Time vs. Ensemble Averages for Nonstationary Time Series
arXiv:0804.0902 · doi:10.1016/j.physa.2008.05.057
Abstract
We analyze the question whether sliding window time averages applied to stationary increment processes converge to a limit in probability. The question centers on averages, correlations, and densities constructed via time averages of the increment x(t,T)=x(t+T)-x(t)and the assumption is that the increment is distributed independently of t. We show that the condition for applying Tchebyshev's Theorem to time averages of functions of stationary increments is strongly violated. We argue that, for both stationary and nonstationary increments, Tchebyshev's Theorem provides the basis for constructing emsemble averages and densities from a single, historic time series if, as in FX markets, the series shows a definite statistical periodicity on the average.
References in corpus (5)
- Universal and non-universal properties of cross-correlations in financial time series
- Markov Processes, Hurst Exponents, and Nonlinear Diffusion Equations with application to finance
- Nonstationary Increments, Scaling Distributions, and Variable Diffusion Processes in Financial Markets
- Ensemble properties of securities traded in the NASDAQ market
- Integration I(d) of Nonstationary Time Series: Stationary and nonstationary increments