Chaotic features in Romanian transition economy as reflected onto the currency exchange rate

This work is focused on the study of the existence of elements of deterministic chaos in the exchange rate of Romanian national currency (ROL) with respect to the United States dollar (USD). The temporal evolution between 1 January 1990 and 30 June 2005 is related to the particular Romanian economic transition from a centralized economy toward an open system. While insulating the short run behaviour, we consider the correlation dimension, the positive largest Lyapunov exponent and the Hurst exponent as the most important pointers for chaotic dynamics. By taking into account the main events occurring in the political and economical environment, we split the nearly 16 years period in two intervals, that we classify as “passive transition” and “active transition”. Despite of several quantitative differences, we find evidence of chaotic dynamics in both of them. We also find arguments to state that Romania is close to reaching a functional market economy.

[1]  A characteristic time scale in dollar–yen exchange rates , 2001 .

[2]  William A. Barnett,et al.  The Aggregation-Theoretic Monetary Aggregates Are Chaotic and Have Strange Attractors: An Econometric Application of Mathematical Chaos , 2004 .

[3]  Ruedi Stoop,et al.  Encounter with Chaos , 1992 .

[4]  William A. Barnett,et al.  Dynamic econometric modeling: The aggregation-theoretic monetary aggregates are chaotic and have strange attractors: an econometric application of mathematical chaos , 1988 .

[5]  S. Yousefi,et al.  On complex behavior and exchange rate dynamics , 2003 .

[6]  Xavier Gabaix,et al.  Scaling and correlation in financial time series , 2000 .

[7]  Mikael Bask,et al.  Dimensions and Lyapunov exponents from exchange rate series , 1996 .

[8]  M. Rosenstein,et al.  A practical method for calculating largest Lyapunov exponents from small data sets , 1993 .

[9]  Itinerancy of money. , 2003, Chaos.

[10]  F. Takens Detecting strange attractors in turbulence , 1981 .

[11]  B. LeBaron,et al.  A test for independence based on the correlation dimension , 1996 .

[12]  Ping Chen,et al.  Nonlinear dynamics and evolutionary economics , 1993 .

[13]  H. Kantz,et al.  Nonlinear time series analysis , 1997 .

[14]  Ping Chen,et al.  A Random Walk or Color Chaos on the Stock Market? Time-Frequency Analysis of S&P Indexes , 1996 .

[15]  A. N. Sharkovskiĭ Dynamic systems and turbulence , 1989 .

[16]  V. Litvin Multiscaling behavior in transition economies , 2004 .

[17]  William A. Barnett,et al.  Dynamic Econometric Modeling , 1988 .

[18]  Mikael Bask,et al.  A positive Lyapunov exponent in Swedish exchange rates , 2002 .

[19]  James P. Crutchfield,et al.  Geometry from a Time Series , 1980 .

[20]  P. Grassberger,et al.  Measuring the Strangeness of Strange Attractors , 1983 .

[21]  F. Westerhoff Expectations driven distortions in the foreign exchange market , 2003 .

[22]  C. Nelson,et al.  Trends and random walks in macroeconmic time series: Some evidence and implications , 1982 .

[23]  Fluctuation dynamics of exchange rates on Polish financial market , 2004 .

[24]  高安 秀樹,et al.  Empirical science of financial fluctuations : the advent of econophysics , 2002 .