{"product_id":"financial-modelling-with-pytho-9780470987841","title":"Financial Modelling with Pytho","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eFletcher and Gardner have created a comprehensive resource that will be of interest not only to those working in the field of finance, but also to those using numerical methods in other fields such as engineering, physics, and actuarial mathematics. By showing how to combine the high-level elegance, accessibility, and flexibility of Python, with the low-level computational efficiency of C++, in the context of interesting financial modeling problems, they have provided an implementation template which will be useful to others seeking to jointly optimize the use of computational and human resources. They document all the necessary technical details required in order to make external numerical libraries available from within Python, and they contribute a useful library of their own, which will significantly reduce the start-up costs involved in building financial models. This book is a must read for all those with a need to apply numerical methods in the valuation of financial claims.\u0026lt;\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003e1 Welcome to Python.\u003c\/b\u003e  \u003c\/i\u003e\u003cp\u003e1.1 Why Python?\u003c\/p\u003e \u003cp\u003e1.2 Common misconceptions about Python.\u003c\/p\u003e \u003cp\u003e1.3 Roadmap for this book.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 The PPF Package.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 PPF topology.\u003c\/p\u003e \u003cp\u003e2.2 Unit testing.\u003c\/p\u003e \u003cp\u003e2.3 Building and installing PPF.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Extending Python from C++.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Boost.Date Time types.\u003c\/p\u003e \u003cp\u003e3.2 Boost.MultiArray and special functions.\u003c\/p\u003e \u003cp\u003e3.3 NumPy arrays.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Basic Mathematical Tools.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Random number generation.\u003c\/p\u003e \u003cp\u003e4.2 \u003ci\u003eN\u003c\/i\u003e(.)\u003c\/p\u003e \u003cp\u003e4.3 Interpolation.\u003c\/p\u003e \u003cp\u003e4.4 Root finding.\u003c\/p\u003e \u003cp\u003e4.5 Linear algebra.\u003c\/p\u003e \u003cp\u003e4.6 Generalised linear least squares.\u003c\/p\u003e \u003cp\u003e4.7 Quadratic and cubic roots.\u003c\/p\u003e \u003cp\u003e4.8 Integration.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Market: Curves and Surfaces.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Curves.\u003c\/p\u003e \u003cp\u003e5.2 Surfaces.\u003c\/p\u003e \u003cp\u003e5.3 Environment.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Data Model.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Observables.\u003c\/p\u003e \u003cp\u003e6.2 Flows.\u003c\/p\u003e \u003cp\u003e6.3 Adjuvants.\u003c\/p\u003e \u003cp\u003e6.4 Legs.\u003c\/p\u003e \u003cp\u003e6.5 Exercises.\u003c\/p\u003e \u003cp\u003e6.6 Trades.\u003c\/p\u003e \u003cp\u003e6.7 Trade utilities.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Timeline: Events and Controller.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Events.\u003c\/p\u003e \u003cp\u003e7.2 Timeline.\u003c\/p\u003e \u003cp\u003e7.3 Controller.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 The Hull–White Model.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 A component-based design.\u003c\/p\u003e \u003cp\u003e8.2 The model and model factories.\u003c\/p\u003e \u003cp\u003e8.3 Concluding remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Pricing using Numerical Methods.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 A lattice pricing framework.\u003c\/p\u003e \u003cp\u003e9.2 A Monte-Carlo pricing framework.\u003c\/p\u003e \u003cp\u003e9.3 Concluding remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Pricing Financial Structures in Hull–White.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Pricing a Bermudan.\u003c\/p\u003e \u003cp\u003e10.2 Pricing a TARN.\u003c\/p\u003e \u003cp\u003e10.3 Concluding remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Hybrid Python\/C++ Pricing Systems.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 nth imm of year revisited.\u003c\/p\u003e \u003cp\u003e11.2 Exercising nth imm of year from C++.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Python Excel Integration.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Black–scholes COM server.\u003c\/p\u003e \u003cp\u003e12.2 Numerical pricing with PPF in Excel.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendices.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA Python.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Python interpreter modes.\u003c\/p\u003e \u003cp\u003eA.2 Basic Python.\u003c\/p\u003e \u003cp\u003eA.3 Conclusion.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eB Boost.Python.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB.1 Hello world.\u003c\/p\u003e \u003cp\u003eB.2 Classes, constructors and methods.\u003c\/p\u003e \u003cp\u003eB.3 Inheritance.\u003c\/p\u003e \u003cp\u003eB.4 Python operators.\u003c\/p\u003e \u003cp\u003eB.5 Functions.\u003c\/p\u003e \u003cp\u003eB.6 Enums.\u003c\/p\u003e \u003cp\u003eB.7 Embedding.\u003c\/p\u003e \u003cp\u003eB.8 Conclusion.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eC Hull–White Model Mathematics.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eD Pickup Value Regression.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBibliography.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48864643481943,"sku":"9780470987841","price":73.62,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470987841.jpg?v=1722272865","url":"https:\/\/bookcurl.com\/products\/financial-modelling-with-pytho-9780470987841","provider":"Book Curl","version":"1.0","type":"link"}