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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
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Toc

Table of Contents (15) Chapters Close

Preface 1. Time Series Analysis FREE CHAPTER 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

The dataset used in our examples

In this chapter, we will use a fictional banking system and its interbank deposit market. We use this market as it usually has the biggest potential loss because these transactions are not collateralized.

For this analysis, we need a connected network, so we constructed one. This network should contain information on the exposure of banks against each other. Usually, we have data on the transaction, like in Table 13.1. Since the average maturity of transactions is very low on the interbank market, it is also possible to use this data. For example, we can construct the network by using the average monthly transaction size between every pair of banks. For this type of analysis, only the partners of each transaction and the contract sizes matter.

The dataset used in our examples

Table 13.1: The data set of the transaction

With all this information, we can put together the matrix of a financial market (which can be visualized as a network).

The dataset used in our examples

The matrix used

The first step will be the core-periphery...

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