Detection of Multiple Beta Shifts in Monthly Returns Data

Abstract

This abstract was created post-production by the JFI Editorial Board. This study has examined the power and type I error rate of four methods of testing for regression parameter changes when applied to detecting beta changes in monthly stock return series. The study used simulated stock return series with known betas, error variances, beta change dates, and error term distributions. In summary, it appears to be nearly impossible to detect or find the location of small or moderate beta changes in monthly stock return series. This suggests that the market model parameter changes reported by Hays and Upton (1986) are most likely not beta changes. However, they find of market model nonstationarity in almost all of the stocks in their sample-far more than this study finds. This suggests that if non-normality of stock returns accounts for the results obtained by Hays and Upton, the Stable Paretian 1.95 distribution does not adequately explain monthly stock returns.

Detection of Multiple Beta Shifts in Mutual Fund Returns Data

“…However, the procedure detects a third beta change in no more than 3.5 percent of the securities in any of the samples. The number of beta change indications is slightly greater than when using series of 180 monthly simulated stock returns (Howe and Pope 2006) but less than when using series of 600 daily simulated stock returns (Howe and Pope 2005) .…”

“…In theory, it is not clear whether one would expect the tests to be more powerful than in Howe and Pope (2006). On one hand, there are fewer observations (104 in this study versus 180 in Howe and Pope (2006)) and this study uses simulated weekly returns rather than simulated monthly returns.…”

“…In theory, it is not clear whether one would expect the tests to be more powerful than in Howe and Pope (2006). On one hand, there are fewer observations (104 in this study versus 180 in Howe and Pope (2006)) and this study uses simulated weekly returns rather than simulated monthly returns. On the other hand, mutual funds are portfolios rather than individual stocks, and thus have much higher market model R 2 s than individual stocks do.…”

“…One would expect the effect of the difference in R 2 to be much stronger than the effect of the difference in degrees of freedom. Therefore, in the samples with one or more beta changes, we would expect more ability to detect the beta changes than in Howe and Pope (2006).…”