Abstract
tion in these studies is that the joint distribution of the rate of return on risky securities (or portfolios) and the market portfolio proxy is bivariate normal and stationary through time.2 That is, the true parameters of the conditional distribution of the (excess) rate of return on risky securities (or portfolios) given the (excess) rate of return on the market portfolio are the same for each observation in the sample. This assumption is a necessary condition for the econometric procedures employed in studies of market efficiency and tests of the two-parameter model, although it is not required by the underlying theory in either case. However, the theory does indicate, for example, that changes in capital structure and the adoption of new projects (or acquisitions) from a different risk class than present operations will change systematic risk. Exogenous economic information may also change the market's assessment of the parameters defining this conditional distribution. Parameter nonstationarity does represent a severe violation of econometric model specification resulting in the loss of known distributional properties of the parameter estimates and confidence in any inferences made conditional on these estimates. The purpose of this study is to provide statistical significance tests of the stationarity specification by providing a more general alternative nonstationarity specification that has, as a special case, the standard linear model. The importance of testing for systematic risk stationarity was first recognized by Blume (1971). In this paper we intend to consider several new problems by addressing the following questions: (1) Are the differences in the estimates of systematic risk in subperiods just due to sampling error or are they significantly different from each other? That is, can the null hypothesis that the true fB's are identical in subperiods be tested? Can we test the same null hypothesis for a's?
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