High-Frequency SPY Options Strategies with Portfolio Optimization

Summary

The study examines high-frequency strategies for SPY options using five-minute observations collected over a one-month period. It calculates option Greeks and implied volatility, prices American options with a binomial tree, and estimates implied volatility with Newton–Raphson. It then forms investment universes using volatility and Greek measures and compares portfolio optimization approaches, including mean-variance and robust methods.

Basic long-short approaches focused on implied volatility and Greeks generally underperform in the reported study. Strategies using additional Greek exposures, including Vega and Rho, together with dynamic portfolio optimization show more promise. The evidence is limited to a short sample and the document does not give detailed performance figures, transaction-cost treatment, or a full account of validation. Its findings therefore suggest possibilities for adaptive options portfolios rather than establishing consistent profitability; the authors point to refining parameters and studying less frequently traded options as further work.

Key ideas

• The study analyzes SPY options at five-minute intervals over one month.
• It calculates Greeks and implied volatility, using a binomial tree for American option prices.
• Basic long-short strategies based on implied volatility and Greeks generally underperform in the reported results.
• Strategies that incorporate Vega and Rho with dynamic optimization show potential.
• The short sample limits conclusions about consistent performance across market conditions.

Tags

# High-Frequency Options Trading | With Portfolio Optimization


# High-Frequency Options Trading | With Portfolio Optimization









This paper explores the effectiveness of high-frequency options trading strategies enhanced by advanced portfolio optimization techniques, investigating their ability to consistently generate positive returns compared to traditional long or short positions on options. Utilizing SPY options data recorded in five-minute intervals over a one-month period, we calculate key metrics such as Option Greeks and implied volatility, applying the Binomial Tree model for American options pricing and the Newton-Raphson algorithm for implied volatility calculation. Investment universes are constructed based on criteria like implied volatility and Greeks, followed by the application of various portfolio optimization models, including Standard Mean-Variance and Robust Methods. Our research finds that while basic long-short strategies centered on implied volatility and Greeks generally underperform, more sophisticated strategies incorporating advanced Greeks, such as Vega and Rho, along with dynamic portfolio optimization, show potential in effectively navigating the complexities of the options market. The study highlights the importance of adaptability and responsiveness in dynamic portfolio strategies within the high-frequency trading environment, particularly under volatile market conditions. Future research could refine strategy parameters and explore less frequently traded options, offering new insights into high-frequency options trading and portfolio management.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.