01099nam0 22002891i 450 UON0049135520231205105333.209978-99-412-7520-320181108d2017 |0itac50 bageoENGGE|||| 1||||Let's Speak GeorgianBasic courseMaia JavakhidzeTbilisiLanguages Study2017253 p.ill.28 cm.CDROMDono Prof. Alberto MancoIT-UONSI Ling22/0106Lingua georgianaGrammaticheUONC014649FIGETbilisiUONL000567499.969Lingua georgiana21JAVAKHIDZEMaiaUONV240457759979Language studiesUONV283245650ITSOL20250516RICASIBA - SISTEMA BIBLIOTECARIO DI ATENEOUONSIUON00491355SIBA - SISTEMA BIBLIOTECARIO DI ATENEOSI 22 0106 SI 26898 7 0106 Dono Prof. Alberto MancoLet's Speak Georgian1537063UNIOR03443nam 22005295 450 991025504340332120250721171414.09783319539799331953979510.1007/978-3-319-53979-9(PPN)286456737(CKB)4100000000586930(DE-He213)978-3-319-53979-9(MiAaPQ)EBC5017875(Perlego)3497851(EXLCZ)99410000000058693020170904d2017 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierAlgorithmic Differentiation in Finance Explained /by Marc Henrard1st ed. 2017.Cham :Springer International Publishing :Imprint: Palgrave Macmillan,2017.1 online resource (XIII, 103 p. 7 illus.)Financial Engineering Explained9783319539782 3319539787 Includes bibliographical references and index.Chapter1 Introduction -- Chapter2 The Principles of Algorithmic Differentiation -- Chapter3 Applications to Finance -- Chapter4 Automated Algorithmic differentiation -- Chapter5 Derivatives to Non-inputs and Non-derivatives to Inputs -- Chapter 6 Calibration.This 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.Financial Engineering ExplainedFinancial engineeringSocial sciencesMathematicsFinancial EngineeringMathematics in Business, Economics and FinanceFinancial engineering.Social sciencesMathematics.Financial Engineering.Mathematics in Business, Economics and Finance.332Henrard Marcauthttp://id.loc.gov/vocabulary/relators/aut941823BOOK9910255043403321Algorithmic Differentiation in Finance Explained2124954UNINA