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.

Checking In on the All-ETF Portfolio

You used the term correlation; not I. Correlation refers to a form of dependency model, better known as linear correlation. If this is not the forum to express sophisticated methodologies, then why is it the forum to showcase simplistic ones? The theme was to highlight the de-coupling of indexing and correlation. That is what I was addressing.
I have read your past posts and we obviously differ in several areas; but only because I’ve been in the business 25 years, have built models since the mid 80’s that have put me in the top 1% for two decades, and mostly read whitepapers, not trade magazines. In the future I would welcome a debate on your previous posts. In previous articles you cited LTCM as the exception, not the rule, I disagree. You note the underperformance of active managers, yet who determines who is active or who is a closet indexer? Two Yale professors recently came out with a new model that determines if a manager is truly active (based on 23 years of research). Truly active managers soundly beat the index after fees and expenses! BTW, I do not consider active managers to be the same thing as market timers. Taleb may be egotistical but he is right none-the-less.
I will love to resume this conversation in 10 years after you’ve experienced what is to come. My word of caution is to avoid absolutes, avoid getting sucked into the academic theories, and be careful who you choose to debate. I’ve chosen to fight the popular opinion my entire career; it’s a tough road. My best word of advice is for you to never stop asking ‘why’. As Kennedy once said “How could I have been so wrong as to trust the experts?” Don’t let a little criticism stunt your progress. I applaud your efforts and cherish your enthusiasm.

Checking In on the All-ETF Portfolio

Geoff, I meant no offense, my goal is to help investors universally.

First, Mandelbrot wrote his book four years ago and we had already built the same concepts several years before. So the fact he hadn't seen the models work doesn't mean they don't exist.

Second, I like your idea’s but I’m highlighting the weaknesses in your models and offering improvements to help; not to be critical. You choose to indirectly advertising QPP so I choose to freely suggest how models can be improved.

So the question is:
a) Do you believe normal distributions are better than stable distributions? I welcome this discussion.
b) Do you believe linear correlation is better than copula dependency? I welcome this discussion.
c) Do you believe a concept from 1952 called mean-variance can possibly be better than another Nobel winning formula created this decade? I welcome this discussion as well.

I’m not advertising our firm but will share our asset allocation mix as of July 22, 2008, the date of our last rebalancing.

2.00% Vanguard Health Care VIPERs VHT
2.00% Vanguard Industrials VIPERs VIS
5.00% Short QQQ ProShares PSQ
3.00% iShares MSCI Brazil (Free) Index EWZ
2.00% iShares FTSE/Xinhua China 25 Index FXI
3.00% iShares S&P Latin America 40 Index ILF
2.00% BLDRS Emerging Markets 50 ADR Index ADRE
3.00% PowerShares DB Agriculture DBA
2.00% iShares Dow Jones US Real Estate IYR
3.00% iShares GS $ InvestTop Corp Bond LQD
71.50% iShares Lehman 1-3 Year Treasury Bond SHY
1.50% Money Market Fund MMA


Because asset allocation models all follow a 3 step process I will describe the advantages in each step:

1) Step 1 (univariate model): a Stable ‘t’ distributions with GARCH features better determines the current risk & forecasted return of a security as opposed to a rear-view mirror approach based on normal distributions.
2) Step 2 (bivariate model): a copula dependency more accurately examines the current relationship between two securities as opposed to linear correlation based upon long-term averages.
3) Step 3 (multivariate model): ranking the bivariate models with Monte Carlo modeling to forecast produces an optimal mix based on current market conditions as opposed to Monte-Carlo modeling based upon long-term averages.

So you can see from our current allocation the risk/return trade-off for securities is unattractive in the current market conditions and the recent volatility is keeping investment out of securities which have traditionally low or negative correlation.
So you see I have only upgraded the basic attributes of asset allocation and continue to follow the same 3-step process as you and the other solution providers.

Again, I wasn’t trying to change the conversation over to EVT but was trying to address the blindness in risk modeling and offering a solution to the problem.

All the best -

Checking In on the All-ETF Portfolio

One contributor wrote: “it is impossible to ‘predict’ fat-tail events”. Yes, using traditional asset allocation models. I have used Extreme Value Theory for four years and have avoided all the major sell-offs, including 9-11, and the fun year of 2008. Our growth models range from +2% YTD to minus 4.5% YTD depending on the fund universe. Check out the latest books by Benoit Mandelbrot or if you prefer heavy reading, any of the recent books on EVT.

Checking In on the All-ETF Portfolio

Finally an intelligent string of correspondence!

Yes, the MPT, APT and follow-up portfolio theories rely on normal (arithmetic) distributions, henceforth ignoring fat-tail events. Converting to a stable (log) distribution exposes the tails to more accurately define the actual risk.

