Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/178540
Authors: 
Allen, David E.
McAleer, Michael
Powell, Robert
Singh, Abhay K.
Year of Publication: 
2013
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 6 [Year:] 2013 [Issue:] 1 [Pages:] 6-30
Abstract: 
This paper features an analysis of the relationship between the S&P 500 Index and the VIX using daily data obtained from the CBOE website and SIRCA (The Securities Industry Research Centre of the Asia Pacific). We explore the relationship between the S&P 500 daily return series and a similar series for the VIX in terms of a long sample drawn from the CBOE from 1990 to mid 2011 and a set of returns from SIRCA's TRTH datasets from March 2005 to-date. This shorter sample, which captures the behavior of the new VIX, introduced in 2003, is divided into four sub-samples which permit the exploration of the impact of the Global Financial Crisis. We apply a series of non-parametric based tests utilizing entropy based metrics. These suggest that the PDFs and CDFs of these two return distributions change shape in various subsample periods. The entropy and MI statistics suggest that the degree of uncertainty attached to these distributions changes through time and using the S&P 500 return as the dependent variable, that the amount of information obtained from the VIX changes with time and reaches a relative maximum in the most recent period from 2011 to 2012. The entropy based non-parametric tests of the equivalence of the two distributions and their symmetry all strongly reject their respective nulls. The results suggest that parametric techniques do not adequately capture the complexities displayed in the behavior of these series. This has practical implications for hedging utilizing derivatives written on the VIX.
Subjects: 
S&P 500
VIX
entropy
non-parametric estimation
quantile regressions
JEL: 
G15
G17
G32
C58
D53
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by/3.0/
Document Type: 
Article
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