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Mastering R for quantitative finance : use R to optimize your trading strategy and build up your own risk management system /

This book is intended for those who want to learn how to use R's capabilities to build models in quantitative finance at a more advanced level. If you wish to perfectly take up the rhythm of the chapters, you need to be at an intermediate level in quantitative finance and you also need to have...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Berlinger, Edina (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing, 2015.
Colección:Community experience distilled.
Temas:
Acceso en línea:Texto completo
Texto completo

MARC

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100 1 |a Berlinger, Edina,  |e author. 
245 1 0 |a Mastering R for quantitative finance :  |b use R to optimize your trading strategy and build up your own risk management system /  |c Edina Berlinger ... [and seventeen others]. 
246 3 0 |a Use R to optimize your trading strategy and build up your own risk management system 
264 1 |a Birmingham, UK :  |b Packt Publishing,  |c 2015. 
300 |a 1 online resource (1 volume) :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
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347 |a text file 
490 1 |a Community experience distilled 
588 0 |a Online resource; title from cover page (Safari, viewed April 6, 2015). 
504 |a Includes bibliographical references and index. 
505 0 |a Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Time Series Analysis; Multivariate time series analysis; Cointegration; Vector autoregressive models; VAR implementation example; Cointegrated VAR and VECM; Volatility modeling; GARCH modeling with the rugarch package; The standard GARCH model; Exponential GARCH model (EGARCH); Threshold GARCH model (TGARCH); Simulation and forecasting; Summary; References and reading list; Chapter 2: Factor Models; Arbitrage pricing theory; Implementation of APT 
505 8 |a Fama-French three-factor modelModeling in R; Data selection; Estimation of APT with principal component analysis; Estimation of the Fama-French model; Summary; References; Chapter 3: Forecasting Volume; Motivation; The intensity of trading; The volume forecasting model; Implementation in R; The data; Loading the data; The seasonal component; AR(1) estimation and forecasting; SETAR estimation and forecasting; Interpreting the results; Summary; References; Chapter 4: Big Data -- Advanced Analytics; Getting data from open sources; Introduction to big data analysis in R 
505 8 |a K-means clustering on big dataLoading big matrices; Big data K-means clustering analysis; Big data linear regression analysis; Loading big data; Fitting a linear regression model on large datasets; Summary; References; Chapter 5: FX Derivatives; Terminology and notations; Currency options; Exchange options; Two-dimensional Wiener processes; The Margrabe formula; Application in R; Quanto options; Pricing formula for call quanto; Pricing a call quanto in R; Summary; References; Chapter 6: Interest Rate Derivatives and Models; The Black model; Pricing a cap with Black's model; The Vasicek model 
505 8 |a The Cox-Ingersoll-Ross modelParameter estimation of interest rate models; Using the SMFI5 package; Summary; References; Chapter 7: Exotic Options; A general pricing approach; The role of dynamic hedging; How R could help a lot; A glance beyond vanillas; Greeks -- the link back to the vanilla world; Pricing the Double-no-touch option; Another way to price the Double-no-touch option; The life of a Double-no-touch option -- a simulation; Exotic options embedded in structured products; Summary; References; Chapter 8: Optimal Hedging; Hedging of derivatives; Market risk of derivatives 
505 8 |a Static delta hedgeDynamic delta hedge; Comparing the performance of delta hedging; Hedging in the presence of transaction costs; Optimization of the hedge; Optimal hedging in the case of absolute transaction costs; Optimal hedging in the case of relative transaction costs; Further extensions; Summary; References; Chapter 9: Fundamental Analysis; The Basics of fundamental analysis; Collecting data; Revealing connections; Including multiple variables; Separating investment targets; Setting classification rules; Backtesting; Industry-specific investment; Summary; References 
520 |a This book is intended for those who want to learn how to use R's capabilities to build models in quantitative finance at a more advanced level. If you wish to perfectly take up the rhythm of the chapters, you need to be at an intermediate level in quantitative finance and you also need to have a reasonable knowledge of R. 
546 |a English. 
590 |a eBooks on EBSCOhost  |b EBSCO eBook Subscription Academic Collection - Worldwide 
590 |a O'Reilly  |b O'Reilly Online Learning: Academic/Public Library Edition 
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