Currently viewing the tag: "risk and return"

Ocean lIner sinking at severe angleWe 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 (βD). Different researchers have supplied different technical definitions for βD; here we will use the one provided by Javier Estrada:

βD = downside covariance between asset and market portfolio / downside variance of market portfolio.

There are several ways to calculate βD, 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.

We continue our journey into the wonderland of alpha, taking up with leverage and the Efficient Frontier.  We documented last time that a mix of a risky portfolio and the risk-free rate (Rf) yields a linear Capital Asset Line (CAL) within risk-return space. When the mix is varied to decrease the amount of Rf, the riskiness and expected return of the portfolio increase.  The mirror image occurs as we increase the relative percentage of Rf in the portfolio mix.  We can even borrow at the risk-free rate to purchase additional risky assets for our portfolio – one form of a practice known as leverage. Continue reading “Modern Portfolio Theory – Part Two” »

Our risk/return series continues with a review of Modern Portfolio Theory (MPT). We’ve already looked at alpha, beta, efficient markets, and returns.  Our ultimate goal is to evaluate the role of alpha in hedge fund profitability, how to replicate hedge fund results without needing alpha, and finally how you can start your own cutting-edge hedge fund using beta-only replication techniques.

MPT suggests that a portfolio can be optimized in terms of risk and return by carefully mixing individual investments that have widely differing betas. Recall that beta is return due to correlation with an overall market (known as systematic risk). To take a trivial example, if you hold equal long and short positions in the S&P 500 index, your portfolio would have an overall beta of (.5 * 1 + .5 * -1) = 0.  There would be no risk, but in this case, there would be no return either, except for the slow drain of commissions, fees, etc.

Continue reading “Modern Portfolio Theory – Part One” »

If you have been following our recent blogs, you are by now familiar with the concepts of alpha, beta, and the Efficient Market Hypothesis.  Our final goal is to evaluate the role of alpha in hedge fund investing, and to look at trading strategies that do not rely on alpha.  Before we can discuss these topics, we need to better understand financial asset pricing models, the role of alpha and beta within these models, and how the models apply specifically to hedge funds. In this installment, we’ll review the concept of rate of return (ROR).

Continue reading “Rate of Return” »

In our previous blog, we discussed the concept of beta as it applies to the risk and return of an investment. Recall that beta is the price movement in an individual investment that can be accounted for by the price movement of the general market.  If your investment has a beta of 1.0 and the market returns 10%, your investment should also return 10%.  If your investment returns over 10%, the excess return is called alpha. Alpha is derived from a in the formula Ri = a + bRm which measures the return on a security (Ri) for a given return on the market (Rm) where b is beta.

Continue reading “Risk and Return: Alpha” »

Hedge funds use an array of strategies to guide trading.  Most of these strategies seek to decouple returns from those of the overall market, as measured by a statistic called “beta” (β). Beta is calculated by dividing the covariance of an investment’s return by the variance of a portfolio or market return:

βi = Cov (ri, rm) / Var(rm) where i = an investment, m = market portfolio, and r = return

Continue reading “Beta” »