Financial Risk Forecasting is a complete introduction to
practical quantitative risk management, with a focus on market
risk. Derived from the authors teaching notes and years spent
training practitioners in risk management techniques, it brings
together the three key disciplines of finance, statistics and
modeling (programming), to provide a thorough grounding in risk
management techniques.
Written by renowned risk expert Jon Danielsson, the book begins
with an introduction to financial markets and market prices,
volatility clusters, fat tails and nonlinear dependence. It then
goes on to present volatility forecasting with both univatiate and
multivatiate methods, discussing the various methods used by
industry, with a special focus on the GARCH family of models. The
evaluation of the quality of forecasts is discussed in detail.
Next, the main concepts in risk and models to forecast risk are
discussed, especially volatility, value-at-risk and expected
shortfall. The focus is both on risk in basic assets such as stocks
and foreign exchange, but also calculations of risk in bonds and
options, with analytical methods such as delta-normal VaR and
duration-normal VaR and Monte Carlo simulation. The book then moves
on to the evaluation of risk models with methods like backtesting,
followed by a discussion on stress testing. The book concludes by
focussing on the forecasting of risk in very large and uncommon
events with extreme value theory and considering the underlying
assumptions behind almost every risk model in practical use -
that risk is exogenous - and what happens when those
assumptions are violated.
Every method presented brings together theoretical discussion
and derivation of key equations and a discussion of issues in
practical implementation. Each method is implemented in both MATLAB
and R, two of the most commonly used mathematical programming
languages for risk forecasting with which the reader can implement
the models illustrated in the book.
The book includes four appendices. The first introduces basic
concepts in statistics and financial time series referred to
throughout the book. The second and third introduce R and MATLAB,
providing a discussion of the basic implementation of the software
packages. And the final looks at the concept of maximum likelihood,
especially issues in implementation and testing.
The book is accompanied by a website - href="http://www. financialriskforecasting. com/">www. financialriskforecasting. com
- which features downloadable code as used in the book.
