Abstract-A number of different numerical methods for accelerating financial option pricing using FPGAs have recently been investigated, such as Monte-Carlo, finite-difference, quadrature, and binomial trees. However, these papers only compare acceleration of each method against the same method

costs, energy efficient and accurate pricing of these models becomes more and more important. In this paper we present- to the best of our knowledge- the first FPGA based accelerator for option pricing with the state-of-the-art Heston model. It is based on advanced Monte Carlo simulations. Compared

of individual rewards is to compare them in choice behavior. Given two options, I would choose the one that at this moment has the higher value for me. Give me the choice between a one-dollar bill and an apple, and you will see which one I prefer and thus my action will tell you whether the value of the apple

Abstract—Quadrature based methods for numerical integration provide a means of quickly and accurately pricing financial products such as options. These methods can be applied to multidimensional products, such as options on multiple underlying assets, but suffer from an exponential increase

accelerate financial derivative calculations is becoming very common. In this work, we implement an FPGA-based specific processor for European option pricing using Monte Carlo simu lations, and we compare its performance and power dissipation to the execution on a CPU. The experimental results show

high-level approach has been chosen, using Altera’s implementation of the OpenCL standard, to answer this issue. We present two FPGA implementations of the binomial option pricing model on American options. The results obtained on a Terasic DE4- Stratix IV board form a solid basis to hold all

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

Abstract Exotic options are popular financial derivatives that play essential roles in financial markets. How to price them efficiently and accurately is very important both in theory and practice. The lattice model is usually used to price them. The prices computed by the lattice converge

sense optimal. These are compared with the usual delta hedging approach and found to differ significantly. I also derive the natural generalization of the Black-Scholes option pricing formula when the underlying security is modeled by a geometric TLP. This generalization would not be possible without

. This GH model is a generalization of the hyperbolic model developed by Eberlein and Keller (1995). It is incomplete. We derive an option pricing formula for GH driven models using the Esscher transform as martingale measure and compare the prices with classical Black-Scholes prices. The objective