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

ISBN-13: 9781482299663 / Angielski / Twarda / 2018 / 614 str.

Erik Vynckier;Juho Kanniainen;John Keane

Opis

This general book on high-performance computing in finance is academically and pragmatically relevant as it is built around real-life challenges. The text covers the newest techniques such as automatic differentiation, dataflow, and large scale computation for Basel III, Solvency II, and stochastic programming. With illustrative success stories in the use of high-performance computing in the finance and insurance industries, the book imparts the wisdom of adopting this method of numbers crunching to achieve business goals.

Spis treści

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