An efficient implementation of a least squares Monte Carlo method for valuing American-style options

Jonen, Christian

doi:10.1080/00207160802647357

Cited by 18 publications

(5 citation statements)

References 34 publications

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“…To tackle this problem, we then implement the CPU program of Jonen's approach (see ) and our improved CPU–GPU program (Algorithm 3) with efficient memory management. Regarding the pricing accuracy, we present the results from the improved approaches with efficient memory management in Table .…”

Section: Numerical Resultsmentioning

confidence: 99%

“…Hence, instead of directly implementing the GPU program of Algorithm 1, we can use Jonen's approach to improve the memory usage in the Longstaff–Schwartz method.…”

Section: Programming Techniques For Option Pricingmentioning

confidence: 99%

“…In , Jonen introduced a vector of optimal stopping time indicators τ k ( S k ) for each simulation path. At the terminal time t M = T , the indicator ; for exercise time t i ( i = M − 1,…,1), we have: where is the immediate exercise value and is the estimated expected continuation value.…”

Section: Programming Techniques For Option Pricingmentioning

confidence: 99%

“…Moreover, in order to compute the large cash flow matrix, which is stored in the GPU global memory, we need to read and write the global memory frequently; therefore, the efficiency of GPU computing suffers. Hence, instead of directly implementing the GPU program of Algorithm 1, we can use Jonen's approach [14] to improve the memory usage in the Longstaff-Schwartz method.…”

Section: Improved Cpu-gpu Implementation With Efficient Memory Managementioning

confidence: 99%

“…In [14], Jonen introduced a vector of optimal stopping time indicators k .S k / for each simulation path. At the terminal time t M D T , the indicator k .S k t M / D M ; for exercise time t i (i D M 1, : : : , 1), we have: 875 By using this discounted cash flow vector, we do not need the optimal stopping time indicator anymore and we can save memory.…”

Section: Improved Cpu-gpu Implementation With Efficient Memory Managementioning

confidence: 99%

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Pricing barrier and American options under the SABR model on the graphics processing unit

Tian

Zhu

Klebaner

et al. 2011

Concurrency and Computation

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SUMMARY In this paper, we presented our study on using the graphics processing unit (GPU) to accelerate the computation in pricing financial options. We first introduced the GPU programming and the SABR stochastic volatility model. We then discussed pricing options with quasi Monte Carlo techniques under the SABR model. In particular, we focused on pricing barrier options by quasi Monte Carlo and conditional probability correction methods and on pricing American options by the least squares Monte Carlo method. We then presented our GPU‐based implementation for pricing barrier options and hybrid CPU–GPU implementation for pricing American options. In addition, we described techniques for efficient use of GPU memory. We provided details of implementing these GPU numerical schemes for pricing options and compared performances of the GPU programs with their CPU counterparts. We found that GPU‐based computing schemes can achieve 134 times speedup for pricing barrier options, while maintaining satisfactory pricing accuracy. For pricing American options, we also reported that when the least squares Monte Carlo method is used, special techniques can be devised to use less GPU memory, resulting in 22 times speedup, instead of the original 10 times speedup. Copyright © 2011 John Wiley & Sons, Ltd.

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Section: Numerical Resultsmentioning

confidence: 99%

“…Hence, instead of directly implementing the GPU program of Algorithm 1, we can use Jonen's approach to improve the memory usage in the Longstaff–Schwartz method.…”

Section: Programming Techniques For Option Pricingmentioning

confidence: 99%