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Long agricultural futures price seri...

Wei, Anning.

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Long agricultural futures price series: ARCH, long memory, or chaos processes?

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Long agricultural futures price series: ARCH, long memory, or chaos processes?/
作者:	Wei, Anning.
面頁冊數:	151 p.
附註:	Source: Dissertation Abstracts International, Volume: 57-12, Section: A, page: 5242.
Contained By:	Dissertation Abstracts International57-12A.
標題:	Economics, Agricultural. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9717344
ISBN:	0591254816

Long agricultural futures price series: ARCH, long memory, or chaos processes?
Wei, Anning.

Long agricultural futures price series: ARCH, long memory, or chaos processes? - 151 p.

Source: Dissertation Abstracts International, Volume: 57-12, Section: A, page: 5242.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1997.

Price series that are twenty-one and half years long for six agricultural futures markets, i.e., corn, soybeans, wheat, hogs, coffee, and sugar, exhibit time-varying volatility, long-range dependence, excessive skewness and kurtosis, though they are covariance stationary. This suggests that the series contain nonlinear dynamics. ARCH, long memory, and chaos are the three nonlinear models that are able to produce these symptoms. This research determines which model is the underlying structure of the futures markets of concern.

ISBN: 0591254816Subjects--Topical Terms:

626648
Economics, Agricultural.

Long agricultural futures price series: ARCH, long memory, or chaos processes?
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245 1 0 $a Long agricultural futures price series: ARCH, long memory, or chaos processes?
300 $a 151 p.
500 $a Source: Dissertation Abstracts International, Volume: 57-12, Section: A, page: 5242.
500 $a Adviser: Raymond M. Leuthold.
502 $a Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1997.
520 $a Price series that are twenty-one and half years long for six agricultural futures markets, i.e., corn, soybeans, wheat, hogs, coffee, and sugar, exhibit time-varying volatility, long-range dependence, excessive skewness and kurtosis, though they are covariance stationary. This suggests that the series contain nonlinear dynamics. ARCH, long memory, and chaos are the three nonlinear models that are able to produce these symptoms. This research determines which model is the underlying structure of the futures markets of concern.
520 $a Though standard ARCH tests suggest that all series might contain ARCH effects, further diagnostics show that the series are not ARCH processes. Three long memory techniques, i.e., the classical R/S analysis, the modified R/S analysis, and the AFIMA model, are reviewed and applied to analyze all six futures markets. The classical R/S analysis suggests there might be long memory structures in the series. However, other two more robust tests, the modified R/S analysis and the AFIMA model, confirm only the case of sugar and reject this proposition for the other five markets.
520 $a The failure of ARCH and long memory processes allows chaos analysis to be applied directly to the raw data. With the carefully-constructed phase space, the correlation dimension (CD) and the largest Lyapunov exponent (LE) are estimated. For the corn, soybean, wheat, hog, and coffee series, the CD's lie in the range of 2-4, and the largest LE's are positive. These combined results infer that these five futures markets contain strange attractors that regulate the movements of the prices. They are chaotic processes.
520 $a For those markets where chaos has been found, the irregular market behavior, even "disasters", are likely to be the product of a few basic market forces through a deterministic process. For a chaotic structure, neither long-run nor short-run quantitative prediction is possible in practice. However, the fractal property of a chaotic structure may help traders make qualitative judgments about market trends, which may lead to trading opportunities.
520 $a This study has advanced the research methods and procedures of nonlinear dynamics modeling. Some basic properties of ARCH processes have been highlighted since they were not given enough attention in the past and lead to the misuse of the ARCH model. The study has introduced the long memory model, especially the AFIMA model, to agricultural market study for the first time. The study suggests that various linear and nonlinear filters should be used carefully in chaos study since it has been found that they can distort potential chaotic structures in the data. The typical chaos analysis must start with constructing the phase space, and the parameters of phase should be specified carefully.
590 $a School code: 0090.
650 4 $a Economics, Agricultural. $3 626648
650 4 $a Economics, Finance. $3 626650
690 $a 0503
690 $a 0508
710 2 0 $a University of Illinois at Urbana-Champaign. $3 626646
773 0 $t Dissertation Abstracts International $g 57-12A.
790 1 0 $a Leuthold, Raymond M., $e advisor
790 $a 0090
791 $a Ph.D.
792 $a 1997
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9717344