3 - Conference contribution

SN - 9781424459421

BT - 2010 International Conference on Information Science and Applications

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Parallelized Local Volatility Estimation Using GP-GPU Hardware Acceleration

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