跳至正文
返回文库全部文档

结合投资组合优化的高频SPY期权策略

文章 arXiv papers · 作者: Sid Bhatia
阅读原文

总结

这项研究考察SPY期权的高频策略，使用一个月内收集的五分钟频率观测数据。研究计算期权希腊值和隐含波动率，使用二叉树为美式期权定价，并用 Newton–Raphson 方法估算隐含波动率。随后，研究根据波动率和希腊值指标构建投资范围，并比较多种投资组合优化方法，包括均值方差方法和稳健方法。

报告的研究结果显示，聚焦隐含波动率和希腊值的基础多空方法总体表现不佳。结合更多希腊值敞口（包括 Vega 和 Rho）及动态投资组合优化的策略则更有希望。证据仅限于较短样本，且本文没有提供详细表现数据、交易成本处理方式或完整的验证说明。因此，研究结果表明自适应期权投资组合可能具有潜力，但并未确立其持续盈利能力；作者指出，进一步工作包括改进参数，并研究交易频率较低的期权。

核心观点

  • 该研究分析一个月内以五分钟间隔观察的SPY期权。
  • 研究计算希腊值和隐含波动率，并用二叉树计算美式期权价格。
  • 报告结果显示，基于隐含波动率和希腊值的基础多空策略总体表现不佳。
  • 结合 Vega、Rho 和动态优化的策略显示出潜力。
  • 样本期较短，限制了对不同市场环境下持续表现的判断。

标签

期权高频交易波动率衍生品定价投资组合构建
全文
# 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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。