Testing and Locating Variance Changepoints with Application to Stock Prices

Jie Chen · Arjun Kumar Gupta

338 citations0/0 files storedJournal of the American Statistical Association

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Financial Risk and Volatility Modeling Statistical Methods and Inference Advanced Statistical Process Monitoring

Abstract

This article explores testing and locating multiple variance changepoints in a sequence of independent Gaussian random variables (assuming known and common mean). This type of problem is very common in applied economics and finance. A binary procedure combined with the Schwarz information criterion (SIC) is used to search all of the possible variance changepoints existing in the sequence. The simulated power of the proposed procedure is compared to that of the CUSUM procedure used by Inclán and Tiao to cope with variance changepoints. The SIC and unbiased SIC for this problem are derived. To obtain the percentage points of the SIC criterion, the asymptotic null distribution of a function of the SIC is obtained, and then the approximate percentage points of the SIC are tabulated. Finally, the results are applied to the weekly stock prices. The unknown but common mean case is also outlined at the end.

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References

1995Journal of the American Statistical Association 1994Use of Cumulative Sums of Squares for Retrospective Detection of Changes of Variance 1964Estimating the Current Mean of a Normal Distribution which is Subjected to Changes in Time 1975On Tests for Detecting Change in Mean 1969On Detecting Changes in the Mean of Normal Variates 1979On the Likelihood Ratio Test for a Shift in Location of Normal Populations

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