High-Frequency Options Trading | With Portfolio Optimization

ArXiv ID: 2408.08866 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Options Trading, Portfolio Optimization, Option Greeks, Binomial Tree Model, Implied Volatility, Derivatives

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Holy Grail
  • Why: The paper employs advanced mathematical techniques, including Binomial Tree models for American options, Newton-Raphson for implied volatility, and derivations of option Greeks for American options. It demonstrates strong empirical rigor with a substantial backtest on high-frequency (5-minute) SPY options data spanning a month and including the Flash Crash period, detailed portfolio optimization implementation, and clear metrics for strategy evaluation.
  flowchart TD
    A["Research Goal: Effectiveness of HFT Options Trading<br>with Portfolio Optimization"] --> B["Data: SPY Options, 5-Min Intervals, 1 Month"]
    B --> C["Methodology: Greeks, Volatility & Pricing<br>Binomial Tree & Newton-Raphson"]
    C --> D["Investment Universe: Filtering<br>by Implied Volatility & Greeks"]
    D --> E["Portfolio Optimization<br>Mean-Variance vs. Robust Methods"]
    E --> F["Analysis: Long-Short Strategies<br>Basic vs. Advanced (Vega/Rho)"]
    F --> G["Findings: Advanced Dynamic Strategies Show<br>Potential; Basic Strategies Underperform"]