By Christian De Schryver
ISBN-10: 3319154060
ISBN-13: 9783319154060
ISBN-10: 3319154079
ISBN-13: 9783319154077
This e-book covers the most recent techniques and effects from reconfigurable computing architectures hired within the finance area. So-called field-programmable gate arrays (FPGAs) have already proven to outperform normal CPU- and GPU-based computing architectures by way of a ways, saving as much as ninety nine% of power counting on the compute projects. well known authors from monetary arithmetic, desktop structure and finance enterprise introduce the readers into today’s demanding situations in finance IT, illustrate the main complicated methods and use circumstances and current at the moment recognized methodologies for integrating FPGAs in finance structures including most up-to-date effects. the full algorithm-to-hardware circulate is roofed holistically, so this ebook serves as a hands-on advisor for IT managers, researchers and quants/programmers who take into consideration integrating FPGAs into their present IT systems.
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Extra info for FPGA Based Accelerators for Financial Applications
Sample text
With the distribution of the loss function, we are ready to introduce so-called risk measures. Their main purpose is stated by Föllmer and Schied in [7] as: . a risk measure is viewed as a capital requirement: We are looking for the minimal amount of capital which, if added to the position and invested in a risk-free manner, makes the position acceptable. For completeness, we state: A risk measure ρ is a real-valued mapping defined on the space of random variables (risks). g. [7, 14, 17]). 1 10 Computational Challenges in Finance 17 As this discussion is beyond the scope of this survey, we restrict ourselves to the introduction of two popular examples of risk measures: The one which is mainly used in banks and has become an industry standard is the value-at-risk.
Then, for a step size of Δ = T /n > 0, the discretized process S(Δ ) (t) generated by the EMS is defined by S(Δ ) (0) s0 , S(Δ ) (kΔ ) S(Δ ) ((k − 1) Δ ) + μ S(Δ ) ((k − 1) Δ ) Δ + σ S(Δ ) ((k − 1) Δ ) Δ Wk , k = 1, . . , n. Here, Δ Wk , k = 1, . . , n, is a sequence of independent, N (0, Δ )-distributed random variables. Between two consecutive discretization points, we obtain the values of S(Δ ) (t) by linear interpolation. The EMS can easily be generalized to a multidimensional setting. s.
In most cases, the model will depend on a set of model parameters that are not directly observable from the market. We denote this set by M. e. the market parameters, such as spot prices or interest rates, is denoted by O. Finally, there is a third parameter set P, entering a pricing formula, which contains parameters of the financial product, also referred to as product parameters. For instance, for a European call on a stock we have P = {K, T }, where K is the strike and T the maturity of the call.
FPGA Based Accelerators for Financial Applications by Christian De Schryver
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