Searching for fractal structure in agricultural futures markets

Corazza, Marco; Malliaris, A. G.; Nardelli, C.

doi:10.1002/(sici)1096-9934(199706)17:4<433::aid-fut4>3.0.co;2-i

Cited by 74 publications

(37 citation statements)

References 55 publications

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“…The tools of fractal analysis are employed by Liu andHsueh (1993), Fang, Lai andLai (1994), Evertsz (1995aEvertsz ( , 1995b, Evertsz and Berkner (1995), Van de Gucht, Dekimpe and Kwok (1996), Corazza, Malliaris and Nardelli (1997) and in this article. A detailed presentation of these techniques is given in Shubik (1997).…”

Section: Review Of the Literaturementioning

confidence: 99%

“…Moreover, this stochastic process is statistically self-similar, that is {B H (t), t 0} and {a -H B H (at), t 0}, with a > 0, have the same distribution law. Further details for the fBm can be found in Falconer (1990), Evertsz (1995aEvertsz ( , 1995b, Evertsz and Berkner (1995) and Corazza, Malliaris and Nardelli (1997).…”

Section: A Fractional and Multifractional Brownian Motionmentioning

confidence: 99%

“…There is extensive literature on fractality from a mathematical point of view, such as Mandelbrot and Van Ness (1968) and Falconer (1990). Applications of fractality in finance are presented in Evertsz (1995aEvertsz ( , 1995b, Evertsz and Berkner (1995), and Corazza, Malliaris and Nardelli (1997).…”

Section: Introductionmentioning

confidence: 99%

“…Representative references include Lo (1991), Peters (1991Peters ( , 1994, Corazza (1996), Evertsz (1995aEvertsz ( , 1995b, Evertsz and Berkner (1995), Belkacem, Levy Vehel, and Walter (1996), Ostasiewicz (1996), Campbell, Lo and MacKinlay (1997) and Corazza, Malliaris and Nardelli (1997). corresponding underlying stochastic process is characterized by a quantity, called the Hurst exponent H, which is related to some fractal aspects of the process itself. 1 In particular, for a standard Brownian motion (sBm) the Hurst exponent is H = 0.5.…”

Section: Introductionmentioning

confidence: 99%

“…This inadequacy is often caused by the existence of many outliers, nonstationarity in the variance level, presence of asymmetry, and short and long-term dependence. Authors such as Lo and MacKinlay (1988), Lo (1991), Peters (1991Peters ( , 1994, Evertsz (1995aEvertsz ( , 1995b, Evertsz and Berkner (1995), Corazza (1996), Campbell, Lo and MacKinlay (1997) and Corazza, Malliaris and Nardelli (1997) provide statistical evidence that asset prices do not follow random walks.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Fractality in Foreign Currency Markets

Corazza¹,

Malliaris²

2002

MFJ

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Several empirical studies have shown the inadequacy of the standard Brownian motion (sBm) as a model of asset returns. To correct for this evidence some authors have conjectured that asset returns may be independently and identically Pareto-Lévy stable (PLs) distributed, whereas others have asserted that asset returns may be identically -but not independently -fractional Brownian motion (fBm) distributed with Hurst exponents, in both cases, that differ from 0.5. In this article we empirically explore such non-standard assumptions for both spot and (nearby) futures returns for five foreign currencies: the British Pound, the Canadian Dollar, the German Mark, the Swiss Franc, and the Japanese Yen.

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Section: Review Of the Literaturementioning

confidence: 99%

Section: A Fractional and Multifractional Brownian Motionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Fractality in Foreign Currency Markets

Corazza¹,

Malliaris²

2002

MFJ

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Fractional cointegration and futures hedging

Lien

Tse

1999

J. Fut. Mark.

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This article examines the performance of various hedge ratios estimated from different econometric models: The FIEC model is introduced as a new model for estimating the hedge ratio. Utilized in this study are NSA futures data, along with the ARFIMA‐GARCH approach, the EC model, and the VAR model. Our analysis identifies the prevalence of a fractional cointegration relationship. The effects of incorporating such a relationship into futures hedging are investigated, as is the relative performance of various models with respect to different hedge horizons. Findings include: (i) Incorporation of conditional heteroskedasticity improves hedging performance; (ii) the hedge ratio of the EC model is consistently larger than that of the FIEC model, with the EC providing better post‐sample hedging performance in the return–risk context; (iii) the EC hedging strategy (for longer hedge horizons of ten days or more) incorporating conditional heteroskedasticty is the dominant strategy; (iv) incorporating the fractional cointegration relationship does not improve the hedging performance over the EC model; (v) the conventional regression method provides the worst hedging outcomes for hedge horizons of five days or more. Whether these results (based on the NSA index) can be generalized to other cases is proposed as a topic for further research. © 1999 John Wiley & Sons, Inc. Jrl Fut Mark 19: 457–474, 1999

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Research of Scaling Law on Stock Market Variations

Walter¹

2009

Scaling, Fractals and Wavelets

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Searching for fractal structure in agricultural futures markets

Cited by 74 publications

References 55 publications

Multi-Fractality in Foreign Currency Markets

Multi-Fractality in Foreign Currency Markets

Fractional cointegration and futures hedging

Research of Scaling Law on Stock Market Variations

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