We study the statistical properties of volatility, measured by locally averaging over a time window T, the absolute value of price changes over a short time interval Δt. We analyze the S&P 500 stock index for the 13-year period Jan. 1984 to Dec. 1996. We find that the cumulative distribution of the volatility is consistent with a power-law asymptotic behavior, characterized by an exponent μ~3, similar to what is found for the distribution of price changes. The volatility distribution retains the same functional form for a range of values of T. Further, we study the volatility correlations by using the power spectrum analysis. Both methods support a power law decay of the correlation function and give consistent estimates of the relevant scaling exponents. Also, both methods show the presence of a crossover at approximately 1.5 days. In addition, we extend these results to the volatility of individual companies by analyzing a data base comprising all trades for the largest 500 U.S. companies over the two-year period Jan. 1994 to Dec. 1995.
Physical Review E
- Pub Date:
- August 1999
- Other topics in areas of applied and interdisciplinary physics;
- Condensed Matter - Statistical Mechanics;
- Quantitative Finance - Statistical Finance
- 34 pages (Revtex pre-print format), 17 figures, 47 references