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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

References


  • Bai, J. (2003): Inferential theory for factor models of large dimensions. Econometrica, 71:135-171.

  • Bialkowski, J., Darolles, S., and Le Fol, G. (2008): Improving VWAP strategies: A dynamic volume approach. Journal of Banking & Finance, 32:1709-1722.

  • Brownlees, C. T., Cipollini, F., and Gallo, G. M. (2011): Intra-daily volume modeling and prediction for algorithmic trading. Journal of Financial Econometrics, 9:489-518.

  • Hmaied, D. M., Sioud, O. B., and Grar, A. (2006): Intra-daily and weekly patterns of bid-ask spreads, trading volume and volatility on the Tunisian Stock Exchange. Banque & Marchés, 84:35-44.

  • Hussain, S. M. (2011): The intraday behavior of bid-ask spreads, trading volume, and return volatility: Evidence from DAX30. International Journal of Economics and Finance, 3:23-34.

  • Kaastra, I. and Boyd, M. S. (1995): Forecasting futures trading volume using neural networks. The Journal of Futures Markets, Vol. 15, No. 8,:953-970.

  • Lux, T. and Kaizoji, T. (2004): Forecasting...

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