Parallel computing in Asian option pricing

Sak, Halis; Özekıcı, Süleyman; Boduroğlu, İ. İlkay

doi:10.1016/j.parco.2006.11.002

“…For the WR solution, many works are related to convergence theory, acceleration methods, and parallel implementation. In the same time, to decrease computation time, many researchers proposed parallelization in finance, see, Sak et al (2007). Schwarz WR methods are easy parallel methods, and converge faster than the nomal Schwarz methods (Gander 2006).…”

Section: Schwarz Waveform Relaxation For Fbsdes In Space-timementioning

confidence: 99%

Finite Difference Methods for the BSDEs in Finance

Guo

¹

2018

IJFS

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This paper gives a review of numerical methods for solving the BSDEs, especially, finite difference methods. For numerical methods of finite difference, we should divide them into three branches. Distributed method (or parallel method) should now become a hot topic. It is a key reason we present the review. We give a brief survey on the financial problems. The problems include solution and simulation methods for the BSDEs. We first describe the BSDEs, and then outline the main techniques and main results of the BSDEs. In addition, we compare with the errors between these methods and the Euler method on the BSDEs.

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“…Prasain et al [2010] proposed a parallel synchronous option pricing algorithm to price simple European options using particle swarm optimisation: a nature-inspired global search algorithm based on swarm intelligence. Sak et al [2007] discussed the application of parallel computing in pricing backward-starting fixed strike Asian options that are continuously averaged. Through a change of numeraire they transformed the pricing problem into solving a one-statevariable partial differential equation (PDE) by both explicit and Crank-Nicolson's implicit finite-difference methods.…”

Section: Related Workmentioning

confidence: 99%

Parallel Binomial American Option Pricing under Proportional Transaction Costs

Zhang

¹

,

Roux

²

,

Zastawniak

³

2012

AM

2

0

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We present a parallel algorithm that computes the ask and bid prices of an American option when proportional transaction costs apply to trading in the underlying asset. The algorithm computes the prices on recombining binomial trees, and is designed for modern multi-core processors. Although parallel option pricing has been well studied, none of the existing approaches takes transaction costs into consideration. The algorithm that we propose partitions a binomial tree into blocks. In any round of computation a block is further partitioned into regions which are assigned to distinct processors. To minimise load imbalance the assignment of nodes to processors is dynamically adjusted before each new round starts. Synchronisation is required both within a round and between two successive rounds. The parallel speedup of the algorithm is proportional to the number of processors used. The parallel algorithm was implemented in C/C++ via POSIX Threads, and was tested on a machine with 8 processors. In the pricing of an American put option, the parallel speedup against an efficient sequential implementation was 5.26 using 8 processors and 1500 time steps, achieving a parallel efficiency of 65.75%

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“…Sak et al [31] discussed the application of parallel computing in pricing backward-starting fixed strike Asian options that are continuously averaged. Through a change of numeraire they transformed the pricing problem into solving a one-state-variable partial differential equation (PDE) by both explicit and Crank-Nicolson's implicit finite-difference methods.…”

Section: = 8192 Nmentioning

confidence: 99%

Parallel Binomial American Option Pricing under Proportional Transaction Costs

Zhang¹

2012

AM

0

View full text Add to dashboard Cite

We present a parallel algorithm that computes the ask and bid prices of an American option when proportional transaction costs apply to trading in the underlying asset. The algorithm computes the prices on recombining binomial trees, and is designed for modern multi-core processors. Although parallel option pricing has been well studied, none of the existing approaches takes transaction costs into consideration. The algorithm that we propose partitions a binomial tree into blocks. In any round of computation a block is further partitioned into regions which are assigned to distinct processors. To minimise load imbalance the assignment of nodes to processors is dynamically adjusted before each new round starts. Synchronisation is required both within a round and between two successive rounds. The parallel speedup of the algorithm is proportional to the number of processors used. The parallel algorithm was implemented in C/C++ via POSIX Threads, and was tested on a machine with 8 processors. In the pricing of an American put option, the parallel speedup against an efficient sequential implementation was 5.26 using 8 processors and 1500 time steps, achieving a parallel efficiency of 65.75%.

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Parallel computing in Asian option pricing

Cited by 18 publications

References 17 publications

Finite Difference Methods for the BSDEs in Finance

Finite Difference Methods for the BSDEs in Finance

Parallel Binomial American Option Pricing under Proportional Transaction Costs

Parallel Binomial American Option Pricing under Proportional Transaction Costs

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