Aggregate Risk Analysis is a computationally intensive and a data intensive problem, thereby making the application of high-performance computing techniques interesting. In this paper, the design and implementation of a parallel Aggregate Risk Analysis algorithm on multi-core CPU and many-core GPU platforms are explored. The efficient computation of key risk measures, including Probable Maximum Loss […]

October 10, 2013 by hgpu

Stochastic simulation techniques employed for the analysis of portfolios of insurance/reinsurance risk, often referred to as `Aggregate Risk Analysis’, can benefit from exploiting state-of-the-art high-performance computing platforms. In this paper, parallel methods to speed-up aggregate risk analysis for supporting real-time pricing are explored. An algorithm for analysing aggregate risk is proposed and implemented for multi-core […]

August 13, 2013 by hgpu

Portfolio risk is commonly defined as the standard deviation of its return. The empirical correlation matrix of asset returns in a portfolio has its intrinsic noise component. This noise is filtered for more robust performance. Eigendecomposition is a widely used method for noise filtering. Jacobi algorithm has been a popular eigensolver technique due to its […]

July 29, 2012 by hgpu

(Counterparty) Credit Valuation Adjustments (CVA) has become a prevailing form of pricing default risk on over-the-counter (OTC) contracts. Due to the large size of portfolios included in the CVA calculation and its computational complexity, large computing grids are needed for the evaluation. The main purpose of this thesis is to investigate an even more computationally […]

January 13, 2012 by hgpu

In this paper, we propose, develop and implement a tool that increases the computational speed of exotic derivatives pricing at a fraction of the cost of traditional methods. Our paper focuses on investigating the computing efficiencies of GPU systems. We utilize the GPU’s natural parallelization capabilities to price financial instruments. We outline our implementation, solutions […]

December 2, 2011 by hgpu

We show how to implement highly efficient GPU solvers for one dimensional PDEs based on finite difference schemes. The typical use case is to price a large number of similar or related derivatives in parallel. Application scenarios include market making, real time pricing, and risk management. The tridiagonal systems in the backward propagation of a […]

October 16, 2011 by hgpu

This article presents differential equations and solution methods for the functions of the form $A(z) = F^-1(G(z))$, where $F$ and $G$ are cumulative distribution functions. Such functions allow the direct recycling of Monte Carlo samples from one distribution into samples from another. The method may be developed analytically for certain special cases, and illuminate the […]

November 8, 2010 by hgpu