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Mastering R for Quantitative Finance

By : Gabler

4 (11)

Mastering R for Quantitative Finance

4 (11)

By: Gabler

Overview of this book

This book is intended for those who want to learn how to use R's capabilities to build models in quantitative finance at a more advanced level. If you wish to perfectly take up the rhythm of the chapters, you need to be at an intermediate level in quantitative finance and you also need to have a reasonable knowledge of R.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Time Series Analysis

1. Time Series Analysis

Multivariate time series analysis

Volatility modeling

Summary

References and reading list

2. Factor Models

2. Factor Models

Arbitrage pricing theory

Modeling in R

Summary

References

3. Forecasting Volume

3. Forecasting Volume

Motivation

The intensity of trading

The volume forecasting model

Implementation in R

Summary

References

4. Big Data – Advanced Analytics

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

5. FX Derivatives

5. FX Derivatives

Terminology and notations

Currency options

Exchange options

Quanto options

Summary

References

6. Interest Rate Derivatives and Models

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

7. Exotic Options

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

8. Optimal Hedging

8. Optimal Hedging

Hedging of derivatives

Hedging in the presence of transaction costs

Further extensions

Summary

References

9. Fundamental Analysis

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

10. Technical Analysis, Neural Networks, and Logoptimal Portfolios

10. Technical Analysis, Neural Networks, and Logoptimal Portfolios

Market efficiency

Technical analysis

Neural networks

Logoptimal portfolios

Summary

References

11. Asset and Liability Management

11. Asset and Liability Management

Data preparation

Interest rate risk measurement

Liquidity risk measurement

Modeling non-maturity deposits

Summary

References

12. Capital Adequacy

12. Capital Adequacy

Principles of the Basel Accords

Risk measures

Risk categories

Summary

References

13. Systemic Risks

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

Index

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Implementation in R

In this section, we show how to implement the model of Bialkowski, J., Darolles, S., and Le Fol, G. (2008) in R. We cover every detail, from loading the data to estimating the model parameters and producing the actual forecasts.

The data

The data we use consists of 10 different stocks from the Dow Jones Industrial Average index (see the next table for an overview). We use the 21 trading days between 06/01/2011 and 06/29/2011. Trading on NYSE and NASDAQ is continuous between 09:30 and 16:00. After aggregating the data into 15-minute time slots, we receive 26 observations every day, and a total of 26 * 21 = 546 observations overall.

Tip

We divided the trading day into 26 time slots, whereas the original article defined 25. This is due to the difference in the opening hours of the different markets from where data was drawn. This only changes one single parameter in the model, but some attention must be paid to this detail.

All the used stocks are liquid enough to have positive...

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Programming languages

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