Correlations in Economic Time Series

نویسندگان

  • Yanhui Liu
  • Pierre Cizeau
  • Martin Meyer
  • Chung-Kang Peng
چکیده

The correlation function of a financial index of the New York stock exchange, the S&P 500, is analyzed at 1min intervals over the 13-year period, Jan 84 – Dec 96. We quantify the correlations of the absolute values of the index increment. We find that these correlations can be described by two different power laws with a crossover time t× ≈ 600min. Detrended fluctuation analysis gives exponents α1 = 0.66 and α2 = 0.93 for t < t× and t > t× respectively. Power spectrum analysis gives corresponding exponents β1 = 0.31 and β2 = 0.90 for f > f× and f < f× respectively. A topic of considerable recent interest to both the economics and physics communities is whether there are correlations in economic time series and, if so, how to best quantify these correlations [1,2,3,4]. Here we study the S&P 500 index of the New York stock exchange over a 13-year period (Fig. 1a). We calculate the logarithmic increments g(t) ≡ lnZ(t+1)−lnZ(t) over a fixed time lag of 1min, where Z(t) denotes the index at time t (t counts the number of minutes during the opening hours of the stock market), and quantify the correlations as follows: (i) We find that the correlation function of g(t) decays exponentially with a characteristic time of the order of 1-10min, but the absolute value |g(t)| does not. This result is consistent with previous studies on several economic series [2,3,4].

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تاریخ انتشار 1997