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_a332.64/5702855133 _223 |
100 | 1 | _aHilpisch, Yves J. | |
245 | 1 | 0 |
_aDerivatives analytics with Python : _bdata analysis, models, simulation, calibration and hedging / _cYves Hilpisch. |
264 | 1 |
_aChichester : _bWiley, _c2015. |
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300 |
_axvii, 356 pages : _billustrations ; _c25 cm. |
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_aunmediated _bn _2rdamedia |
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490 | 1 | _aThe Wiley finance series | |
504 | _aIncludes bibliographical references (pages 341-345) and index. | ||
520 |
_a"Supercharge options analytics and hedging using the power of Python Derivatives Analytics with Python shows you how to implement market-consistent valuation and hedging approaches using advanced financial models, efficient numerical techniques, and the powerful capabilities of the Python programming language. This unique guide offers detailed explanations of all theory, methods, and processes, giving you the background and tools necessary to value stock index options from a sound foundation. You'll find and use self-contained Python scripts and modules and learn how to apply Python to advanced data and derivatives analytics as you benefit from the 5,000+ lines of code that are provided to help you reproduce the results and graphics presented. Coverage includes market data analysis, risk-neutral valuation, Monte Carlo simulation, model calibration, valuation, and dynamic hedging, with models that exhibit stochastic volatility, jump components, stochastic short rates, and more. The companion website features all code and IPython Notebooks for immediate execution and automation. Python is gaining ground in the derivatives analytics space, allowing institutions to quickly and efficiently deliver portfolio, trading, and risk management results. This book is the finance professional's guide to exploiting Python's capabilities for efficient and performing derivatives analytics. Reproduce major stylized facts of equity and options markets yourself Apply Fourier transform techniques and advanced Monte Carlo pricing Calibrate advanced option pricing models to market data Integrate advanced models and numeric methods to dynamically hedge options Recent developments in the Python ecosystem enable analysts to implement analytics tasks as performing as with C or C++, but using only about one-tenth of the code or even less. Derivatives Analytics with Python -- Data Analysis, Models, Simulation, Calibration and Hedging shows you what you need to know to supercharge your derivatives and risk analytics efforts"-- _cProvided by publisher. |
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505 | 0 | _a1. A quick tour -- 2. What is market-based valuation? -- 3. Market stylized facts -- 4. Risk-neutral valuation -- 5. Complete market models -- 6. Fourier-based option pricing -- 7. Valuation of American options by simulation -- 8. A first example of market-based valuation -- 9. General model framework -- 10. Monte Carlo simulation -- 11. Model calibration -- 12. Simulation and valuation in the general model framework -- 13. Dynamic hedging -- 14. Executive summary -- Appendix A. Python in a nutshell. | |
650 | 0 | _aDerivative securities. | |
650 | 0 | _aHedging (Finance) | |
650 | 0 | _aPython (Computer program language) | |
650 | 7 |
_aDerivative securities. _2fast _0(OCoLC)fst00891019 |
|
650 | 7 |
_aHedging (Finance) _2fast _0(OCoLC)fst00954458 |
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650 | 7 |
_aPython (Computer program language) _2fast _0(OCoLC)fst01084736 |
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776 | 0 | 8 |
_iOnline version: _aHilpisch, Yves J. _tDerivatives analytics with Python. _b1. _dHoboken : Wiley, 2015 _z9781119037934 _w(DLC) 2015013871 |
830 | 0 | _aWiley finance series. | |
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