We have devoted a lot of blog space in the past examining the pros and cons of the **Capital Asset Pricing Model (CAPM)**. The model predicts the amount of **excess return** (return above the risk-free rate) of an arbitrary portfolio that can be ascribed to a relationship (called **beta ^{[1]}**) to the excess returns on the underlying market portfolio. It assumes that investors seek maximum utility from their portfolios, and that investors expect to be compensated for taking on additional risk. In CAPM, no portfolio can outperform a mix of the risk-free asset and the market portfolio on a risk-adjusted basis.

Risk and return are the only two variables of importance in CAPM. Risk is measured by beta, which is rooted in the variance (or more properly, its square root, standard deviation) of returns, and is an indication of volatility.

Recall that CAPM makes a number of simplifying (and criticized) assumptions to work. A couple of the assumptions that have been the subject to criticism are:

1) Portfolio returns are distributed symmetrically around a mean.

2) Portfolio returns are assumed to have no outliers (or “fat tails”).

Empirical evidence suggests otherwise. Researchers have thus sought alternatives to a variance-based beta that relaxes both of these assumptions, and one popular candidate is called **semivariance** – a measure of the dispersion of those values in a distribution that fall *below the mean* or target value of a data set. In short, semivariance-based modifications to CAPM concentrate on **downside-risk** only. Semivariance is a better statistic when dealing with asymmetric distributions, as it automatically incorporates the notion of **skewness**. Ignoring skewness, by assuming that variables are symmetrically distributed when they are not, will cause any model to *understate* the risk of variables with high skewness

One popular semivariant CAPM alternative is called **D-CAPM** (Downside-CAPM). Regular old beta is replaced by downside-beta (**β**** _{D}**). Different researchers have supplied different technical definitions for β

_{D}; here we will use the one provided by Javier Estrada:

β_{D } = downside covariance between asset and market portfolio / downside variance of market portfolio.

There are several ways to calculate β_{D}, but I will spare you the details. The important point is that empirical studies^{[2]} indicate D-CAPM gives better predictions compared to CAPM, especially for emerging markets. This may be due to the hypothesis that returns from emerging markets are less normal and more skewed than returns from developed markets.

D-CAPM is generally well-regarded because of its plausibility, supporting evidence, and the widespread use of D-CAPM. For instance, a study by Mamoghli and Daboussi^{[3]} concluded “*It comes out from these results that the D-CAPM makes it possible to overcome the drawbacks of the traditional CAPM concerning the asymmetrical nature of returns and the risk perception…*”

However, D-CAPM is not without its **critics**. Let me quote from one^{[4]}, in which downside correlation is taken to task: “*This measure ignores the ability of upside returns of one asset to hedge the downside returns of another asset in a portfolio**. **Within the scope of D-CAPM the standard equation to calculate portfolio’s downside semideviation cannot be correct, since the Estrada’s downside correlation equates upside returns to zeros and does not represent the true downside correlations. Specifically, the downside correlation cannot be measured, because the portfolio’s semideviation depends on the weights of assets, their standard deviations and correlation between them, rather than on the semivariance. This formula is specified for normally distributed and symmetrical returns and there is no formula invented to calculate the portfolio’s semideviation yet*”.

This criticism, if true, is especially relevant to hedge fund traders, who use long-short strategies as a matter of course.

Nevertheless, Professor Estrada responded^{[5]} as follows: *“**First, it’s true that the downside beta I suggest in my paper does not account for something that would be legitimately called risk, which is the market going up and the asset going down. The way I suggest to calculate downside beta only accounts for down-down states. But there is a reason for this: It’s the only way to achieve a symmetric semivariance-semicovariance matrix, necessary for a solution of the problem. For example, the only way to apply standard techniques to solve an optimization problem with downside risk is to have a symmetric matrix. Second, and importantly, my several papers on downside beta, D-CAPM, and downside risk in general have been published in several journals and therefore peer-reviewed. I believe Mr. Cheremushkin’s comment is an unpublished piece.”*

This controversy will no doubt be resolved over the fullness of time. We welcome ** your** views.

[1] Technically, beta is equal to the **covariance** of an asset with the market portfolio divided by the **variance** of the market portfolio.

[2] Estrada, Javier (2003). “*Mean-Semivariance Behavior (II): The D-CAPM*.” University of Navarra.

[3] Mamoghli, Chokri and Daboussi, Sami (2008). Valuation of Hedge Funds Portfolios in a Downside Risk Framework.

[4] Cheremushkin, Sergei V. (2009). “*Why D-CAPM is a big mistake? The incorrectness of the cosemivariance statistics*.” Mordovian State University. Also private communication 9-Aug-10.

[5] Private communication 9-Aug-10.

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