These traditional models rely on mean-variance which is why they predict 10% annualized returns (based on the past 80 years. It is ~ 7% over the past 180 years). Using mean-variance, your clients should have doubled their money since 1998; but in fact the S&P 500 is still down after 10 years. Making a bet on the law of large numbers is great if you have 50 years for a pay-off; I don’t. Markets move through economic cycles that reward equities positively on average 18.5 years (bullish) and penalize equities negatively on an average 17 years (bearish). It can take up to 20 to 30 years for an investor to break-even if invested at the market highs.

An upgrade can be made to mean-variance with a newer Noble prize winning formula (circa 2003) called GARCH (Generalized Auto-Regressive Heteroskedacity) that tracks the clustering of data.

The third problem with traditional models is that they assume markets are static as well as correlation. We all know that correlations increase as markets become more volatile. Why not have real time correlation models? You can upgrade linear correlation to a dynamic correlation by switching to a copula dependency model.

Upgrade your 1) risk analysis using stable distributions, 2) time-series using GARCH, and 3) correlation dependency using copula dependency. These upgrades significantly add to performance and are the underlying concepts of Extreme Value Theory.

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 -

Defining ETF Risk: Does It Pass the "Smell" Test?

Matt Hougan wrote about this very thing on June 8th and lists the exact same information plus 'tax risk' and 'counterparty risk' (in the case of commodity and leverage funds). I think you are missing the two most important risks: 'Expected Shortfall risk' and 'volatility risk'.
Expected Shortfall is the extra (fat-tail) loss that is ignored using a normal distribution. By converting to a 'Stable' (logarithmic) distribution you can actually see the true risk of a frequency distribution. In other words, it is a Value-at-Risk (VaR) model that better describes the tails of a distribution. With VaR, with may think you stand to lose 3% of the portfolio value on a given day, one percent of the time (at a 99% VaR). With conditional expected shortfall (or conditional VaR) the actual loss 1% of the time may actually be 6%; like what happened this past February.
Volatility Risk is the extra risk you assume by investing in less diversified asset classes. This is a big deal with ETFs. The cause of this problem stems from the sudden interest in ETFs and the need for ETF manufacturers to gobble up their stake in the ETF real-estate game. As the land-grab for ETF shelf space continues so does the increase in volatility. The first ETFs were broad-based market indices, like the S&P 500. The next wave of ETFs was the industry sectors (health care, financials, basic materials, etc.). Because they are less diversified the risk on one industry, in terms of volatility (measured in standard deviation) is 1.3 to 8.6 times the volatility of the S&P 500. Having seized the industry sector space the ETF manufacturers went to the sub-sector frontier to build their niche (such as bio-tech); and henceforth more risk. Not to be out done, competing manufactures launched inverse funds and leveraged funds; again, more risk. Only since June of last year has the risk in new ETF’s subsided with the introduction of fixed income, real estate and some commodity ETF’s. The largest risk in managing a portfolio of ETF’s is in choosing the proper fund universe; then comes the ardent task of fundamental research and asset allocation.

Expected Shortfall is the extra (fat-tail) loss that is ignored using a normal distribution. By converting to a 'Stable' (logrithmic) distribution you can actually see the ture risk of a frequency distribution. In other words, it is a Value-at-Risk (VaR) model that better describes the tails of a distribution. With VaR, with may think you stand to lose 3% of the portfolio value on a given day, one percent of the time (at a 99% VaR). With conditional expected shortfall (or conditional VaR) the actual loss 1% of the time may actually be 6%; like what happened this past February.

Volatility Risk is the extra risk you assume by investing in less diversified asset classes. This is a big deal with ETFs. The cause of this problem stems from the sudden interest in ETFs and the need for ETF manufacturers to gobble up their stake in the ETF real-estate game.

ETF Investment Risks

An overlooked metric is volatility risk. As the land-grab for ETF shelf space continues so does the increase in volatility. The first ETFs were broad-based market indices, like the S&P 500. The next wave of ETFs was the industry sectors (health care, financials, basic materials, etc.). Because they are less diversified the risk on one industry, in terms of volatility (measured in standard deviation) is 1.3 to 8.6 times the volatility of the S&P 500. Having seized the industry sector space the ETF manufacturers went to the sub-sector frontier to build their niche (such as bio-tech); and henceforth more risk. Not to be out done, competing manufactures launched inverse funds and leveraged funds; again, more risk. Only since June of last year has the risk in new ETF’s subsided with the introduction of fixed income, real estate and some commodity ETF’s. The largest risk in managing a portfolio of ETF’s is in choosing the proper fund universe; then comes the ardent task of fundamental research and asset allocation.

Asset Allocation as a Method for Risk Management

This is an excellent illustration of the merits of diversification. Thanks for sharing -

Relative Returns By Equity Asset Class

Thank you for sharing; always nice to see performance from a multiple time-frame perspective!