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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 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

Liquidity risk measurement

Traditional liquidity risk measurement tools are the so-called static and dynamic liquidity gap tables. A liquidity gap table gives a cash-flow view of the balance sheet, and organizes the balance sheet items according to their contractual cash-inflows and cash-outflows into maturity buckets. The net cash-flow gap in each bucket shows the bank structural liquidity position. The static view assumes a rundown balance sheet while the dynamic liquidity table also takes into account the cash-flows from rollovers and new businesses. For the sake of simplicity, we demonstrate here only the static view of the liquidity positions.

Starting with the preparation of daily cash-flow positions. Sometimes, we need to know what the forecasted liquidity position is on a given date. It is easy to aggregate the cashflow.table by date.

head(aggregate(. ~ date, FUN = sum, data = subset(cashflow.table,select = -c(id, account))))
        date            cf    interest    capital remaining...
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