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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
Languages
Tools
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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

Possible interpretations and suggestions

The main difficulty of the examination of systemic importance is always its huge data need. From this point of view, core-periphery decomposition is an easier method because we only need the exposure of the banks on the interbank market. Although in many cases this may also result in some difficulty since direct linkages between banks are often unknown. However, in the literature, we can find some good solutions to fill these gaps, for example, the minimum density approach by Anand et al. (2014). Alternatively, there are some other suggestions on how to create a network from market data (for example, Billio et al., 2013).

Due to the differences between the two methods, the results can be confusing. We will give you some ideas on how to interpret the results. The core-periphery decomposition focuses only on one market. It implies that being in the core means that the bank is important on this market. The importance for the whole banking system then...

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