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Activity Number: 662
Type: Contributed
Date/Time: Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
Sponsor: Business and Economic Statistics Section
Abstract - #304199
Title: A New Test For Randomness with Application to Stock Market Index Data
Author(s): Boris Iglewicz*+ and Alicia Graziosi Strandberg
Companies: Temple University and Temple University
Address: Dept. of Statistics 00600, Philadelphia, PA, 19122-6012, United States
Keywords: Nonparametric ; Randomness ; Stock Market ; Time Series ; Variance Ratio

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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