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Mastering R for Quantitative Finance

You're reading from  Mastering R for Quantitative Finance

Product type Book
Published in Mar 2015
Publisher
ISBN-13 9781783552078
Pages 362 pages
Edition 1st Edition
Languages

Table of Contents (20) Chapters

Mastering R for Quantitative Finance
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Time Series Analysis 2. Factor Models 3. Forecasting Volume 4. Big Data – Advanced Analytics 5. FX Derivatives 6. Interest Rate Derivatives and Models 7. Exotic Options 8. Optimal Hedging 9. Fundamental Analysis 10. Technical Analysis, Neural Networks, and Logoptimal Portfolios 11. Asset and Liability Management 12. Capital Adequacy 13. Systemic Risks Index

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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