Evaluating Option-Based Strategies and Dollar-Cost Averaging

Revisiting Option Strategies and Dollar-Cost Averaging Performance

Author

Nam Nguyen
March 28, 2026 • Estimated Reading Time: 10 minutes

In past issues, we discussed popular investment strategies such as covered calls and collars. In this issue, we continue by examining other strategies, focusing on their performance, limitations, and how they behave under different market conditions.

In this issue:

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Reexamining the Performance of Passive Options Strategies

More than 40 years ago, Merton et al. published two papers [1,2] examining the performance of passive options strategies. They concluded that these strategies outperformed the traditional buy-and-hold approach. At the time of their studies, options data was not widely available, so they used historical volatility to calculate options prices. Merton et al. conducted their research by simulating the impact of options on two portfolios: a broad market proxy of 136 equities and the Dow Jones 30 index. Using a twelve-year period, the backtest incorporated historical volatility and applied the Black–Scholes-Merton model to price the options.

Since then, the options market has become highly liquid, with significant structural changes. A recent article [3] reexamines the strategies studied by Merton et al., along with additional strategies, using actual options data from the period 2012 to 2023. The strategies studied include Call-Write strategies (with seven variants), Put-Write strategies (with two variants), and the Protective Put (PPUT) strategy.

Findings

  • Early studies showed that passive option strategies could outperform the underlying index on a risk-return basis.

  • The options market has evolved significantly, from open outcry and single listings to a high-frequency, electronic environment.

  • The findings suggest that the original strategies no longer provide favorable risk-adjusted returns and that earlier results may have been driven by simplifying assumptions.

  • Recent evidence indicates that simple option strategies generally do not add value to portfolios.

  • However, certain dynamic option strategies can still outperform the S&P 500 on a risk–return basis.

  • Incorporating simple market regime signals can improve the performance of these strategies.

  • The PPUT strategy consistently outperforms the S&P 500 on a risk-adjusted basis.

  • A modified PPUT strategy, which avoids puts after a one-standard-deviation drawdown, delivers higher returns with lower risk.

  • The outperformance may be driven by the widespread use of covered call strategies, which suppress implied volatility and underprice tail risk.

In short, none of the simple options strategies have outperformed the S&P 500. Interestingly, the PPUT strategy outperforms the buy-and-hold approach on a risk-adjusted basis, and the VIX is shown to be an effective regime filter.

Reference

[1] Merton, Robert C., Myron S. Scholes, and Mathew L. Gladstein. 1978. The Returns and Risk of Alternative Call Option Portfolio Investment Strategies. Journal of Business 51: 183–242.

[2] Merton, Robert C., Myron S. Scholes, and Mathew L. Gladstein. 1982. The Returns and Risks of Alternative Put-Option Portfolio Investment Strategies. Journal of Business 55: 1–55.

[3] Andrew Kumiega, Greg Sterijevski, and Eric Wills, Black–Scholes 50 Years Later: Has the Outperformance of Passive Option Strategies Finally Faded?, International Journal of Financial Studies 12: 114.

The Effectiveness of Dollar Cost Averaging Under Varying Market Conditions

Dollar-cost averaging (DCA) is an investment strategy where you invest a fixed amount of money at regular intervals, regardless of market conditions. By consistently buying over time, you smooth out entry prices, reduce the impact of short-term volatility, and avoid the risk of mistiming the market with a single large purchase.

DCA has often been presented as an effective portfolio management technique, and financial advisors and brokers encourage clients to adopt it. But is it truly effective, or merely a marketing scheme?

Reference [4] critically examined this question. The study employed Monte Carlo simulation, rather than historical backtesting, to explore the issue. Specifically, the authors utilized Geometric Brownian Motion (GBM) to simulate stock prices under various market conditions.

Findings

  • DCA involves investing a fixed amount at regular intervals and is commonly used for risk mitigation.

  • The analysis uses Monte Carlo simulations based on geometric Brownian motion to generate price paths.

  • The study compares DCA with a Buy-and-Hold (B&H) strategy across varying levels of market drift and volatility.

  • The results show that DCA underperforms B&H in steadily rising and stable markets.

  • DCA provides better risk-adjusted performance in highly volatile market environments.

  • Market volatility and transaction frequency are key drivers of DCA performance.

  • Lower transaction frequency improves the effectiveness of the DCA strategy.

  • The study highlights the importance of adjusting DCA parameters based on market conditions.

In brief, DCA is effective when volatility is high; otherwise, it underperforms buy-and-hold. Further, the paper leads to interesting questions about the validity of position-sizing techniques such as scaling in and out.

Reference

[4] Siyuan Sang, Ru Bai, Haibo Li, The Dynamic Relationship Between Market Volatility and Dollar Cost Averaging Strategy Returns: An Empirical Investigation, in Proceedings of the 2025 3rd International Academic Conference on Management Innovation and Economic Development (MIED 2025)

Closing Thoughts

In this issue, two strands of research on investment strategies are discussed. The first revisits option-based strategies and shows that simple passive approaches no longer deliver attractive risk-adjusted returns, although more dynamic strategies, especially those incorporating regime signals, can still add value. The second examines Dollar-Cost Averaging and shows that its effectiveness is highly dependent on market conditions, underperforming in stable markets but offering advantages in volatile environments. Taken together, the results suggest that simple, static strategies are no longer sufficient, and that performance increasingly depends on adapting to market regimes and implementation details.

Additional Reading

For further discussion on popular option strategies, please refer to previous issues:

Educational Video

Professor Myron Scholes-Reflections on the Black Scholes Model & its Applications

In this video, Professor Myron Scholes reflects on the development of the Black–Scholes framework and its broader implications. He explains that the model was not intended to be a complete description of reality, but rather a practical approach to valuing options and understanding risk through hedging. He emphasizes that its lasting contribution lies in providing a technology for thinking about contingent claims, which has proven robust despite simplifying assumptions such as constant volatility and interest rates.

He also argues that the true value of option theory is in managing uncertainty, constraints, and time. He says that options should be viewed as tools for flexibility and risk management, particularly for mitigating tail risks and improving compound returns. He highlights that option markets embed forward-looking information, especially about volatility and extreme outcomes, and can be used to dynamically adjust risk rather than relying on static strategies or historical data. Looking ahead, he suggests that the role of option theory will continue to expand as markets become more complex and uncertain.

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