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Mastering R for Quantitative Finance

You're reading from   Mastering R for Quantitative Finance Use R to optimize your trading strategy and build up your own risk management system

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Product type Paperback
Published in Mar 2015
Publisher
ISBN-13 9781783552078
Length 362 pages
Edition 1st Edition
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Toc

Table of Contents (15) Chapters Close

Preface 1. Time Series Analysis 2. Factor Models FREE CHAPTER 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

The role of dynamic hedging


Most of the time, replication is a dynamic strategy. You should do more or less trading almost continuously during the lifetime of the derivatives. Haug (2007b) shows that the hedging error of non-continuous hedging could be significant even for plain vanilla options. Anyway, continuous hedging is a huge effort, which is often not seen explicitly in the pricing formulas; however, most pricing functions are based on the assumption that dynamic hedging should be done in the background properly all the time. This is also the case whenever we talk about risk-neutral world or the risk-neutral pricing. For further references, see Wilmott (2006).

Luckily, no matter how hard dynamic hedging could be, running an option book is at least a scalable business; hedging thousands of options is not much more difficult than hedging just a couple of them. All options can be decomposed into certain sensitivities, the so-called Greek letters (or simply Greeks). This nickname came...

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