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This paper "Transforming Stock Market Forecasts with Variable Expected Returns", SSRN https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4953845 presents a solution to the low-volatility anomaly. The paper which references Blitz starting on page 18, demonstrates that the classic expected return metric, which is the first raw moment of the distribution is inappropriate for processes like stock markets that are better modeled (simple models of course) as multiplicative processes. The first raw moment/"expected value" is known to be biased for positive-only fat-tailed distributions, and this bias increases with volatility. My paper proposes the expectation of the sampled geometric mean as a more appropriate metric for returns. When used, this metric resolves the low-volatility anomaly because only the drift term, exp(\mu t) ends up being important for forecasts with large number of periods. This expectation measure is not upward biased by the variance/2 term that is present in the current expected returns methodology. I propose that this approach be described in the explanations section. Vance Harwood Vance Harwood (talk) 01:39, 20 October 2024 (UTC)[reply]
