SmartETF

What Is Diversification Worth?

Good try but you have an archaic application to asset allocation. Asset allocation relies on four basic attributes: Risk, Return, Dependency (Correlation), and Data Management. These four attributes are managed in a 3 step process: the first step, called a ‘Univariate Model’, measures the risk & return of an asset, the second step, or ‘Bivariate Model’ measures the dependency between two securities, and the third step ranks the bivariate model in what is called a ‘Multivariate Model’ to create the efficient frontier. Your model contains four critical flaws:
First you use the most simplistic measure of dependency to diversify a portfolio with the use of correlation. Correlation assumes a fixed relationship between two securities over the sampled time period and is purely academic. Correlation increases dramatically during extreme events, in fact during any volatile market. If you want a lesson in correlation look to the merry band of MPT disciples at Long-Term Capital Management for a classic case study, or look at Merriweather’s current performance! As the adage goes ‘the only thing that goes up in a down market is correlation’. Trash the static correlation model and move to a dynamic correlation model like Copula Dependency.
Second, you measure risk using standard deviation (σ). Standard deviation, semi-variance and Value-at-Risk are all hyper-flawed because they all rely on normal distributions. Do you really think a 5σ event will only occur every 7000 years or an 87’ magnitude crash will only happy once in every three lifetimes of the universe? Enlighten yourself to the world of Stable Distributions using logarithmic, not arithmetic distributions. You will find 5σ events really occur every 3-4 years. I recommend you convert to Expected Shortfall as you new method of risk measurement.
Third, how can you forecast using any of the methods you suggest? Running a simulation model using Black-Litterman (an Arbitrage Pricing Theory model) or the other solutions are simply band-aids on the old MVO model; the only difference is you are trying to tilt the results to more of a bullish or bearish state. This doesn’t solve the problem it just makes it less damaging. Why not take a scientific physics approach and use a data management tool like GARCH (that won the Noble Prize in 2002) instead of relying on Markowitz and his methodology from 1959? You do know you have faster processors and electronic data exchanges and advanced math models; why not upgrade after 40 years?
Since this article is ostensibly an advertisement for Quantext, I feel its fair game to point out the inherent flaws in your model as well as other suggested models using old mathematical applications and theories. I’m happy to unconditionally prove the superiority of newer models and their specific attributes and will cite the works of Benoit Mandelbrot and Extreme Value Theory as a comparative solution. Set yourself free from averaging thinking!