1st Edition

High-Performance Computing in Finance
Problems, Methods, and Solutions

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ISBN 9781482299670
January 1, 2021 Forthcoming by Chapman and Hall/CRC
614 Pages 127 B/W Illustrations

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Book Description

High-Performance Computing (HPC) delivers higher computational performance to solve problems in science, engineering and finance. There are various HPC resources available for different needs, ranging from cloud computing– that can be used without much expertise and expense – to more tailored hardware, such as Field-Programmable Gate Arrays (FPGAs) or D-Wave’s quantum computer systems. High-Performance Computing in Finance is the first book that provides a state-of-the-art introduction to HPC for finance, capturing both academically and practically relevant problems.

Table of Contents

Part I: Computationally Expensive Problems in the Financial Industry

1. Computationally Expensive Problems in Investment Banking

[Jonathan Rosen, Christian Kahl, Russell Goyder, and Mark Gibbs]

2. Using Market Sentiment to Enhance Second-Order Stochastic Dominance Trading Models

[Gautam Mitra, Christina Erlwein-Sayer, Cristiano Arbex Valle, and Xiang Yu]

3. The Alpha Engine: Designing an Automated Trading Algorithm

[Anton Golub, James B. Glattfelder, and Richard B. Olsen]

4. Portfolio Liquidation and Ambiguity Aversion

[Alvaro Cartea, Ryan Donnelly, and Sebastian Jaimungal]

5. Challenges in Scenario Generation: Modeling Market and Non-Market Risks in Insurance

[Douglas McLean]

Part II: Numerical Methods in Financial High-Performance Computing (HPC)

6. Finite Difference Methods for Medium- and High-Dimensional Derivative Pricing PDEs

[C. Reisinger and R. Wissmann]

7. Multilevel Monte Carlo Methods for Applications in Finance

[Michael B. Giles and Lukasz Szpruch]

8. Fourier and Wavelet Option Pricing Methods

[Stefanus C. Maree, Luis Ortiz-Gracia, and Cornelis W. Oosterlee]

9. A Practical Robust Long-Term Yield Curve Model

[M. A. H. Dempster, Elena A. Medova, Igor Osmolovskiy, and Philipp Ustinov]

10. Algorithmic Differentiation

[Uwe Naumann, Jonathan Huser, Jens Deussen, and Jacques du Toit]

11. Case Studies of Real-Time Risk Management via Adjoint Algorithmic Differentiation (AAD)

[Luca Capriotti and Jacky Lee]

12. Tackling Reinsurance Contract Optimization by Means of Evolutionary Algorithms and HPC

[Omar Andres Carmona Cortes and Andrew Rau-Chaplin]

13. Evaluating Blockchain Implementation of Clearing and Settlement at the IATA Clearing House

[Sergey Ivliev, Yulia Mizgireva, and Juan Ivan Martin]

Part III: HPC Systems: Hardware, Software, and Data with Financial Applications

14. Supercomputers

[Peter Schober]

15. Multiscale Dataflow Computing in Finance

[Oskar Mencer, Brian Boucher, Gary Robinson, Jon Gregory, and Georgi Gaydadjiev]

16. Manycore Parallel Computation

[John Ashley and Mark Joshi]

17. Practitioner’s Guide on the Use of Cloud Computing in Finance

[Binghuan Lin, Rainer Wehkamp, and Juho Kanniainen]

18. Blockchains and Distributed Ledgers in Retrospective and Perspective

[Alexander Lipton]

19. Optimal Feature Selection Using a Quantum Annealer

[Andrew Milne, Max Rounds, and Phil Goddard]

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Michael Dempster is Professor Emeritus, Centre for Financial Research, University of Cambridge. He has held research and teaching appointments at leading universities globally and is founding Editor-in-Chief of Quantitative Finance. His numerous papers and books have won several awards and he is Honorary Fellow of the IFoA, Member of the Academia dei Lincei and Managing Director of Cambridge Systems Associates.

Juho Kanniainen is Professor of Financial Engineering at Tampere University of Technology, Finland. He has served as Coordinator of two international EU-programmes, HPC in Finance (www.hpcfinance.eu) and Big Data in Finance (www.bigdatafinance.eu). His research is broadly in quantitative finance focusing on computationally expensive problems and data-driven approaches.

John Keane is Professor of Data Engineering in the School of Computer Science at the University of Manchester, UK. As part of the UK Government’s Foresight Project, The Future of Computer Trading in Financial Markets, he co-authored a commissioned economic impact assessment review. He has been involved in both the EU HPC in Finance and Big Data in Finance programmes. His wider research interests are data and decision analytics, and related performance aspects.

Erik Vynckier is board member of Foresters Friendly Society, partner of InsurTech Venture Partners and Chief Investment Officer of Eli Global, following a career in banking, insurance, asset management and petrochemical industry. He co-founded EU initiatives on high performance computing and big data in finance. Erik graduated as MBA at London Business School and as chemical engineer at Universiteit Gent.