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  • Book Overview & Buying Introduction to R for Quantitative Finance
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Introduction to R for Quantitative Finance

Introduction to R for Quantitative Finance

By : Gergely Daróczi, Agnes Vidovics Dancs, Michael Phule, Edina Berlinger (EURO), Peter Csoka, Daniel Daniel Havran, Marton Michaletzky, Zsolt Tulassay, Kata Váradi, Agnes Vidovics-Dancs
3.2 (20)
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Introduction to R for Quantitative Finance

Introduction to R for Quantitative Finance

3.2 (20)
By: Gergely Daróczi, Agnes Vidovics Dancs, Michael Phule, Edina Berlinger (EURO), Peter Csoka, Daniel Daniel Havran, Marton Michaletzky, Zsolt Tulassay, Kata Váradi, Agnes Vidovics-Dancs
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Overview of this book

Introduction to R for Quantitative Finance will show you how to solve real-world quantitative fi nance problems using the statistical computing language R. The book covers diverse topics ranging from time series analysis to fi nancial networks. Each chapter briefl y presents the theory behind specific concepts and deals with solving a diverse range of problems using R with the help of practical examples.This book will be your guide on how to use and master R in order to solve quantitative finance problems. This book covers the essentials of quantitative finance, taking you through a number of clear and practical examples in R that will not only help you to understand the theory, but how to effectively deal with your own real-life problems.Starting with time series analysis, you will also learn how to optimize portfolios and how asset pricing models work. The book then covers fixed income securities and derivatives such as credit risk management.
Table of Contents (17 chapters)
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Introduction to R for Quantitative Finance
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Introduction to R for Quantitative Finance
Credits
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Credits
About the Authors
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About the Authors
About the Reviewers
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About the Reviewers
www.PacktPub.com
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www.PacktPub.com
Preface
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Preface
Lock Free Chapter
1
Time Series Analysis
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Time Series Analysis
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Working with time series data
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Linear time series modeling and forecasting
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Cointegration
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Modeling volatility
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Summary
2
Portfolio Optimization
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Portfolio Optimization
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Mean-Variance model
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Solution concepts
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Working with real data
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Tangency portfolio and Capital Market Line
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Noise in the covariance matrix
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When variance is not enough
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Summary
3
Asset Pricing Models
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Asset Pricing Models
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Capital Asset Pricing Model
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Arbitrage Pricing Theory
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Beta estimation
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Model testing
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Summary
4
Fixed Income Securities
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Fixed Income Securities
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Measuring market risk of fixed income securities
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Immunization of fixed income portfolios
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Pricing a convertible bond
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Summary
5
Estimating the Term Structure of Interest Rates
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Estimating the Term Structure of Interest Rates
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The term structure of interest rates and related functions
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The estimation problem
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Estimation of the term structure by linear regression
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Cubic spline regression
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Applied R functions
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Summary
6
Derivatives Pricing
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Derivatives Pricing
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The Black-Scholes model
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The Cox-Ross-Rubinstein model
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Connection between the two models
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Greeks
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Implied volatility
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Summary
7
Credit Risk Management
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Credit Risk Management
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Credit default models
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Correlated defaults – the portfolio approach
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Migration matrices
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Getting started with credit scoring in R
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Summary
8
Extreme Value Theory
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Extreme Value Theory
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Theoretical overview
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Application – modeling insurance claims
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Summary
9
Financial Networks
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Financial Networks
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Representation, simulation, and visualization of financial networks
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Analysis of networks’ structure and detection of topology changes
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Contribution to systemic risk – identification of SIFIs
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Summary
1
References
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References
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Time series analysis
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Portfolio optimization
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Asset pricing
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Fixed income securities
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Estimating the term structure of interest rates
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Derivatives Pricing
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Credit risk management
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Extreme value theory
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Financial networks
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Index
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Index

Connection between the two models

After applying the two basic option pricing models, we give some theoretical background to them. We do not aim to give a detailed mathematical derivation, but we intend to emphasize (and then illustrate in R) the similarities of the two approaches. The financial idea behind the continuous and the binomial option pricing is the same: if we manage to hedge the option perfectly by holding the appropriate quantity of the underlying asset, it means we created a risk-free portfolio. Since the market is supposed to be arbitrage-free, the yield of a risk-free portfolio must equal the risk-free rate. One important observation is that the correct hedging ratio is holding underlying asset per option. Hence, the ratio is the partial derivative (or its discrete correspondent in the binomial model) of the option value with respect to the underlying price. This partial derivative is called the delta of the option. Another interesting connection between the two models...

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Introduction to R for Quantitative Finance
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