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Abstract Details
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Activity Number:
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662
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Type:
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Contributed
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Date/Time:
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Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Business and Economic Statistics Section
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| Abstract - #304199 |
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Title:
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A New Test For Randomness with Application to Stock Market Index Data
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Author(s):
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Boris Iglewicz*+ and Alicia Graziosi Strandberg
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Companies:
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Temple University and Temple University
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Address:
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Dept. of Statistics 00600, Philadelphia, PA, 19122-6012, United States
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Keywords:
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Nonparametric ;
Randomness ;
Stock Market ;
Time Series ;
Variance Ratio
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Abstract:
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Strandberg and Iglewicz (2012) propose a test that detects deviations from randomness, without a priori distributional assumptions. This nonparametric test is designed to detect deviations of neighboring observations from randomness, especially when the data set consists of time series observations. This test is especially effective for larger data sets. In our simulation study, this test is compared to a number of variance ratio and traditional statistical tests. The proposed test is shown to be a competitive alternative for a diverse choice of distributions and data models. In addition, this test is able to successfully detect changes in variance, which can be informative in short term investing and option trading. In our empirical application, we review and compare several transformations while evaluating the common US stock market indices. We consider two commonly used transformation
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Authors who are presenting talks have a * after their name.
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