Abstract
We study the distribution of fluctuations of the S&P 500 index over a time scale by analyzing three distinct databases. Database (i) contains approximately 1 200 000 records, sampled at 1-min intervals, for the 13-year period 1984–1996, database (ii) contains 8686 daily records for the 35-year period 1962–1996, and database (iii) contains 852 monthly records for the 71-year period 1926–1996. We compute the probability distributions of returns over a time scale where varies approximately over a factor of —from 1 min up to more than one month. We find that the distributions for 4 d (1560 min) are consistent with a power-law asymptotic behavior, characterized by an exponent well outside the stable Lévy regime To test the robustness of the S&P result, we perform a parallel analysis on two other financial market indices. Database (iv) contains 3560 daily records of the NIKKEI index for the 14-year period 1984–1997, and database (v) contains 4649 daily records of the Hang-Seng index for the 18-year period 1980–1997. We find estimates of consistent with those describing the distribution of S&P 500 daily returns. One possible reason for the scaling of these distributions is the long persistence of the autocorrelation function of the volatility. For time scales longer than d, our results are consistent with a slow convergence to Gaussian behavior.
- Received 20 May 1999
DOI:https://doi.org/10.1103/PhysRevE.60.5305
©1999 American Physical Society