Kirjahaku

Global Financial Systems is an innovative, interdisciplinary text that explores the â??whyâ?? behind global financial stability. Danielsson draws on economic theory, finance, mathematical modelling, risk theory, and policy to posit a coherent and current analysis of the global financial system.

A challenge to the conventional wisdom surrounding financial risk, providing insight into why easy solutions to control the financial system are doomed to fail Finance plays a key role in the prosperity of the modern world, but it also brings grave dangers. We seek to manage those threats with a vast array of sophisticated mathematical tools and techniques of financial risk management. Too often, though, we fail to address the greatest risk—the peril posed by our own behavior. Jón Daníelsson argues that critical risk is generated from within, through the interactions of individuals and perpetuated by their beliefs, objectives, abilities, and prejudices. He asserts that the widespread belief that risk originates outside the financial system frustrates our ability to measure and manage it, and the likely consequences of new regulations will help alleviate small-scale risks but, perversely, encourage excessive risk taking. Daníelsson uses lessons from past and recent crises to show that diversity is the best way to safeguard our financial system.

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 - www.financialriskforecasting.com – which features downloadable code as used in the book.