Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-15T08:00:18.219Z Has data issue: false hasContentIssue false

The Application of Spectral Analysis to Demonstrate the Stochastic Distortion in the Delta Midrange of Price Series

Published online by Cambridge University Press:  19 October 2009

Extract

Statistical analyses of price series generated by auction markets has been oriented historically toward the detection of structure (or lack of structure). While the results of such studies have been mixed, insufficient empirical evidence has been obtained on the properties of the measures employed to capture the inherent behavior of the price series. The first differences of the closing price have been shown, theoretically, to be unbiased for a random walk process. The use of averages, particularly the first differences of the midrange, has been shown to introduce spurious serial dependence in mathematical time series. It is the purpose of this article to examine the properties of both the delta close and delta midrange as measures, and to perform a variety of statistical tests and analyses to establish empirically the relationship between them. The predominant effort in seeking this relationship consisted of the spectral analysis of 11 years of May potato futures prices. The results obtained support the contention that the delta midrange amplifies the delta close, and that the amount of amplification is stochastic distortion in the delta midrange.

Type
Research Article
Copyright
Copyright © School of Business Administration, University of Washington 1975

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

[1]Bartletf, M. S.An Introduction to Stochastic Processes with Special Reference to Methods and Applications.” Cambridge: Cambridge University Press, 1953.Google Scholar
[2]Brownlee, K. A.Statistical Theory and Methodology in Service and Engineering.” New York: Wiley, 1960.Google Scholar
[3]Cootner, Paul H.The Random Character of Stock Market Prices. Cambridge, Mass.: MIT Press, 1964.Google Scholar
[4]Daniels, H. E.Autocorrelation between First Differences of Mid–Ranges.” Econometrica, vol. 34, (January 1966), pp. 215219.CrossRefGoogle Scholar
[5]Houtkaher, Hendrik. “Systematic and Random Elements in Short–term Price Movements.” American Economic Review, vol. 51 (1961), pp. 164172.Google Scholar
[6]Jenkins, G. M., and Watts, D. G.. Spectral Analysis and Its Applications. San Francisco: Holden–Day, 1968.Google Scholar
[7]Kendall, M. G., and Stuart, A.. The Advanced Theory of Statistics, vol. 3. London: Charles Griffin & Company, 1966.Google Scholar
[8]Labys, Walter C., and Granger, C. W. J.. Speculation, Hedging and Commodity Price Forecasts. Lexington, Mass.: Heath Lexington Books, D. C. Heath, 1970.Google Scholar
[9]Larson, A. B.Measurement of a Random Process in Futures Prices.” Food Research Institute Studies, vol. 1 (November 1960), pp. 313324.Google Scholar
[10]Rosenberg, Barr. “Statistical Analysis of Price Series Obscured by Averaging Measures.” Journal of Financial and Quantitative Analysis, September 1971, pp. 10831094.CrossRefGoogle Scholar
[11]Smidt, Seymour. “A New Look at the Random Walk Hypothesis.” Journal of Financial and Quantitative Analysis, September 1968, pp. 235259.CrossRefGoogle Scholar
[12]Working, Holbrook. “Price Effects on Futures Trading.” Food Research Institute Studies, vol. 1, no. 1 (February 1960).Google Scholar