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Mastering R for Quantitative Finance

Edina Berlinger Ferenc Illés Milán Badics ?dám Banai Gergely Daróczi Barbara D?m?t?r Gergely Gabler Dániel Havran Péter Juhász 著

更新時間：2021-07-23 20:11:08

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最新章節： Index

計算機網絡編程語言與程序設計

ThisbookisintendedforthosewhowanttolearnhowtouseR'scapabilitiestobuildmodelsinquantitativefinanceatamoreadvancedlevel.Ifyouwishtoperfectlytakeuptherhythmofthechapters,youneedtobeatanintermediatelevelinquantitativefinanceandyoualsoneedtohaveareasonableknowledgeofR.

目錄(111章)

倒序

coverpage
Mastering R for Quantitative Finance
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Support files eBooks discount offers and more
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Chapter 1. Time Series Analysis
Multivariate time series analysis
Volatility modeling
Summary
References and reading list
Chapter 2. Factor Models
Arbitrage pricing theory
Modeling in R
Summary
References
Chapter 3. Forecasting Volume
Motivation
The intensity of trading
The volume forecasting model
Implementation in R
Summary
References
Chapter 4. Big Data – Advanced Analytics
Getting data from open sources
Introduction to big data analysis in R
K-means clustering on big data
Big data linear regression analysis
Summary
References
Chapter 5. FX Derivatives
Terminology and notations
Currency options
Exchange options
Quanto options
Summary
References
Chapter 6. Interest Rate Derivatives and Models
The Black model
The Vasicek model
The Cox-Ingersoll-Ross model
Parameter 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 can 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
Hedging in the presence of 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
Chapter 10. Technical Analysis Neural Networks and Logoptimal Portfolios
Market efficiency
Technical analysis
Neural networks
Logoptimal portfolios
Summary
References
Chapter 11. Asset and Liability Management
Data preparation
Interest rate risk measurement
Liquidity risk measurement
Modeling non-maturity deposits
Summary
References
Chapter 12. Capital Adequacy
Principles of the Basel Accords
Risk measures
Risk categories
Summary
References
Chapter 13. Systemic Risks
Systemic risk in a nutshell
The dataset used in our examples
Core-periphery decomposition
The simulation method
Possible interpretations and suggestions
Summary
References
Index 更新時間：2021-07-23 20:11:08

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Mastering R for Quantitative Finance