Early warnings for turns in business cycles and finance
In many areas, it is important to detect turning points in time series early and without faults. Turns in business cycles and financial time series are discussed here. A variety of approaches for analyzing the turns in cyclical processes has been proposed. Some of the proposed techniques aim at point prediction of all values of the process. These techniques often give very low accuracy near turns. Other approaches concentrate on predicting the time of the turn. In this thesis we consider prospective monitoring of a stochastic process in order to call an alarm as soon as we have enough evidence that the critical event of interest has occurred. Statistical surveillance deals with the theory and methodology of online detection of an important change in the underlying process of a time series as soon as possible after it has occurred. The theory of statistical surveillance is used in this thesis to construct and compare methods for two important applications. In the first paper, three likelihood-based methods for detection of a tum are compared. The problem is being addressed in a business cycle context. We aim at timely detecting a turning point in a leading indicator of the business cycle. By detecting the turns in a leading indicator we have an instrument for predicting the turns in the business cycle. One of the methods is based on a hidden Markov model. The two others are based on the theory of statistical surveillance. One of these is free from parametric assumptions of the curve shape. Evaluations are made of e.g. the effects of different specifications of the curve and of the transition probabilities. The second paper investigates inferential differences and similarities between statistical surveillance and some prospective decision rules suggested for trading in financial markets. It is found that the proposed rules can be seen as special cases of classical methods of surveillance and can hence be discussed in the context of optimality properties of surveillance methods. Evaluation measures and utility functions commonly used in statistical surveillance are compared with those generally used in financial settings. Some of the methods are evaluated by case studies. The relative merits of case studies and Monte Carlo studies are discussed.
University of Gothenburg