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On Determining the Dimension of Real-Time Stock-Price Data
E. Scott Mayfield and Bruce Mizrach
Journal of Business & Economic Statistics
Vol. 10, No. 3 (Jul., 1992), pp. 367-374
Stable URL: http://www.jstor.org/stable/1391548
Page Count: 8
You can always find the topics here!Topics: Entropy, Statistical estimation, Correlations, Time series, Stock market indices, Economic statistics, Dynamical systems, Autoregressive moving average, Empirical evidence, Chaos theory
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We estimate the dimension of high-frequency stock-price data using the correlation integral of Grassberger and Procaccia. The data, even after filtering, appear to be of low dimension. To control for dependence in higher moments, we use a new technique known as the method of delays in our reconstruction. Delaying the data leads dimension estimates similar to random processes. We conclude that the data are either of low dimension with high entropy or nonlinear but of high dimension.
Journal of Business & Economic Statistics © 1992 American Statistical Association