| 000 | 02517nam a22003015i 4500 | ||
|---|---|---|---|
| 001 | 19612754 | ||
| 003 | OSt | ||
| 005 | 20201020094442.0 | ||
| 008 | 170427s2017 nyu 001 0 eng | ||
| 010 | _a 2017941212 | ||
| 020 | _a9783319539782 (pbk) | ||
| 040 |
_aDLC _beng _erda _cNCL |
||
| 082 |
_223 _a515.330285 _bHEN-A 2017 10874 |
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| 100 | 1 | _a Henrard, Marc | |
| 245 | 0 | 1 |
_aAlgorithmic differentiation in finance explained./ _cby Marc Henrard |
| 263 | _a1706 | ||
| 264 | 1 |
_aNew York, NY : _bSpringer Berlin Heidelberg, _c2017. |
|
| 300 |
_a103 p.; _c23 cm. |
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| 336 |
_atext _btxt _2rdacontent |
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| 505 | _a 1 Introduction; 2 The Principles of AlgorithmicDifferentiation; 3 Application to Finance; 4 Automatic Algorithmic Differentiation; 5 Derivatives to Non-inputs and Non-derivatives to Inputs; 6 Calibration; Appendix A Mathematical Results; Index. | ||
| 520 | _aThis book provides the first practical guide to the function and implementation of algorithmic differentiation in finance. Written in a highly accessible way, Algorithmic Differentiation Explained will take readers through all the major applications of AD in the derivatives setting with a focus on implementation. Algorithmic Differentiation (AD) has been popular in engineering and computer science, in areas such as fluid dynamics and data assimilation for many years. Over the last decade, it has been increasingly (and successfully) applied to financial risk management, where it provides an efficient way to obtain financial instrument price derivatives with respect to the data inputs. Calculating derivatives exposure across a portfolio is no simple task. It requires many complex calculations and a large amount of computer power, which in prohibitively expensive and can be time consuming. Algorithmic differentiation techniques can be very successfully in computing Greeks and sensitivities of a portfolio with machine precision. Written by a leading practitioner who works and programmes AD, it offers a practical analysis of all the major applications of AD in the derivatives setting and guides the reader towards implementation. Open source code of the examples is provided with the book, with which readers can experiment and perform their own test scenarios without writing the related code themselves. | ||
| 650 | _aAutomatic differentiation | ||
| 650 | _aFinance | ||
| 650 | _aEconomics, Mathematical | ||
| 650 | _aFinancial engineering | ||
| 906 |
_a0 _bibc _corignew _d2 _eepcn _f20 _gy-gencatlg |
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| 942 |
_2ddc _cBK |
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| 999 |
_c8115 _d8115 |
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