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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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Daróczi, Gergely (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing, 2013.
Colección:Community experience distilled.
Temas:
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.