This volume provides practical solutions and introduces recent
theoretical developments in risk management, pricing of credit
derivatives, quantification of volatility and copula modeling. This
third edition is devoted to modern risk analysis based on quantitative
methods and textual analytics to meet the current challenges in banking
and finance. It includes 14 new contributions and presents a
comprehensive, state-of-the-art treatment of cutting-edge methods and
topics, such as collateralized debt obligations, the high-frequency
analysis of market liquidity, and realized volatility.
The book is divided into three parts: Part 1 revisits important market
risk issues, while Part 2 introduces novel concepts in credit risk and
its management along with updated quantitative methods. The third part
discusses the dynamics of risk management and includes risk analysis of
energy markets and for cryptocurrencies. Digital assets, such as
blockchain-based currencies, have become popular b
ut are theoretically challenging when based on conventional methods.
Among others, it introduces a modern text-mining method called dynamic
topic modeling in detail and applies it to the message board of
Bitcoins.
The unique synthesis of theory and practice supported by computational
tools is reflected not only in the selection of topics, but also in the
fine balance of scientific contributions on practical implementation and
theoretical concepts. This link between theory and practice offers
theoreticians insights into considerations of applicability and, vice
versa, provides practitioners convenient access to new techniques in
quantitative finance. Hence the book will appeal both to researchers,
including master and PhD students, and practitioners, such as financial
engineers. The results presented in the book are fully reproducible and
all quantlets needed for calculations are provided on an accompanying
website.
The Quantlet platform quantlet.de, quantlet.com, quantlet.org is an
integrated QuantNet environment consisting of different types of
statistics-related documents and program codes. Its goal is to promote
reproducibility and offer a platform for sharing validated knowledge
native to the social web. QuantNet and the corresponding Data-Driven
Documents-based visualization allows readers to reproduce the tables,
pictures and calculations inside this Springer book.
