Modern Computational Finance: AAD and Parallel SimulationsJohn Wiley & Sons, 20. nov. 2018 - 592 sider Arguably the strongest addition to numerical finance of the past decade, Algorithmic Adjoint Differentiation (AAD) is the technology implemented in modern financial software to produce thousands of accurate risk sensitivities, within seconds, on light hardware. AAD recently became a centerpiece of modern financial systems and a key skill for all quantitative analysts, developers, risk professionals or anyone involved with derivatives. It is increasingly taught in Masters and PhD programs in finance. Danske Bank's wide scale implementation of AAD in its production and regulatory systems won the In-House System of the Year 2015 Risk award. The Modern Computational Finance books, written by three of the very people who designed Danske Bank's systems, offer a unique insight into the modern implementation of financial models. The volumes combine financial modelling, mathematics and programming to resolve real life financial problems and produce effective derivatives software. This volume is a complete, self-contained learning reference for AAD, and its application in finance. AAD is explained in deep detail throughout chapters that gently lead readers from the theoretical foundations to the most delicate areas of an efficient implementation, such as memory management, parallel implementation and acceleration with expression templates. The book comes with professional source code in C++, including an efficient, up to date implementation of AAD and a generic parallel simulation library. Modern C++, high performance parallel programming and interfacing C++ with Excel are also covered. The book builds the code step-by-step, while the code illustrates the concepts and notions developed in the book. |
Indhold
About the Companion C++ Code | xxv |
CHAPTER 1 | 13 |
CHAPTER 2 | 25 |
CHAPTER 3 | 47 |
Parallel Simulation | 123 |
CHAPTER 5 | 154 |
MonteCarlo | 185 |
CHAPTER 6 | 213 |
CHAPTER 10 | 357 |
CHAPTER 11 | 401 |
CHAPTER 12 | 407 |
CHAPTER 13 | 439 |
CHAPTER 14 | 476 |
k Multiple Differentiation in Almost Constant Time | 483 |
CHAPTER 15 | 503 |
Debugging AAD Instrumentation | 541 |
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Almindelige termer og sætninger
AAD library Adjoint Differentiation adjoint propagation algorithm allocation apply arguments Asset Pricing auto back-propagation barrier Black and Scholes blocklist bool cache calculation code calibration callable cash-flows Chapter check-pointing client code compile concurrent const double const size_t constructor copy constructor cores cout debug defined derivatives dimension double& efficient endl European European options evaluation event date execution expression templates forward function Gaussian implementation implied volatility initialization inline inputs instrumented iterator Libor linear local volatility lock loop matrix maturity model parameters MODERN COMPUTATIONAL FINANCE Monte-Carlo multi-threaded multiple mutable objects mutex node number type numeraire operations operator overloading option overload path payoffs pointers programming queue random numbers risk-neutral measure samples scenario sensitivities SIMD simulation library smart pointers spinlock spot static stochastic volatility swap swaption tape task template class thread pool unary variables vector void volatility worker threads