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Market Dependence and Economic Events
Author(s) -
Nawrocki David N.
Publication year - 1996
Publication title -
financial review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.621
H-Index - 47
eISSN - 1540-6288
pISSN - 0732-8516
DOI - 10.1111/j.1540-6288.1996.tb00874.x
Subject(s) - autocovariance , autocorrelation , econometrics , term (time) , economics , random walk , random walk hypothesis , stock market , mean reversion , statistical physics , efficient market hypothesis , mathematics , statistics , physics , mathematical analysis , paleontology , horse , fourier transform , quantum mechanics , biology
Recent studies on stock market pricing have rejected the random walk model for short‐term periods and have concentrated on long‐term persistent or mean‐reverting dependence. The problem with these studies is that their statistical results can be biased by the shorter term dependence. Rather than trying to develop a unified theory that explains both short‐ and long‐term dependence, current studies use different methodologies to correct for the short‐term dependence while trying to test for long‐term dependence. This paper uses a sequential information theory to focus attention on short‐term dependence effects. This theory states that the market process is a nonstationary mean process surrounded by a nonstationary autocovariance error process. A nonstationary mean process implies short‐term dependence resulting from changing economic events (new information). Long‐term persistent dependence then derives from nonperiodic economic cycles. A new empirical approach, a cross‐sectional autocorrelation coefficient is used since it is free from the stationarity problems of previous techniques.