Model-driven statistical arbitrage on LETF option markets

TL;DR

This paper develops a model-driven statistical arbitrage strategy for LETF options by analyzing implied volatility surfaces, employing a semiparametric factor model, and incorporating stochastic volatility to identify profitable trading opportunities.

Contribution

It introduces a new statistical arbitrage approach using a dynamic semiparametric factor model and extends the moneyness scaling method with Heston stochastic volatility for LETF options.

Findings

The implied volatility smiles differ significantly after moneyness scaling.

The proposed strategy yields positive returns with high probability.

The analysis provides new insights into the dynamics of LETF implied volatility surfaces.

Abstract

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.