Financial Risk Forecasting

The Theory and Practice of Forecasting Market Risk with Implementation in R and Matlab

Financial Risk Forecasting

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.

Quantitative Financial Risk Management

Quantitative Financial Risk Management

The bulk of this volume deals with the four main aspects of risk management: market risk, credit risk, risk management - in macro-economy as well as within companies. It presents a number of approaches and case studies directed at applying risk management to diverse business environments. Included are traditional market and credit risk management models such as the Black-Scholes Option Pricing Model, the Vasicek Model, Factor models, CAPM models, GARCH models, KMV models and credit scoring models.

Forecasting Extreme Financial Risk

A Critical Analysis of Practical Methods for the Japanese Market

Forecasting Extreme Financial Risk


Nonlinear Financial Econometrics: Forecasting Models, Computational and Bayesian Models

Nonlinear Financial Econometrics: Forecasting Models, Computational and Bayesian Models

This book investigates several competing forecasting models for interest rates, financial returns, and realized volatility, addresses the usefulness of nonlinear models for hedging purposes, and proposes new computational techniques to estimate financial processes.

Quantifying Systemic Risk

Quantifying Systemic Risk

In the aftermath of the recent financial crisis, the federal government has pursued significant regulatory reforms, including proposals to measure and monitor systemic risk. However, there is much debate about how this might be accomplished quantitatively and objectively—or whether this is even possible. A key issue is determining the appropriate trade-offs between risk and reward from a policy and social welfare perspective given the potential negative impact of crises. One of the first books to address the challenges of measuring statistical risk from a system-wide persepective, Quantifying Systemic Risk looks at the means of measuring systemic risk and explores alternative approaches. Among the topics discussed are the challenges of tying regulations to specific quantitative measures, the effects of learning and adaptation on the evolution of the market, and the distinction between the shocks that start a crisis and the mechanisms that enable it to grow.

The Analytics of Risk Model Validation

The Analytics of Risk Model Validation

Risk model validation is an emerging and important area of research, and has arisen because of Basel I and II. These regulatory initiatives require trading institutions and lending institutions to compute their reserve capital in a highly analytic way, based on the use of internal risk models. It is part of the regulatory structure that these risk models be validated both internally and externally, and there is a great shortage of information as to best practise. Editors Christodoulakis and Satchell collect papers that are beginning to appear by regulators, consultants, and academics, to provide the first collection that focuses on the quantitative side of model validation. The book covers the three main areas of risk: Credit Risk and Market and Operational Risk. *Risk model validation is a requirement of Basel I and II *The first collection of papers in this new and developing area of research *International authors cover model validation in credit, market, and operational risk

Systemic Real and Financial Risks

Measurement, Forecasting, and Stress Testing

Systemic Real and Financial Risks

This paper formulates a novel modeling framework that delivers: (a) forecasts of indicators of systemic real risk and systemic financial risk based on density forecasts of indicators of real activity and financial health; (b) stress-tests as measures of the dynamics of responses of systemic risk indicators to structural shocks identified by standard macroeconomic and banking theory. Using a large number of quarterly time series of the G-7 economies in 1980Q1-2010Q2, we show that the model exhibits significant out-of sample forecasting power for tail real and financial risk realizations, and that stress testing provides useful early warnings on the build-up of real and financial vulnerabilities.

Business Intelligence in Economic Forecasting: Technologies and Techniques

Technologies and Techniques

Business Intelligence in Economic Forecasting: Technologies and Techniques

With the rapid development of economic globalization and information technology, the field of economic forecasting continues its expeditious advancement, providing business and government with applicable technologies. This book discusses various business intelligence techniques including neural networks, support vector machine, genetic programming, clustering analysis, [email protected], fuzzy systems, text mining, and many more. It serves as a valuable reference for professionals and researchers interested in BI technologies and their practical applications in economic forecasting, as well as policy makers in business organizations and governments.

Uneconomic Economics and the Crisis of the Model World

Uneconomic Economics and the Crisis of the Model World

What has gone wrong with economics? Economists now routinely devise highly sophisticated abstract models that score top marks for theoretical rigour but are clearly divorced from observable activities in the current economy. This creates an 'uneconomic economics', where models explain relationships in blackboard rather than real-life markets.