The 3% Option Strategy
Put a million dollars into this strategy. Use only ten to thirty thousand of it to trade index options. The rest stays in cash. That's what Guasoni, Mayerhofer, and Zhao (2026) get when they let an algorithm pick the best mix of index options, month after month, for twenty-five years (January 1996 – December 2020): a small bet behind a big wall of money.
Each month, the algorithm performs a delicate piece of financial arithmetic. It forecasts next month's volatility using a basic log regression on past implied and realized values, builds an empirical distribution of index returns, and solves a quadratic optimization. To prevent the optimizer from blowing up the account with infinite leverage, the authors impose a "position limit" (a cap on the Euclidean norm of the weights). This hard cap keeps gross option exposure at a timid 1% to 3%, yielding glamorous annualized Sharpe ratios up to 1.94 for the Nasdaq 100. For the S&P 500, this yields Sharpe ratios between 0.84 and 1.46, easily doubling the market index's meager 0.40 Sharpe ratio over the same period.
How does it achieve this? It sells out-of-the-money puts, collecting premium for bearing crash risk, while taking mixed call positions—buying deeper out-of-the-money calls and selling calls closer to the money—to shape the portfolio's directional exposure. The optimizer rotates exposure across the put surface, shifting weight across different strike levels depending on the volatility regime, rather than mechanically selling the same strikes. In plain terms, the portfolio is best understood as a put-selling strategy, with call positions used to manage market exposure and payoff shape.
Have Guasoni and colleagues found the perfect strategy to get rich? Not quite. This entire setup rests on a volatility forecast that is essentially just a log regression extrapolating past implied and realized volatility — it cannot predict the next black swan, whether that's the yen carry-trade unwind of August 2024 or the tariff shock of April 2025. To be precise, the paper admits the March 2020 crash caused "large losses." The strategy survived only because 97% of the money was sitting in cash, not because the model is robust. Worth adding a caveat the paper itself doesn't spell out: the monthly margin check — calculated using the Theoretical Intermarket Margin System (TIMS), stress-tested against a grid of index moves combined with ±75% implied-volatility shocks — is only a snapshot taken at portfolio formation, not a dynamic constraint, since brokers recalibrate requirements day to day as index levels, volatility, skew, and clearing conditions change. When initial margin is infeasible, the model holds cash, so reported performance is conditional on months with feasible margins. Without that magical volatility forecast, the Sharpe ratio collapses from 1.20 to a weak 0.08, suggesting that much of the apparent edge may depend on the forecast.
In the end as Nicholas Taleb could say with his delicate sarcasm, the paper proves a profound economic truth: if you put 97% of your money in a bank account, your portfolio will survive almost anything. It's a brilliant way to manage risk, provided your definition of "alpha" is just keeping your original million intact while occasionally buying a lottery ticket.
References
Guasoni, P., Mayerhofer, E., Zhao, M. (2026)
Options portfolio selection with position limits — Journal of Empirical Finance, Vol. 89, 101770
Key takeaways
- Guasoni, Mayerhofer and Zhao (2026) argue that a tiny monthly options overlay — 97-99% cash — beats the S&P 500 on a risk-adjusted basis: Sharpe up to 1.46, against 0.40 for the index itself.
- Selling out-of-the-money puts, the authors claim, is what actually drives the edge; calls are just there to manage market exposure, not to generate returns.
- If they're right, the main caveat is how it's measured: the margin check is a once-a-month snapshot, not the daily recalculation a real broker does — and treating a 97% cash cushion as risk management is a stretch too.