Volume 84, Number 2, October 2008
|Number of page(s)||5|
|Section||Interdisciplinary Physics and Related Areas of Science and Technology|
|Published online||26 September 2008|
Skewness, long-time memory, and non-stationarity: Application to leverage effect in financial time series
Dipartimento di Fisica, Università di Milano-Bicocca - Piazza della Scienza 3, 20126 Milano, Italy, EU
2 Institut für Festkörperphysik, Technische Universität Darmstadt - Hochschulstr. 8, 64289 Darmstadt, Germany, EU
3 Hewlett-Packard - Via Giuseppe Di Vittorio 9, 20063 Cernusco sul Naviglio (MI), Italy, EU
Corresponding author: email@example.com
Accepted: 3 September 2008
We analyze daily log-returns data for a set of 1200 stocks, taken from US stock markets, over a period of 2481 trading days (January 1996November 2005). We estimate the degree of non-stationarity in daily market volatility employing a polynomial fit, used as a detrending function. We find that the autocorrelation function of absolute detrended log-returns departs strongly from the corresponding original data autocorrelation function, while the observed leverage effect depends only weakly on trends. Such effect is shown to occur when both skewness and long-time memory are simultaneously present. A fractional derivative random walk model is discussed yielding a quantitative agreement with the empirical results.
PACS: 89.65.Gh – Economics; econophysics, financial markets, business and management / 05.45.Tp – Time series analysis / 05.40.-a – Fluctuation phenomena, random processes, noise, and Brownian motion
© EPLA, 2008
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