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Mastering R for Quantitative Finance

Mastering R for Quantitative Finance

By : Gabler
4 (11)
close
Mastering R for Quantitative Finance

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.
Table of Contents (15 chapters)
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14
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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