Saturday, June 13, 2015

beta definition in CAPM - confusion cleared

In CAPM, beta (of a stock like ibm) is defined in terms of
* cov(ibm excess return, mkt excess return), and
* variance of ibm excess return

I was under the impression that variance is the measured "dispersion" among the recent 60 monthly returns over 5 years (or another period). Such a calculation would yield a beta value that's heavily influenced or "skewed" by the last 5Y's performance. Another observation window is likely to give a very different beta value. This beta is based on such unstable input data, but we will treat it as a constant, and use it to predict the ratio of ibm's return over index return! Suppose we are lucky so last 12M gives beta=1.3, and last 5Y yields the same, and year 2000 also yields the same. We still could be unlucky in the next 12M and this beta
fails completely to predict that ratio... Wrong thought!

One of the roots of the confusion is the 2 views of variance, esp. with time-series data.

A) the "statistical variance", or sample variance. Basically 60 consecutive observations over 5 years. If these 60 numbers come from drastically different periods, then the sample variance won't represent the population.

B) the "probability variance" or "theoretical variance", or population variance, assuming the population has a fixed variance. This is abstract. Suppose ibm stock price is influenced mostly by temperature (or another factor not influenced by human behavior), so the inherent variance in the "system" is time-invariant. Note the distribution of daily return can be completely non-normal -- Could be binomial, uniform etc, but variance should be fixed, or at least stable -- i feel population variance can change with time, but should be fairly stable during the observation window -- Slow-changing.

My interpretation of beta definition is based on the an unstable, fast-changing variance. In contrast, CAPM theory is based on a fixed or slow-moving population variance -- the probability context. Basically the IID assumption. CAPM assumes we could estimate the population variance from history and this variance value will be valid in the foreseeable future.

In practice, practitioners (usually?) use historical sample to estimate population variance/cov. This is, basically statistical context A)

Imagine the inherent population variance changes as frequently as stock price itself. It would be futile to even estimate the population variance. In most time-series contexts, most models assume some stability in the population variance.

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