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杠杆 ETF 期权波动率曲面统计套利

文章 arXiv papers · 作者: Sergey Nasekin et al.
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总结

论文检验按价内外程度缩放，能否使杠杆和非杠杆 ETF 期权的隐含波动率微笑在统计上趋于一致。研究构建自助法一致置信带来比较缩放后的微笑，发现差异仍然存在，说明这种变换无法完全消除两者差异。

在交易应用方面，作者利用动态半参数因子模型开发统计套利决策方法。该方法比较观测到的杠杆 ETF 隐含波动率曲面与模型估计值，并据此提出交易建议。论文报告称，收益为正的概率较高，也考察了样本外预测和隐含波动率曲面的潜在动态特征。这些发现表明所研究市场可能存在机会，但摘要没有提供绩效数字、交易成本、实施细节或数据范围。研究还将缩放方法扩展到 Heston 随机波动率情形，以提高可处理性。

核心观点

  • 自助法一致置信带显示，按价内外程度缩放无法消除杠杆和非杠杆 ETF 期权微笑之间的统计差异。
  • 动态半参数因子模型比较实际观测和模型估计的隐含波动率曲面，以生成交易建议。
  • 作者报告称，该策略产生正收益的概率较高。
  • 分析包括样本外预测和隐含波动率曲面动态特征研究。
  • 缩放方法纳入了 Heston 随机波动率模型。

标签

期权波动率套利统计学衍生品定价
全文
# Model-driven statistical arbitrage on LETF option markets


# Model-driven statistical arbitrage on LETF option markets









In this paper, we study the statistical properties of the moneyness scaling transformation by Leung and Sircar (2015). This transformation adjusts the moneyness coordinate of the implied volatility smile in an attempt to remove the discrepancy between the IV smiles for levered and unlevered ETF options. We construct bootstrap uniform confidence bands which indicate that the implied volatility smiles are statistically different after moneyness scaling has been performed. An empirical application shows that there are trading opportunities possible on the LETF market. A statistical arbitrage type strategy based on a dynamic semiparametric factor model is presented. This strategy presents a statistical decision algorithm which generates trade recommendations based on comparison of model and observed LETF implied volatility surface. It is shown to generate positive returns with a high probability. Extensive econometric analysis of LETF implied volatility process is performed including out-of-sample forecasting based on a semiparametric factor model and uniform confidence bands' study. It provides new insights into the latent dynamics of the implied volatility surface. We also incorporate Heston stochastic volatility into the moneyness scaling method for better tractability of the model.

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。