Reconfigurable Control Variate Monte-Carlo Designs for Pricing Exotic Options

Tse, Anson H.T.; Thomas, David B.; Tsoi, Kuen Hung; Luk, Wayne

doi:10.1109/fpl.2010.46

Cited by 14 publications

(7 citation statements)

References 7 publications

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“…Therefore, apart from simulating the payoff of the Asian option, the payoff of a correlated European option is also simulated at the same time using the control variate MonteCarlo (CVMC) algorithm [12], [13].…”

Section: A Asian Option Pricing Using Control Variate Methodsmentioning

confidence: 99%

Dynamic scheduling Monte-Carlo framework for multi-accelerator heterogeneous clusters

Tse

Thomas

Tsoi

et al. 2010

2010 International Conference on Field-Programmable Technology

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Monte-Carlo (MC) simulation is an effective tool for solving complex problems such as many-body simulation, exotic option pricing and partial differential equation solving. The huge amount of computation in MC makes it a good candidate for acceleration using hardware and distributed computing platforms. We propose a novel MC simulation framework suitable for a wide range of problems. This framework enables different hardware accelerators in a multi-accelerator heterogeneous cluster to work collaboratively on a single application. It also provides scheduling interfaces to adaptively balance the workload according to the cluster status. Two financial applications, involving asset simulation and option pricing, are built using this framework to demonstrate its capability and flexibility. A cluster with 8 Virtex-5 xc5vlx330t FPGAs and 8 Tesla C1060 GPUs using the proposed framework provides 44 times speedup and 19.6 times improved energy efficiency over a cluster with 16 AMD Phenom 9650 quad-core 2.4GHz CPUs for the GARCH asset simulation application. The Efficient Allocation Line (EAL) is proposed for determining the most efficient allocation of accelerators for either performance or energy consumption.

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Section: A Asian Option Pricing Using Control Variate Methodsmentioning

confidence: 99%

Dynamic scheduling Monte-Carlo framework for multi-accelerator heterogeneous clusters

Tse

Thomas

Tsoi

et al. 2010

2010 International Conference on Field-Programmable Technology

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“…Note that we do not show the comparison with the results reported in [9] as there is no well-defined benchmark to relate the performance and accuracy for Asian options, although other options have been compared in [10].…”

Section: Implementation and Evaluationmentioning

confidence: 95%

Unifying Finite Difference Option-Pricing for Hardware Acceleration

Jin

Luk

Thomas

2011

2011 21st International Conference on Field Programmable Logic and Applications

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Explicit finite difference method is widely used in finance for pricing many kinds of options. Its regular computational pattern makes it an ideal candidate for acceleration using reconfigurable hardware. However, because the corresponding hardware designs must be optimised both for the specific option and the target platform, it is challenging and time consuming to develop all the designs efficiently. This paper presents a framework for describing and automatically implementing financial explicit finite difference procedures in reconfigurable hardware, allowing parallelised fully pipelined implementations to be created from high-level mathematical expressions. The proposed methodology is demonstrated using three option pricing problems. Our results show that the implementation generated by our framework on a Virtex-6 device at 310MHz is more than 24 times faster than a software implementation fully optimised by the Intel compiler on a four-core Xeron CPU at 2.66GHz. In addition, the latency of the FPGA solvers is up to 90 times lower than the corresponding software solvers.

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“…Many types of options, such as the widely-traded American options, have no analytical solutions and are priced using numerical methods. For instance, when the contract has complicated exotic features or there are multiple sources of uncertainty in modelling the asset price, Monte Carlo methods are often used [9]- [12]. These methods have the main disadvantage of being slow compared to other numerical models for American-style options [13], while also early exercise features are either difficult or impossible to implement using Monte Carlo methods [14].…”

Section: Background a The Binomial-tree Option Pricing Modelmentioning

confidence: 99%

A High-Level Design Framework for the Automatic Generation of High-Throughput Systolic Binomial-Tree Solvers

Tavakkoli¹,

Thomas²

2018

IEEE Trans. VLSI Syst.

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Abstract-The binomial-tree model is a numerical method widely used in finance with a computational complexity which is quadratic with respect to the solution accuracy. Existing research has employed reconfigurable computing to provide faster solutions compared to general-purpose processors, but they require low-level manual design by a hardware engineer, and can only solve American options. This paper presents a formal mathematical framework that captures a large class of binomial-tree problems, and provides a systolic data-movement template that maps the framework into digital hardware. The article also presents a fully-automated design flow, which takes C-level user descriptions of binomial trees, with custom data types and tree operations, and automatically generates fullypipelined reconfigurable hardware solutions in FPGA bit-stream files. On a Xilinx Virtex-7 xc7vx980t FPGA at a 100-MHz clock frequency, we require 54-µs latency to solve three 876-step 32-bit fixed-point American option binomial trees, with a pricing rate of 114k trees/s. From the same device and in comparison to existing solutions with equivalent FPGA technology, we always achieve better throughput. This ranges from 1.4× throughput compared to a hand-tuned register-transfer level systolic design, to 9.1× and 5.6× improvement with respect to scalar and vector architectures, respectively.

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Reconfigurable Control Variate Monte-Carlo Designs for Pricing Exotic Options

Cited by 14 publications

References 7 publications

Dynamic scheduling Monte-Carlo framework for multi-accelerator heterogeneous clusters

Dynamic scheduling Monte-Carlo framework for multi-accelerator heterogeneous clusters

Unifying Finite Difference Option-Pricing for Hardware Acceleration

A High-Level Design Framework for the Automatic Generation of High-Throughput Systolic Binomial-Tree Solvers

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