Long Memory In Futures Prices

Abstract

This paper tests for fractional roots in the futures prices for selected commodities, foreign currencies, and stock indexes. The fractional testing method is the spectral regression method suggested by Geweke and Porter-Hudak (1983). The empirical results suggest the presence of a fractional exponent in the differencing process for several commodity and foreign currency futures prices. The returns series for these commodities and currencies exhibit long range positive dependence. However, differencing of exact order one is sufficient for the stock index futures prices. Implications are drawn concerning theoretical and econometric modeling and price forecasting.

Detecting Chaos from Agricultural Product Price Time Series

“…Corazza et al [16] discussed the fractal structure in agricultural futures markets. Barkoulas et al [17] researched the long memory effect in futures prices. Wei and Raymond [18] applied R/S analysis, modified R/S analysis, and the AFIMA model to discuss the long memory in six agricultural futures markets.…”

“…For the nonlinear, fractal, and chaotic features in agricultural markets, we refer to the articles [16][17][18][19][20][21][22]. Corazza et al [16] discussed the fractal structure in agricultural futures markets.…”

Long memory and structural breaks in commodity futures markets

“…Our results 22 See, inter alia, Barkoulas, Labys, and Onochie (1999); Crato and Ray (2000); Yang et al (2001); Baillie, Han, Myers, and Song (2007); Elder and Jin (2007). 23 A 20-year sample period is considered as an appropriate period by recent studies such as Barkoulas et al (1999), Crato and Ray (2000), and Baillie et al (2007).…”

A new method of large-scale short-term forecasting of agricultural commodity prices: illustrated by the case of agricultural markets in Beijing

“…Granger [7] pointed out that over-difference happened when using ARIMA model to deal with data which have long-term memory, therefore proposed autoregressive fractionally integrated moving average (ARFIMA) model. Barkoulas et al [8] computed the fractional difference of futures price of agricultural commodities and found that some futures prices had long-term memory, thus met the requirement of ARFIMA model. ARIMA model ignored the influence of other factors on price.…”