ETF Covered Call Income Model
The objective of the ETF covered call portfolio is to provide total return and consistent income from a highly diversified ETF portfolio with relatively low portfolio volatility. Call options are written monthly to provide cash flow. For clients who prefer lower turnover in their portfolio, the frequency of the call option selling can be adjusted downward. Call options can be written on a quarterly, semi-annually of even an annual basis for investors who like lower turnover ratios than the monthly strategy produces.
The portfolio will hold ETFs on the world’s major stock market indexes, US treasury bonds, and will add currencies and commodities if they add diversification to the portfolio. Currencies and commodities may be added based on a proprietary model that includes fundamental research, relative strength analysis, correlation analysis and a screen for option liquidity.
The ETF universe is monitored to locate funds that have sufficient option liquidity and provide diversification to the model. Correlation analysis is used to determine the optimal fund mix. Portfolio volatility is reduced by holding assets with low correlation and by selling call options. Company specific risk, also known as non-systematic risk is completely eliminated by the use of index based ETFs.The typical composition of the portfolio will be a mix of equity, debt, currency and commodity funds.
Once the fund allocation has been determined, call options are written monthly. To determine which options to write we use an option pricing model and evaluate the theta, delta, gamma, vega and implied volatility of the available strike prices and choose options that will contribute the most to the portfolio in terms of total return. A $200,000 portfolio will typically have a theta of about $100 per day, which means that if the underlying funds were to stay flat and produce no return, the investor would receive $100 per day just from option decay.
In addition to more traditional option pricing models like Black-Scholes, we also use a GARCH (generalized autoregressive conditional heteroskedasticity) model to study the conditional variance of the returns of the ETF’s. One of the consequences of dependency in asset returns is that the volatility of these returns tends to exhibit patterns. Volatility is not random but has a tendency to appear in clusters and these clusters have a tendency to be persistent over time. While periods of high volatility tend to be a concern for most investors, we look forward to the higher option premiums and enhanced income produced during those times. We have found that the Black-Scholes Model will tend to under price options during periods of high volatility and overprice them during periods of low volatility. Volatility forecasting helps us to determine which call options to sell.
The screening process consists of monitoring the ETF universe for new issues and to see which issues become optionable. If they are optionable, they must have sufficient option liquidity to sell large numbers of contracts and have tight bid-ask spreads. Funds may be added if they’ll provide diversification to a portfolio consisting of the world’s major stock market indices. If the funds meet those criteria they are then analyzed from a fundamental and technical perspective to determine if they are suitable candidates for purchase.
The theoretical basis for the model comes from several different major academic studies. While most investors have heard of the random walk theory and the efficient markets hypothesis, known as EMH, the question of how to apply those theories to individual portfolio management can present some problems. Randomwalkers believe that they should stay fully invested in low cost index funds for the long haul. The EMH theory states that markets are efficient, that all information is already built in to the current price and that movement cannot be predicted. Recent years have given us great advancement in the field of behavioral finance, which tells us that investors are not rational all the time. The AMH or adaptive markets hypothesis attempts to reconcile the differences between EMH and behavioral finance. While academics continue to research and debate, investors have a need to produce income, get acceptable returns and have comfortable risk levels. We follow the most recent developments in the academic world to determine if newly available research can help us enhance portfolio return and reduce risk or
The covered call income model benefits from combining elements of different, widely accepted studies. The portfolio holds low cost equity index funds as the EMH and random walkers suggest. We produce income from those funds by selling call options.
Modern portfolio theory or MPT was developed by Harry Markowitz, who won a Nobel Prizein economics for his work. The theory says that by combining assets classes with low correlation an investor can get better returns and lower risk. The covered call model follows MPT by being broadly diversified across major asset classes.
Dr. John Lintner of Harvard University was one of the original developers of the Capital Asset Pricing Model or CAPM. In 1983 he wrote a paper showing that using commodity futures in a traditional stock and bond portfolio enhanced return and lower risk. The covered call model will add low cost commodity ETFs to lower risk by providing greater diversification than can be found in a traditional stock and bond portfolio.
There have been a few major studies that also show that a covered call or buy write strategy on an equity index will deliver superior risk adjusted returns. In 2007 Kapadia and Szado at the Isenberg School of Management at the University of Massachusetts did a study on writing calls on the Russell 2000 Index that concluded, ”The results demonstrate that the strategy has consistently outperformed the Russell 2000 Index on a risk adjusted basis.”
To recieve the updated Kapadia and Szado whitepaper, or to recieve a summarized brochure on their study, please e-mail a request , see the contact page.
There is further evidence that call writing can lower risk, in 2006 Callan Associates did a similar evaluation of a buy write strategy on the S & P 500 Index and concluded that the covered call strategy delivered superior risk adjusted returns over an 18 year period from 1988 to 2006. The covered call model includes both the S&P 500 and the Russell 2000 Index in the portfolio.
Another study that is often cited in the financial planning arena is the “Trinity Study”, more commonly known as the 4% rule. It was published in 1998 by three professors at Trinity College in Texas and applies to the ideal withdrawal rate of a retirement fund. The rule suggests that you withdraw 4% or your portfolio in the first year of retirement, then each year increase that by an inflation adjuster. Using that methodology retirees have a very high probability of seeing their funds last at least thirty years. In the original study, a 50-50 stock and bond portfolio had a 95% success rate. The covered call model has been tested at various withdrawal rates and should be able to sustain a 4% + inflation adjusted withdrawal rate indefinitely. It has been tested at higher withdrawal rates like 5% and 6% and proven to work over long periods without drawdown into the retirement nest egg.
Robert Engle won the Nobel Prize in economics in 2003 “for methods of analyzing economic time series with time varying volatility (ARCH).” ARCH and GARCH models as mentioned above can be a useful tool for forecasting volatility. We employ volatility forecasting to determine which option strikes to write for our covered call model.
The proprietary methodology used to manage the covered call income model is based on the results of our own research which has a solid foundation from numerous widely accepted academic studies. The chart below shows the SPY with the implied volatility/statistical volatility indicator which is one that we monitor regularly.
For a tear sheet with current holdings and model returns please contact us, see the contact page.