"Prediction is difficult, especially of the future.
" Niels Bohr'
This thesis consists of two parts in which neither part deals explicitly with prediction. However, the difficulties of prediction are both the origin of and the common link in this thesis, even though each part tackles the problem from a different point of view. That prediction is a problem for financial markets is revealed by the very existence of insurance arrangements for those markets - if the future was not associated with uncertainty, there would be no risks and insurance markets would not exist.
The first part of this thesis, which consists of Papers [i]-[iii], deals with a possible explanation of why prediction is difficult, namely, that observed time series may be chaotic. More specifically, this part of the thesis proposes a method to answer the following question: are observed time series, e.g. exchange rate series, generated by a chaotic dynamical system? If so, the predictive ability of the system is strongly limited, especially for long-run predictions.
The second part of this thesis, which consists of Paper [iv], deals with a simple solution to the prediction problem, namely, that there is predictive power in certain trading rules, i. e. in technical analysis.
Two questions, which are also more important than the possibility of chaos, are in focus in this part of the thesis. They are as follows: are technical analysts in the foreign exchange market destabilizing actors in the economy? If so, are there any stability conditions that prevent the economy from "exploding"? The remainder of this Introduction and Summary of Papers is organized as follows.
Section 2 contains a review on how to detect chaotic dynamics in observed time series. Short summaries of Papers [i]-[iii] are also included. Section 3 contains a short introduction to technical analysis in general and to theories of endogenous speculative bubbles in particular. A summary of Paper [iv] is also included in this section. Concluding remarks can be found in Section 4.
This quotation can be found iii Peitgen et al. (1992).
Next: Detecting chaotic dynamics in observed time series
Summary: Index