Abstract
In Chapter 7 I reported on a GARCH study of Yeager’s ruble data. At least two considerations limit the value of that study. First, the GARCH model assumes that the underlying error term is normally distributed. But, as Broussard and Koppl (1999) report, Yeager’s ruble returns exhibit kurtosis. The distribution of returns has fat tails; it is not a normal distribution. Second, the model assumes the data are continuous, not discrete. But the ruble data are discrete. The price of hundred-ruble notes moved in jumps of 0.05 marks. It is possible to overcome these two difficulties with non-parametric tools designed to handle discrete data. This chapter reports on a test for herding and contra-herding that uses such tools. It builds on Crack and Ledoit’s (1996) analysis of the “compass rose.”
This chapter is a modified version of Koppl and Nardone (2001).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Copyright information
© 2002 Roger Koppl
About this chapter
Cite this chapter
Koppl, R. (2002). The Angular Distribution of Asset Returns in Delay Space. In: Big Players and the Economic Theory of Expectations. Palgrave Macmillan, London. https://doi.org/10.1057/9780230629240_9
Download citation
DOI: https://doi.org/10.1057/9780230629240_9
Publisher Name: Palgrave Macmillan, London
Print ISBN: 978-1-349-39968-0
Online ISBN: 978-0-230-62924-0
eBook Packages: Palgrave Economics & Finance CollectionEconomics and Finance (R0)