| Description |
1 online resource |
| Series |
Community experience distilled |
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Community experience distilled.
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| Contents |
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 |
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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 |
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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 |
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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 |
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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 |
| Summary |
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 |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| Subject |
R (Computer program language)
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Finance -- Mathematical models -- Data processing
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Finance -- Data processing.
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MATHEMATICS -- Applied.
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MATHEMATICS -- Probability & Statistics -- General.
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Finance -- Data processing
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Finance -- Mathematical models -- Data processing
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R (Computer program language)
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| Form |
Electronic book
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| ISBN |
9781783280940 |
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1783280948 |
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