Journal article
Time-Series Processes of Utility Betas: Implications for Forecasting Systematic Risk
Financial management, v 19(3)
01 Oct 1990
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
A Kalman filtering model is used to estimate the time-series process followed by utility betas. The model is estimated using monthly stock return data from the COMPUSTAT PDE file for 109 utility firms - 61 electric and 48 electric and gas - for the period 1967-1981. The beta for the majority of utility companies in the sample follows either an autoregressive or a constant coefficient process. Very few appear to follow a random walk process, which would produce betas that were not only unstable, but very difficult to forecast. With an autoregressive process, a patient forecaster using relatively simple diagnostic procedures should be able to obtain a reasonable long-run estimate of systematic risk. A reasonable forecast of beta then admits application of the capital asset pricing model for utilities, even if beta is time-varying. The strong evidence of autoregressive tendencies in utility betas lends support to the use of Bayesian-type adjustment processes.
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Details
- Title
- Time-Series Processes of Utility Betas: Implications for Forecasting Systematic Risk
- Creators
- Michael GombolaDouglas Kahl
- Publication Details
- Financial management, v 19(3)
- Publisher
- Blackwell Publishing Ltd
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- [Retired Faculty]
- Web of Science ID
- WOS:A1990EQ18800013
- Other Identifier
- 991019183925904721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Business, Finance