We elucidate on several empirical statistical observations of stock market returns. Moreover, we find that these properties are recurrent and are also present in invariant measures of low-dimensional dynamical systems. Thus, we propose that the returns are modeled by the first Poincare return time of a low-dimensional chaotic trajectory. This modeling, which captures the recurrent properties of the return fluctuations, is able to predict well the evolution of the observed statistical quantities. In addition, it explains the reason for which stocks present simultaneously dynamical properties and high uncertainties. In our analysis, we use data from the S&P 500 index and the Brazilian stock Telebris. (C) 2002 Elsevier Science B.V. All rights reserved.
|Number of pages||26|
|Journal||Physica. A, Statistical Mechanics and its Applications|
|Early online date||16 May 2002|
|Publication status||Published - 15 Sep 2002|
- stock market