gpusvcalibration: A R Package for Fast Stochastic Volatility Model Calibration Using GPUs

10 Pages Posted: 13 Feb 2014

See all articles by Matthew Francis Dixon

Matthew Francis Dixon

Illinois Institute of Technology

Sabbir Khan

Old Dominion University

Mohammed Zubair

Old Dominion University

Date Written: February 11, 2014

Abstract

In this paper we describe the gpusvcalibration R package for accelerating stochastic volatility model calibration on GPUs. The package is designed for use with existing CRAN packages for optimization such as DEOptim and nloptr. Stochastic volatility models are used extensively across the capital markets for pricing and risk management of exchange traded financial options. However, there are many challenges to calibration, including comparative assessment of the robustness of different models and optimization routines. For example, we observe that when fitted to sub-minute level mid-market quotes, models require frequent calibration every few minutes and the quality of the fit is routine sensitive.

The R statistical software environment is popular with quantitative analysts in the financial industry partly because it facilitates application design space exploration. However, a typical R based implementation of a stochastic volatility model calibration on a CPU does not meet the performance requirements for sub-minute level trading, i.e. mid to high frequency trading. We identified the most computationally intensive part of the calibration process in R and off-loaded that to the GPU. We created a map-reduce interface to the computationally intensive kernel so that it can be easily integrated in a variety of R based calibration codes using our package. We demonstrate that the new R based implementation using our package is comparable in performance to a C/C++ GPU based calibration code.

Suggested Citation

Dixon, Matthew Francis and Khan, Sabbir and Zubair, Mohammed, gpusvcalibration: A R Package for Fast Stochastic Volatility Model Calibration Using GPUs (February 11, 2014). Available at SSRN: https://ssrn.com/abstract=2394306 or http://dx.doi.org/10.2139/ssrn.2394306

Matthew Francis Dixon (Contact Author)

Illinois Institute of Technology ( email )

Department of Math
W 32nd St., E1 room 208, 10 S Wabash Ave, Chicago,
Chicago, IL 60616
United States

Sabbir Khan

Old Dominion University ( email )

Norfolk, VA 23529-0222
United States

Mohammed Zubair

Old Dominion University ( email )

Norfolk, VA 23529-0222
United States

Register to save articles to
your library

Register

Paper statistics

Downloads
175
Abstract Views
953
rank
169,219
PlumX Metrics