Time Scales in Futures Markets and Applications
The probability distribution of log-returns for financial time series, sampled at high frequency, is the basis for any further developments in quantitative finance. In this letter, we present experimental results based on a large set of time series on futures. We show that the t-distribution with $\nu \simeq 3$ gives a nice description of almost all data series considered for a time scale $\Delta t$ below 1 hour. For $\Delta t \ge 8$ hours, the Gaussian regime is reached. A particular focus has been put on the DAX and Euro futures. This appears to be a quite general result that stays robust on a large set of futures, but not on any data sets. In this sense, this is not universal. A technique using factorial moments defined on a sequence of returns is described and similar results for time scales are obtained. Let us note that from a fundamental point of view, there is no clear reason why DAX and Euro futures should present similar behavior with respect to their return distributions. Both are complex markets where many internal and external factors interact at each instant to determine the transaction price. These factors are certainly different for an index on a change parity (Euro) and an index on stocks (DAX). Thus, this is striking that we can identify universal statistical features in price fluctuations of these markets. This is really the advantage of micro-structure analysis to prompt unified approaches of different kinds of markets. Finally, we examine the relation of power law distribution of returns with another scaling behavior of the data encoded into the Hurst exponent. We have obtained $H=0.54 \pm 0.04$ for DAX and $H=0.51 \pm 0.03$ for Euro futures.
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