Special Issue for the Workshop on High Performance Computational Finance

High performance computational finance is an emerging multidisciplinary technical field, combining conventional high performance computing expertise with computational finance. Traditionally, these subject areas have been kept separate, with the high performance computing expert having little need to understand the mathematical models that financial institutions develop to price and measure the risk of financial instruments. Conversely, financial modeling experts or ‘quants’ have had little regard for the technicalities of high performance computing. As the traditional computational approaches fall short and the volume of data and the complexity of the financial models grow at an astonishing rate, financial companies are becoming increasingly reliant on high performance computing. This is transforming the way that financial markets do business and paving the way for new product and strategy innovations. But the ‘expertise gap’ between system engineers and quantitative developers is leading to ubiquitous deficiencies in financial institution’s abilities to future-proof quantitative pricing and risk infrastructure to rapidly changing modeling requirements or advances in microprocessor technology. The Workshop on High Performance Computational Finance (WHPCF) at Supercomputing is uniquely positioned to address these issues from a vendor-neutral vantage point. Drawing upon world experts in financial mathematics and high performance computing, this workshop has identified and dissected key obstacles encountered in deploying financial models in a high performance computing environment. One of the most prominent of these obstacles is the lack of a critical appraisal of accelerator platforms and their implementation support infrastructure for developing financial models. Accelerator platforms was the theme of the 2010 Supercomputing Workshop, and this special issue features several contributions describing high performance implementations of computationally intensive derivative pricing models on graphics processing units (GPUs) and field-programmable gate arrays (FPGAs). Another key obstacle, which was the theme of the 2009 Supercomputing Workshop, is the inadequacy of quantitative risk infrastructure within global financial institutions. As credit and market risk estimation on large portfolios of traded assets is too numerically intensive, practitioners often use crude approximations to heavily simplify the computations to the detriment of the amount of insight that can be gained using quantitative models. The workshop has identified some of the main computational challenges in estimating risk more accurately, and this special issue presents practical solutions that rely on a combination of numerical linear algebra and shared memory parallelism. Beyond addressing these challenges, the workshop fills a void by stimulating an area of research that is too often overlooked by traditional research funding organizations, despite the subject area’s growth potential to facilitate global financial analysis as the adoption of accelerator platforms for financial computations becomes main stream. This special issue consists of seven peer reviewed papers that are extended versions of a select group of papers published in the previous two workshop proceedings. The content is diverse as are the authors who are either affiliated with financial institutions, academia, or computer vendor research facilities. The first part of the special issue focuses on the practice of financial modeling with accelerator platforms and covers topics ranging from pricing multiasset American and barrier options on GPUs, estimating the market Value-at-Risk of large portfolios on highly parallel architectures, and pricing credit derivatives using FPGAs. The second part of the special issue presents proof of concept high performance algorithms applied to financial applications. Topics range from stock prediction using virtual generalized random access memory (VG-RAM) weightless neural