#### Overview of this book

Introduction to R for Quantitative Finance will show you how to solve real-world quantitative fi nance problems using the statistical computing language R. The book covers diverse topics ranging from time series analysis to fi nancial networks. Each chapter briefl y presents the theory behind specific concepts and deals with solving a diverse range of problems using R with the help of practical examples.This book will be your guide on how to use and master R in order to solve quantitative finance problems. This book covers the essentials of quantitative finance, taking you through a number of clear and practical examples in R that will not only help you to understand the theory, but how to effectively deal with your own real-life problems.Starting with time series analysis, you will also learn how to optimize portfolios and how asset pricing models work. The book then covers fixed income securities and derivatives such as credit risk management.
Introduction to R for Quantitative Finance
Credits
www.PacktPub.com
Preface
Free Chapter
Time Series Analysis
Portfolio Optimization
Asset Pricing Models
Fixed Income Securities
Estimating the Term Structure of Interest Rates
Derivatives Pricing
Credit Risk Management
Extreme Value Theory
References
Index

## Contribution to systemic risk – identification of SIFIs

A complex system is not simply the sum of its elements. It is possible that all entities are safe in themselves, but the system as a whole is still vulnerable. Systemic risk is the risk of the entire system collapsing due to one or several shocks. If we wish to identify the systemically important financial institutions (SIFIs) as it was proposed by BCBS (2011), we have to consider five factors contributing to systemic risk: size, interconnectedness, lack of substitutes, cross-jurisdictional activity, and complexity of the activities. When measuring interconnectedness, we can rely on network data and can apply several methods, for example, centrality measures, stress test, and core-periphery models.

Now, we illustrate the first method based on an index of some centrality measures, as described in Komárková et al.(2012) and von Peter (2007). Banks with the highest index-value can be considered as the most central ones, thus with the most...