Long memory in interest rate futures markets: A fractional cointegration analysis

Booth, G. Geoffrey; Tse, Yiuman

doi:10.1002/fut.3990150505

Cited by 51 publications

(34 citation statements)

References 41 publications

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“…To make the three series comparable, we use corn contract months as the reference to define maturity months and nearby contracts when constructing our futures price series. 2 Following previous research (Jin and Frechette, 2004;Booth and Tse, 1995), when a futures contract comes to its maturity month, the nearby contract is used to compile the data. A jump is made to the new nearby contract when the old contract is close to expiration.…”

Section: Resultsmentioning

confidence: 99%

Price Transmission in the US Ethanol Market

Serra

Zilberman

Gil³

et al. 2009

Handbook of Bioenergy Economics and Policy

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We use nonlinear time series models to assess price relationships within the US ethanol industry. Daily ethanol, corn, and crude oil futures prices observed from mid-2005 to mid-2007 are used in the analysis. Our results suggest the existence of an equilibrium relationship between the three prices studied. Only ethanol prices are found to adjust to deviations from this relationship. The evolution of ethanol prices in relation to corn and crude oil prices may have important implications for the long-run competitiveness of the US ethanol industry.

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Section: Resultsmentioning

confidence: 99%

Price Transmission in the US Ethanol Market

Serra

Zilberman

Gil³

et al. 2009

Handbook of Bioenergy Economics and Policy

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“…Fractional integration is often utilized to capture long memory features (cf., Baillie, Bollerslev, & Mikkelsen, 1996;Barkoulas, Labys, & Onochie, 1997;Booth & Tse, 1995;Breidt, Crato, & de Lima, 1998;Cheung & Lai, 1995). Fractional integration is a generalization of integer integration, in which time series are presumed to be integrated of order zero or one.…”

Section: Journal Of Futures Markets Doi: 101002/futmentioning

confidence: 99%

Long memory in commodity futures volatility: A wavelet perspective

Elder

Jin

2007

Journal of Futures Markets

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The authors reexamine the volatility of agricultural commodity futures for evidence of fractional integration, providing new empirical results and extending the extant literature in important dimensions. First, they utilize two relatively new estimators based on wavelets, which are generally superior to, for example, the popular estimator by J. Geweke and S. PorterHudak (GPH; 1983) and exact maximum likelihood estimators (MLEs) on the basis of mean squared error (MSE). Second, they provide simulations to contrast their point estimates with those obtained by a fractionally integrated GARCH (generalized autoregressive conditional heteroscedasticity) model. Third, they conduct a wavelet coefficient decomposition of futures volatility. They find that futures volatilities display the self-similarity property consistent with long memory and that futures volatilities exhibit persistent long memory with finite unconditional variance.

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“…Examples of applied work exploiting this approach include Cheung and Lai (1993), Baillie and Bollerslev (1994), Booth and Tse (1995), Masih and Masih (1995), Sephton (1996), and Duecker and Startz (1998).…”

Section: Long Memory Modelsmentioning

confidence: 99%

Long memory and nonlinear time series

Davidson¹,

Teräsvirta²

2002

Journal of Econometrics

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The last two decades have witnessed tremendous advances in econometric time series research. The linear stationary framework of ARMA and VAR models driven by i.i.d. shocks, which was for many years the cornerstone of econometric modelling, has increasingly given way to methods that can deal with the manifestly nonstationary and nonlinear features of many economic and financial time series. Two types of model in particular have found their way into the mainstream of applied research, the unit-root/cointegration framework for nonstationary time series and the ARCH and related models of conditional heteroscedasticity. Recent research has been aimed at both extending our understanding of these well established models, and widening the range of data features that can be handled. Long memory models generalize the unit root model of nonstationarity, and a range of new models of nonlinear dynamics allow for asymmetric responses, threshold behaviour and stochastically switching regimes. The concept of cointegration has been generalized to accommodate many of these novel features.The papers gathered in this special Annals issue were presented at a conference in Cardiff, UK on July 9th-11th 2000, called to bring together researchers with common interests in these topics. Thirty six invited participants from six countries took part, and a total of sixteen papers were delivered, of which twelve have been submitted and accepted, after revision, for publication in this issue. Eight of them take nonstationarity or long memory as a theme, and eight are concerned with nonlinearity. In other words, no fewer than four succeed in combining both concerns. This was not an outcome planned or anticipated by the conference organizers, but illustrates the extent to which time series research represents a thoroughly 1

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Long memory in interest rate futures markets: A fractional cointegration analysis

Cited by 51 publications

References 41 publications

Price Transmission in the US Ethanol Market

Price Transmission in the US Ethanol Market

Long memory in commodity futures volatility: A wavelet perspective

Long memory and nonlinear time series

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