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.
Reihe
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Illustrationen
127 s/w Abbildungen, 53 s/w Tabellen
53 Tables, black and white; 127 Illustrations, black and white
Maße
Höhe: 240 mm
Breite: 161 mm
Dicke: 38 mm
Gewicht
ISBN-13
978-1-4822-9966-3 (9781482299663)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
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.
Part I: Computationally Expensive Problems in the Financial Industry 1. Computationally Expensive Problems in Investment Banking 2. Using Market Sentiment to Enhance Second-Order Stochastic Dominance Trading Models 3. The Alpha Engine: Designing an Automated Trading Algorithm 4. Portfolio Liquidation and Ambiguity Aversion 5. Challenges in Scenario Generation: Modeling Market and Non-Market Risks in Insurance Part II: Numerical Methods in Financial High-Performance Computing (HPC) 6. Finite Difference Methods for Medium- and High-Dimensional Derivative Pricing PDEs 7. Multilevel Monte Carlo Methods for Applications in Finance 8. Fourier and Wavelet Option Pricing Methods 9. A Practical Robust Long-Term Yield Curve Model 10. Algorithmic Differentiation 11. Case Studies of Real-Time Risk Management via Adjoint Algorithmic Differentiation (AAD) 12. Tackling Reinsurance Contract Optimization by Means of Evolutionary Algorithms and HPC 13. Evaluating Blockchain Implementation of Clearing and Settlement at the IATA Clearing House Part III: HPC Systems: Hardware, Software, and Data with Financial Applications 14. Supercomputers 15. Multiscale Dataflow Computing in Finance 16. Manycore Parallel Computation 17. Practitioner's Guide on the Use of Cloud Computing in Finance 18. Blockchains and Distributed Ledgers in Retrospective and Perspective 19. Optimal Feature Selection Using a Quantum Annealer
