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ABSTRACT: Estimating the current cost of an option by predicting the underlying asset prices is the most common methodology for pricing options. Pricing options has been a challenging problem for a long time due to unpredictability in market which gives rise to unpredictability in the option prices. Also the time when the options have to be exercised has to be determined to maximize the profits. This paper proposes an algorithm for predicting the time and price when the option can be exercised to gain expected profits. The proposed method is based on Nature inspired algorithm i.e. Ant Colony Optimization (ACO) which is used extensively in combinatorial optimization problems and dynamic applications such as mobile ad-hoc networks where the objective is to find the shortest path. In option pricing, the primary objective is to find the best node in terms of price and time that would bring expected profit to the investor. Ants traverse the solution space (asset price movements) in the market to identify a profitable node. We have designed and implemented an Aggregated ACO algorithm to price options which is distributed and robust. The initial results are encouraging and we are continuing this work further.
Parallel & Distributed Processing, 2009. IPDPS 2009. IEEE International Symposium on; 06/2009
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ABSTRACT: Option pricing is one of the challenging problems in finance. Finding the best time to exercise an option is a even more challenging problem, especially since the price of the underlying assets change rapidly. In this work, we study complex path dependent options by exploiting and extending a novel idea that we proposed earlier using a nature inspired meta-heuristic algorithm. ant colony optimization (ACO). ACO has been used extensively in combinatorial optimization problems and recently in dynamic applications such as mobile ad-hoc networks where the objective is find a shortest path. However, in finance, especially in option pricing, we look for best time to exercise an option. Specifically, we use ants to decide on the best time to exercise so that the holder of the option contract will get the maximum benefit from his/her investment. Our algorithm and implementation suggests a better way to price options than traditional techniques such as Monte Carlo simulation or binomial lattice algorithm. Our pricing results compare very well with other techniques and at the same time the computational cost is reduced to a large extent.
Evolutionary Computation, 2009. CEC '09. IEEE Congress on; 06/2009
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ABSTRACT: Communication and synchronization are two main latency issues in computing FFT on parallel architectures. Both latencies have to be either hidden or tolerated to achieve high performance. One approach to achieve this is by multithreading. Another approach to tolerate latency is to map data efficiently onto the processors' local memory and exploiting data locality. Indirect swap networks, an idea proposed in VLSI circuits can be efficiently used to compute the butterfly computations in FFT. Data mapping in the swap network topology reduces the communication overhead by half at each iteration. Cell broadband engine (Cell/B.E.)processor is a heterogeneous multicoreprocessor for stream data applications and high performance computing. Its eight SIMD processing elements, synergistic processor elements (SPEs), provide multi-folded parallelism. In this paper, we investigate the improved Cooley-Tukey FFT algorithm based on indirect swap network, and design the parallel algorithm taking into consideration all the features of the Cell/B.E. architecture. The performance results show that the new algorithm on Cell/B.E. is 3.7 faster than the cluster for 4K input data size and 6.4 faster than the cluster for 16K input data size at the processor level.
High Performance Computing Systems and Applications, 2008. HPCS 2008. 22nd International Symposium on; 07/2008
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ABSTRACT: Finite-Difference Time-Domain (FDTD) is a kernel used to solve problems in electromagnetics applications such as microwave tomography. It is a data-intensive and computation-intensive problem. However, its computation scheme indicates that an architecture with SIMD support has the potential to bring performance improvement over traditional architectures without SIMD support. The Cell Broadband Engine (Cell/B.E.) processor is an implementation of a heterogeneous multicore architecture. It consists of one conventional microprocessor, PowerPC Processor Element (PPE), and eight SIMD co-processor elements, Synergistic Processor Elements (SPEs). One unique feature of an SPE is that it has 128-entry 128-bit uniform registers which support SIMD. Therefore, FDTD may be mapped well on Cell/B.E. processor. However, each SPE can directly access only 256KB local store (LS) both for instructions and data. The size ofLS is much less than what is needed for an accurate simulation of FDTD which requires large number of fine-grained Yee cells. In this paper, we design the algorithm on Cell/B.E. by efficiently using the asynchronous DMA (direct memory access) mechanism available on an SPE transferring data between its LS and the main memory via the high bandwidth bus on-chip EIB (Element Interconnect Bus). The new algorithm was run on an IBM Blade QS20 blades running at 3.2GHz. For a computation domain of 600 x 600 Yee cells, we achieve an overall speedup of 14.14 over AMD Athlon and 7.05 over AMD Opteron at the processor level.
Parallel and Distributed Processing, 2008. IPDPS 2008. IEEE International Symposium on; 05/2008
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ABSTRACT: Mobile ad hoc networks (MANETS) are infrastructureless network consisting of mobile nodes, with constantly changing topologies, that communicate via a wireless medium. Therefore, routing is a challenging issue in MANETs. Recently, nature inspired algorithms have been explored as means of finding an efficient solution to this routing problem. In this paper, we develop an improved routing algorithm for MANETs based on ant colony optimization (ACO) inspired by real ants. The performance of the routing algorithm is evaluated through simulation and is compared to an existing well known MANET routing protocol, ad hoc on-demand distance vector (AODV). Several performance metrics are considered in different scenarios with varying mobility levels and traffic load.
