Financial Risk Forecasting

## Financial Risk Forecasting:

#### The Theory and Practice of Forecasting Market Risk

##### Financial Risk Forecasting provides a thorough introduction to practical quantitative risk management, with an emphasis on market risk. It brings together the three key disciplines of finance, statistics, and modelling to provide a solid grounding in risk management techniques, and is based on the author's teaching notes and years of training practitioners in risk management techniques. These pages supplement the R and Matlab book code with Julia and Python implementations. A detailed workbook on how to implement risk forecasting in R is also provided. In addition, we provide questions and solutions for weekly seminar sessions, emphasising how to put each chapter of the book into practise.

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. This website further provides an implementation of the methods in Python and Julia. The code can be downloaded from the code webpage.

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 website further provides eight weekly seminar sessions used by Jon Danielsson in his lectures, along with both questions were students and assignments.

finally, the website provides a detailed notebook outlining the various issues that arise in implementing market risk forecasts in practice. The notebook currently is based on R, with other languages being planned.

#### Professor Oliver B. Linton

##### Professor of Econometrics, University of Cambridge

This is an outstanding book on empirical finance. I wholeheartedly recommend it.

#### Professor Xavier Freixas

##### Universitat Pompeu Fabra

More than ever risk managers in financial institutions have to assess the risk of financial products and portfolios in a rigorous way. With his new book, Professor Danielsson has risen to the task and produced a great book that combines his expertise with years of teaching market risk at LSE and other major universities. With perfect timing, this book achieves two objectives the academic and scientific community had to face: on the one hand it addresses the latest analytical techniques in the exact computation of risk measures, their use and their limitations, and on the other hand it considers the issue of risk pricing during a crisis. A real accomplishment and a must read for both risk professionals and students in the quantitative finance track.

#### Professor Casper de Vries

##### Chair of Monetary Economics, Departments of Economics and Business, School of Economics, Erasmus University Rotterdam

I believe that this book covers the spectrum of quantitative techniques that any student of risk management should cover. The book moves gradually from traditional risk measures to downside risk measures and their application in stress testing. Advanced estimation of volatility models and use of extreme value theory are not eschewed and are the way to go for scenario analysis. A great added value of the book is the programs for all routines both in R and MATLAB®. The book ventures into the barren area of endogeneity of risk drivers. If I have to make a prediction, I would venture that this will keep scientists and markets busy for years to come. In short, a highly recommended book for any student of modern risk management techniques and their uses.

#### Con Keating

##### Market Structure Commission, European Federation of Financial Analysts’ Societies

Financial Risk Forecasting is a tour de force. It is one of those rare works which successfully combine accessibility with academic rigour; it is copiously and most informatively illustrated. The addition of computer code, in commonly-used programming languages, for the implementation of concepts and techniques demonstrates a profound understanding of practical issues. With risk-based regulation now dominating the financial landscape post-crisis, this book is a timely and authoritative resource for both students and practising financial analysts, of whatever stripe. It will join that select group of works on my bookshelf that have become dog-eared from repeated use over the years.

##### Financial Risk Forecasting
Market risk forecasting with R, Julia, Python and Matlab. Code, lecture slides, implementation notes, seminar assignments and questions.