From turbulence to financial time series

We develop a framework especially suited to the autocorrelation properties observed in financial times series, by borrowing from the physical picture of turbulence. The success of our approach as applied to high frequency foreign exchange data is demonstrated by the overlap of the curves in Figure (1), since we are able to provide an analytical derivation of the relative sizes of the quantities depicted. These quantities include departures from Gaussian probability density functions and various two and three-point autocorrelation functions.Comment: 10 pages, 1 figure, LaTeX, version to appear in Physica

Time Series: Economic Forecasting

Time-series forecasts are used in a wide range of economic activities, including setting monetary and fiscal policies, state and local budgeting, financial management, and financial engineering. Key elements of economic forecasting include selecting the forecasting model(s) appropriate for the problem at hand, assessing and communicating the uncertainty associated with a forecast, and guarding against model instability. 1. Time Series Models for Economic Forecasting Broadly speaking, statistical approaches to economic forecasting fall into two categories: time-series methods and structural economic models. Time-series methods use economic theory mainly as a guide to variable selection, and rely on past patterns in the dat

Autoregressive approximation in nonstandard situations: the fractionally integrated and non-invertible cases

Autoregression, Autoregressive approximation, Fractional process, Non-invertibility, Order selection, Asymptotic efficiency,