Long Memory In Futures Prices (original) (raw)
Related papers
Long memory in energy futures prices
Review of Financial Economics, 2008
This paper extends the work in Serletis . Unit root behavior in energy futures prices. The Energy Journal 13, 119-128] by re-examining the empirical evidence for random walk type behavior in energy futures prices. It tests for fractional integrating dynamics in energy futures markets utilizing more recent data (from January 3, 1994 to June 30, 2005) and a new semi-parametric wavelet-based estimator, which is superior to the more prevalent GPH estimator (on the basis of Monte-Carlo evidence). We find new evidence that energy prices display long memory and that the particular form of long memory is anti-persistence, characterized by the variance of each series being dominated by high frequency (low wavelet scale) components.
International Review of Financial Analysis, 2013
This paper analyses the long-memory properties of a high-frequency financial time series dataset. It focuses on temporal aggregation and other features of the data, and how they might affect the degree of dependence of the series. Fractional integration or I(d) models are estimated with a variety of specifications for the error term. In brief, we find evidence that a lower degree of integration is associated with lower data frequencies. In particular, when the data are collected every 10 minutes there are several cases with values of d strictly smaller than 1, implying mean-reverting behaviour; however, for higher data frequencies the unit root null cannot be rejected. This holds for all four series examined, namely Open, High, Low and Last observations for the US dollar / British pound spot exchange rate and for different sample periods.
FRACTIONAL DIFFERENCING MODELING AND FORECASTING OF EUROCURRENCY DEPOSIT RATES
Journal of Financial Research, 1997
Using the spectral regression method, we test for long-term stochastic memory in three- and six-month daily returns series of Eurocurrency deposits denominated in major currencies. Significant evidence of positive long-term dependence is found in several Eurocurrency returns series. Compared with benchmark linear models, the estimate fractional models result in dramatic out-of-sample forecasting improvements over longer horizons for the Eurocurrency deposits
Modelling Long Memory Volatility in Agricultural Commodity Futures Returns
SSRN Electronic Journal, 2000
This paper estimates the long memory volatility model for 16 agricultural commodity futures returns from different futures markets, namely corn, oats, soybeans, soybean meal, soybean oil, wheat, live cattle, cattle feeder, pork, cocoa, coffee, cotton, orange juice, Kansas City wheat, rubber, and palm oil. The class of fractional GARCH models, namely the FIGARCH model of , FIEGACH model of , and FIAPARCH model of Tse (1998), are modelled and compared with the GARCH model of Bollerslev (1986), EGARCH model of Nelson (1991), and APARCH model of Ding et al. (1993). The estimated d parameters, indicating long-term dependence, suggest that fractional integration is found in most of agricultural commodity futures returns series. In addition, the FIGARCH (1,d,1) and FIEGARCH(1,d,1) models are found to outperform their GARCH(1,1) and EGARCH(1,1) counterparts.
High and Low Intraday Commodity Prices: A Fractional Integration and Cointegration Approach
2018
This paper examines the behaviour of high and low prices of four commodities, namely crude oil, natural gas, gold and silver, and of the corresponding ranges using both daily and intraday data at various frequencies. For this purpose, it applies fractional integration and cointegration techniques; in particular, an FCVAR model is estimated to capture both the long-run equilibrium relationships between high and low commodity prices, referred to as the range, and the long-memory properties of their linear combination. Fractional cointegration in found in all cases, with the range showing stationary and nonstationary patterns and changing substantially across the frequencies. The findings may assist investors in improving their trading strategies since high and low prices serve as entry and exit signals in the market.
Económico Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models
2016
In recent years fractionally differenced processes have received a great deal of attention due to its flexibility in financial applications with long memory. This paper considers a class of models generated by Gegenbauer polynomials, incorporating the long memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the statistical properties of the new model, suggest using the spectral likelihood estimation for long memory processes, and investigate the finite sample properties via Monte Carlo experiments. We apply the model to three exchange rate return series. Overall, the results of the out-of-sample forecasts show the adequacy of the new GLMSV model.
Energy for Sustainable Development, 2009
Nigeria experienced a long history of macroeconomic instability since political independence. For many years, Nigerian policymakers had been unsuccessful in their attempts to manage the national economy to deal with macroeconomic instability. They have been accused of mismanagement and incompetence when it comes to preparation and execution of national budgets. In an effort to arrest this problem, the federal government recently introduced structure into public budgeting. A Fiscal Responsibility Act was passed to improve the budgetary process. Consistent with the law, policymakers are required to devise a process for forecasting revenue. Thus, with the help of experts from the International Monetary Fund, an ARMA(p,q) (autoregressive moving average) process was recommended. An ARMA model combines an autoregressive component which captures past values of a time series with a moving average component which captures past forecast errors associated with the time series. As a mono-cultural economy with crude oil as the main source of government revenue, an ARMA(p,q) process assumes that the oil price series is stationary with a short memory. In this paper, we set out to test for the presence of long memory or persistence in oil price series using weekly data from 1978 to 2007. The results of the detrended fluctuation analysis (DFA) suggest Nigeria's oil price series is anti-persistent. A Hurst exponent of 0.48 is indicative of a time series which is covariance stationary but mean-reverting. In other words, we can fit a stationary ARMA(p,q) model driven by fractional noise to the series. This implies that a low price level has a tendency to be followed by a high price level and vice versa. Thus, past price trends are more likely to change in the future. Moreover, the effect of shocks to the price system dies away in the long run. Therefore, the current revenue forecasting tool would need to be overhauled in the presence of price reversals. This is more critical in the face of constant disruptions to oil production in the Niger Delta where the Forcados variety is produced.
Time scale and fractionality in financial time series
Agricultural Finance Review
Purpose – Turvey (2007, Physica A) introduced a scaled variance ratio procedure for testing the random walk hypothesis (RWH) for financial time series by estimating Hurst coefficients for a fractional Brownian motion model of asset prices. The purpose of this paper is to extend his work by making the estimation procedure robust to heteroskedasticity and by addressing the multiple hypothesis testing problem. Design/methodology/approach – Unbiased, heteroskedasticity consistent, variance ratio estimates are calculated for end of day price data for eight time lags over 12 agricultural commodity futures (front month) and 40 US equities from 2000-2014. A bootstrapped stepdown procedure is used to obtain appropriate statistical confidence for the multiplicity of hypothesis tests. The variance ratio approach is compared against regression-based testing for fractionality. Findings – Failing to account for bias, heteroskedasticity, and multiplicity of testing can lead to large numbers of err...