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Introduction to R for quantitative finance : solve a diverse range of problems with R, one of the most powerful tools for quantitative finance /
This book is a tutorial guide for new users that aims to help you understand the basics of and become accomplished with the use of R for quantitative finance. If you are looking to use R to solve problems in quantitative finance, then this book is for you. A basic knowledge of financial theory is as...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2013.
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| Colección: | Community experience distilled.
|
| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
MARC
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| 100 | 1 | |a Daróczi, Gergely, |e author. | |
| 245 | 1 | 0 | |a Introduction to R for quantitative finance : |b solve a diverse range of problems with R, one of the most powerful tools for quantitative finance / |c Gergely Daróczi [and others]. |
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2013. | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Community experience distilled | |
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Time Series Analysis; Working with time series data; Linear time series modeling and forecasting; Modeling and forecasting UK house prices; Model identification and estimation; Model diagnostic checking; Forecasting; Cointegration; Cross hedging jet fuel; Modeling volatility; Volatility forecasting for risk management; Testing for ARCH effects; GARCH model specification; GARCH model estimation; Backtesting the risk model; Forecasting; Summary. | |
| 505 | 8 | |a Chapter 2: Portfolio OptimizationMean-Variance model; Solution concepts; Theorem (Lagrange); Working with real data; Tangency portfolio and Capital Market Line; Noise in the covariance matrix; When variance is not enough; Summary; Chapter 3: Asset Pricing Models; Capital Asset Pricing Model; Arbitrage Pricing Theory; Beta estimation; Data selection; Simple beta estimation; Beta estimation from linear regression; Model testing; Data collection; Modeling the SCL; Testing the explanatory power of the individual variance; Summary; Chapter 4: Fixed Income Securities. | |
| 505 | 8 | |a Measuring market risk of fixed income securitiesExample -- implementation in R; Immunization of fixed income portfolios; Net worth immunization; Target date immunization; Dedication; Pricing a convertible bond; Summary; Chapter 5: Estimating the Term Structure of Interest Rates; The term structure of interest rates and related functions; The estimation problem; Estimation of the term structure by linear regression; Cubic spline regression; Applied R functions; Summary; Chapter 6: Derivatives Pricing; The Black-Scholes model; The Cox-Ross-Rubinstein model; Connection between the two models. | |
| 505 | 8 | |a GreeksImplied volatility; Summary; Chapter 7: Credit Risk Management; Credit default models; Structural models; Intensity models; Correlated defaults -- the portfolio approach; Migration matrices; Getting started with credit scoring in R; Summary; Chapter 8: Extreme Value Theory; Theoretical overview; Application -- modeling insurance claims; Exploratory data analysis; Tail behavior of claims; Determining the threshold; Fitting a GPD distribution to the tails; Quantile estimation using the fitted GPD model; Calculation of expected loss using the fitted GPD model; Summary. | |
| 505 | 8 | |a Chapter 9: Financial NetworksRepresentation, simulation, and visualization of financial networks; Analysis of networks' structure and detection of topology changes; Contribution to systemic risk -- identification of SIFIs; Summary; Appendix: References; Time series analysis; Portfolio optimization; Asset pricing; Fixed income securities; Estimating the term structure of interest rates; Derivatives Pricing; Credit risk management; Extreme value theory; Financial networks; Index. | |
| 520 | |a This book is a tutorial guide for new users that aims to help you understand the basics of and become accomplished with the use of R for quantitative finance. If you are looking to use R to solve problems in quantitative finance, then this book is for you. A basic knowledge of financial theory is assumed, but familiarity with R is not required. With a focus on using R to solve a wide range of issues, this book provides useful content for both the R beginner and more experience users. | ||
| 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 | ||
| 650 | 0 | |a R (Computer program language) | |
| 650 | 0 | |a Finance |x Mathematical models |x Data processing. | |
| 650 | 0 | |a Finance |x Data processing. | |
| 650 | 6 | |a R (Langage de programmation) | |
| 650 | 6 | |a Finances |x Modèles mathématiques |x Informatique. | |
| 650 | 6 | |a Finances |x Informatique. | |
| 650 | 7 | |a MATHEMATICS |x Applied. |2 bisacsh | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a Finance |x Data processing |2 fast | |
| 650 | 7 | |a Finance |x Mathematical models |x Data processing |2 fast | |
| 650 | 7 | |a R (Computer program language) |2 fast | |
| 776 | 0 | 8 | |i Print version: |t Introduction to R for quantitative finance |z 178328093X |w (OCoLC)863326609 |
| 830 | 0 | |a Community experience distilled. | |
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