Introducing an asymmetric beta as part of the CAPM can allow an investor to build a portfolio with expectations far above the stock market line.
The incorporation of an asymmetric beta provides evidence of a valuation error in certain profitability profiles, namely tail-hedged stocks, which can be analyzed using variants of the CAPM-type framework. CAPM-based asset allocations are poorly specified and ill-equipped to handle asymmetric returns.
The fixed asset valuation model is a fundamental element with which investors make allocation decisions over time. Investment decisions are made on the basis of risk-return concepts, and within this framework, CAPM, for the most part, has stood the test of time. Due to its simplicity, it is widely used when an equity investor wants to roughly estimate the expected returns of their portfolio.
We want to assess the value of tail hedging in a CAPM framework, and thus show the effectiveness of tail hedging and the poor specificity of the model itself.
Using Harry Markowitz’s efficient frontier, one can roughly compare different asset classes based on their consensus expected returns and observed risk (mostly calculated using the standard deviation of asset returns). However, this measure of risk is quite naÃ¯ve as it has been well documented that most, if not all, asset classes have non-normal and often skewed return distributions. Asymmetric properties are not captured well in a mean-variance framework because they underestimate tail risk in negatively asymmetric portfolios. Stress tests should therefore be used, as they are critical risk estimation tools that transparently demonstrate vulnerabilities to large spreads that can impact expected returns in the long term. (We recognize the success of empirical research indicating that multifactor models can explain and predict returns on investment, but they have similar limitations.)
Due to the principal-agent problem in the asset management industry, most fund managers rationally have a propensity to use a negatively skewed distribution of earnings. This type of behavior, on the whole, is also evidenced in historical data, which shows large losses for professional investors during the biggest market downturns. Most investors and asset distributors, in addition to these negatively skewed positions, additionally view the returns from hedging strategies in a vacuum, rather than as a holistic part of their larger portfolio. Thus, they are likely to view portfolio hedging programs as a drag on their performance figures and to undervalue them further. We believe that these and other factors contribute to market segmentation which creates an undervaluation of tail risk hedges.
Assuming that there are such opportunities in tail risk hedging, let’s assess how one can describe the risk-return profile of an asset class and see if using a fair tail risk hedging program could. help investors better manage these not-so-rare market crashes. We use Dr. Markowitz’s efficient frontier-type framework to plot a “measure of risk” on the x-axis (which is the average semi-variance for the three-year rolling monthly returns) and the annualized returns of the corresponding asset on the y-axis.