Advanced Information Networking and Applications, 2008. AINA 2008. 22nd International Conference on; 04/2008
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ABSTRACT: This paper addresses the nonlinear tomographic image reconstruction problem with particular emphasis on developing efficient numerical algorithms for early breast cancer detection using parallel algorithms to enhance both the speed and quality of the recovered images. Our goal is to illustrate an effective method of microwave imaging for early stage breast cancer detection, using parallel finite-difference time domain method (PFDTD) and parallel genetic algorithms (PGAs) optimization, by using message passing interface (MPI) library.
Antennas and Propagation Society International Symposium, 2007 IEEE; 07/2007
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ABSTRACT: Monte Carlo (MC) simulation is one of the popular approaches for approximating the value of options and other derivative securities due to the absence of straightforward closed form solutions for many financial models. However, the slow convergence rate, O(N<sup>- 1</sup>2/) for N number of samples of the MC method has motivated research in quasi Monte-Carlo (QMC) techniques. QMC methods use low discrepancy (LD) sequences that provide faster, more accurate results than MC methods. In this paper, we focus on the parallelization of the QMC method on a heterogeneous network of workstations (HNOWs) for option pricing. HNOWs are machines with different processing capabilities and have distinct execution time for the same task. It is therefore important to allocate and schedule the tasks depending on the performance and resources of these machines. We present an adaptive, distributed QMC algorithm for option pricing, taking into account the performances of both processors and communications. The algorithm distributes data and computations based on the architectural features of the available processors at run time. We implement the algorithm using mpC, an extension of ANSI C language for parallel computation on heterogeneous networks. We compare and analyze the performance results with different parallel implementations. The results of our algorithm demonstrate a good performance on heterogenous parallel platforms.
Simulation Symposium, 2006. 39th Annual; 05/2006
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ABSTRACT: We focus on the neural network training problem that could be used for price forecasting or other purposes in finance. We design and develop four different parallel and multithreaded backpropagation neural network algorithms: neuron and training set parallelism on a distributed memory architecture using MPI; loop-level (fine-grain) and coarse-grained parallelism in shared memory architecture using OpenMP. We have conducted various experiments to study the performance of these algorithms and compared our results with a traditional autoregression model to establish accuracy of our results. The comparison between our MPI and OpenMP results suggest that the training set parallelism performs better than all the other types of parallelism considered in the study.
Parallel Processing Workshops, 2003. Proceedings. 2003 International Conference on; 11/2003
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ABSTRACT: Efficient data distribution is important to overcome latencies in distributed memory multiprocessors. In this paper we have studied the distributed implementation of the FFT algorithm using the ISN topology to improve data locality. The algorithm is implemented on the Beowulf clusters using MPI. We obtain 20% better performance compared to the butterfly network.
Electrical and Computer Engineering, 2003. IEEE CCECE 2003. Canadian Conference on; 06/2003
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ABSTRACT: In this paper, we develop a multithreaded algorithm for pricing simple options and implement it on a 8 node SMP machine using MIT's supercomputer programming language Cilk. The algorithm dynamically creates lots of threads to exploit parallelism and relies on the Cilk runtime system to distribute the computation load. We present both analytical and experimental results and our results explain how Cilk could be used effectively to exploit parallelism in the given problem. The analytical results show that our algorithm has a very high average parallelism and hence Cilk is the target paradigm to implement the algorithm. We conclude from our implementation results that the size of the threads, the number of threads created, the load balancer the cost of spawning a thread are parameters that must be considered while designing the algorithm on the Cilk platform.
High Performance Computing Systems and Applications, 2002. Proceedings. 16th Annual International Symposium on; 02/2002
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ABSTRACT: We propose a framework for mobility-aware pro-active low energy (MAPLE) clustering in ad hoc mobile wireless networks. Most of the clustering approaches proposed in the literature primarily focus on the algorithmic aspects of clustering without considering the practical implementation issues, and these are often reactive in nature. The proposed approach addresses the problem of clustering in a medium-access control framework and enables pro-active and energy-efficient clustering exploiting the node mobility information. In particular, for pro-active clustering, we introduce a method to exploit the link level information to estimate the mobility pattern of the wireless nodes and the cost of using a wireless link in terms of required transmission power. A channel reservation technique is used to reduce the number of contentions among the nodes while accessing the channel during cluster formation. Simulation results show that the proposed framework results in superior clustering performance in terms of control overhead, average number of link failures and load distribution compared to other clustering approaches proposed in the literature, such as the LCC-LID (least cluster change lowest ID)-based clustering.
Global Telecommunications Conference, 2004. GLOBECOM '04. IEEE;
