SmartETF

Asset Allocation and ETFs: Pimco's El-Arian in 2008

Performance of this magnitude is a result of using mean variance and ill-fated risk metrics like VaR and Standard Deviation. I spoke at last years Schwab conference and warned of the dangers of using these older asset allocation methodologies and warned of the risk in the markets at that time (last Fall). Managing ETF's using newer theories like Extreme Value Theory would have resulted in losses of 13.2% YTD. Extreme Value Theory and its application to asset allocation, Dynamic Portfolio Optimization, would have prevented this level of loss because it uses recent Nobel winning concepts like GARCH instead of 50 year old ideas like MVO, and replaces normal distributions with stable distributions (log-based distributions with fat-tails that scale). Read Mandelbrots book 'The (Mis)behavior of Markets.

Tracking Mean Reversion After Bad Months

Our research concludes that you are on the right track but your focus is too linear. Mean reversion over the long-term is an academic boon for getting a Nobel Laureate designation but it does not translate into a workable application in the real world. For example, MVO demonstrates domestic equities have returned 10% over 80 years. Therefore, you should get a 10% return on average. In the real world, the domestic equity market is down over the past 1, 3, and 10 years; yes 10 years. Granted it worked in the 80’s & 90’s, but not the 60’s & 70’s, and definitely not this era. It’s like a broken clock that is right twice a day; it is devoid of market cycles.

Short-term MVO is very interesting and much more meaningful. The question is, and will always be, what time frame is best for analyzing the time series of data (aka, time parameter estimation). I think you are off track when you try to curve fit your data by selecting a particular number of months. Markets don’t move in a linear pattern like monthly. You will have much greater success by rebalancing when markets move by a defined level of volatility or price (or both). Take volatility as an example, last February the market hit an extreme level of volatility (and price drop); buying at the level would have been very profitable. It is these extreme moves (up & down) that create the fat-tails of distributions and are reflected in the extreme technical patterns like Relative Strength. A more scientific approach is to go with a Noble winning approach from 2002 (in effect tossing the MPT model from 1959) and incorporate Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) which examines the clustering of data; basically, a scientific approach to short-term mean-variance. The analogy is MVO works like the Farmer’s Almanac for predicting weather; whereas GARCH acts like the Doppler Radar. Alternatively, you can use price and volatility movement to create a poor man’s GARCH model to track short term mean-variance. Cheers -