The modern approaches to forecasting critical conditions and catastrophic events in complex economic systems are introduced. Examples of application of quantitative methods of the economic risk theory to analysis of stability and diagnostics in economic systems based on statistical and/or nonlinear processing of time series are represented. The main attention is devoted to ways of holistic modeling of stochastic economic system behavior and problems of quantitative estimation of economic risks.
The textbook is designed for students of economic, marketing, management departments and for everyone who is interested in the problem of risk analysis in complex economic systems.
O. Y. Uritskaya. Introduction to Economic Risk Theory: Lecture Textbook // St. Petersburg: SPbSTU Press, 1999.
Fig. 1. An example of bifurcation in model time series.
Fig. 2. Mont
hly fluctuations in world prices for gold (top) and the corresponding simulated cycle. (Cycles, 1993).
Fig. 3. Dependence of the form of the Pareto distribution tail on the Pareto index value.
Contents

The subject of economic risk theory

Origin of risk in economic system
 Characteristics of economic systems

Modeling of economic processes

Time series analysis

 Basis of economic risk theory

Selforganized criticality theory
 Selforganized criticality models
 Fractals and their properties

Fractal objects

Fractal properties of economic systems

 Methodology of economic risk theory

Economic risk estimation

Data preparation (measurements, aggregation, calendar problems)
 Fractal dimension of time series (ruler method, box counting statistics, correlation function method, spectral method, Hurst rescaled range analysis)

Forecasting longterm tendencies on the base of time series fractal dimension

Analysis of system stability and catastrophe forecasting

Risk estimation by statistical methods

Risk estimation by fractal methods


Problems