Academic journal article Academy of Accounting and Financial Studies Journal

Nuances of Chaos in Foreign Exchange Markets

Academic journal article Academy of Accounting and Financial Studies Journal

Nuances of Chaos in Foreign Exchange Markets

Article excerpt

ABSTRACT

Is the economy an evolutionary process? Recently, scientists have begun to think that the economic dynamics of free-market societies can be explained by evolutionary dynamics. If so, on the aggregate level then, foreign exchange markets may be driven by a collective "image of the future" that societies are driven by. When economies are viewed as evolutionary processes, it is just possible that on the aggregate, but a subconscious level, competitive forces in foreign exchange markets become endogenous in a system that drives exchange rates towards a collective futuristic image. Moreover, such a system could be deterministic. This paper investigates such possibility in the daily dollar price movements of five major trading currencies and three less actively traded currencies over a 25-year time span beginning with the inception of the floating exchange rate system in 1973. The results of this study suggest that none of the examined currencies are influenced by low-dimensional chaotic determinism. Although three of the examined exchange rates do exhibit signs of being driven by higher-dimensional chaos, this finding does not significantly favor the possibility of predicting these currency movements. As such, very little evidence of a deterministic driving force behind foreign exchange rates is uncovered in this study.

INTRODUCTION

Of late, there has been some deliberation about viewing economies as evolutionary processes. In such a case, it is just possible that on the aggregate, but a subconscious level, competitive forces in foreign exchange markets become endogenous in a system that drives exchange rates towards a collective "image of the future". Grabbe [1996] presents the possibility of self-organization of human societies, and thus by implication of the economy, with a shared image or a vision of the future. At the singular level, this vision might be subconscious or nonexistent, but at the aggregate level such a vision might be discernible. In the foreign exchange markets, most of the trading occurs while traders are marketmakers or speculators. They may not afford the luxury of acting late on any relevant news. Very often, the trader must anticipate other traders' moves and try to preempt such moves. As such, each trader must not just act on his or her expectations but rather act on anticipation of other traders moves who themselves are trying to anticipate the first's and everyone else's moves and so on. Evolutionary dynamics provide a solution in the form of spontaneous order involving dynamic feedback at a higher, or aggregate, level. In the foreign exchange markets context, what appears to be competition amongst traders and central banks at the lower level, where expectations are generated, functions as co-ordination at the higher (global) level (Grabbe [1996]). Recent research in behavioral economics has also yielded explanations of chaotic influences in economic and financial data series based on equilibrium solutions under conditions of imperfect foresight (Sorger [1996]).

If such is the case, foreign exchange rates may be driven by nonlinear deterministic systems. Recent advances in the study of nonlinear dynamics and chaotic processes have yielded tools that can distinguish stochastic variables from seemingly random data that are, in fact, generated by low-complexity nonlinear deterministic processes. Tests for informational efficiency in foreign exchange markets can now be strengthened by employing these tests for chaotic dynamics among time series of security returns. Since some forms of chaotic determinism can generate seemingly random variates, it is imperative that tests for nonlinear dependencies become an integral part of market efficiency tests.

In examining the pricing efficiency of foreign exchange markets, the vast majority of research has relied on linear modeling techniques, which have serious limitations in detecting multidimensional patterns. …

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