Implied volatility of S & P 500 companies during earnings announcement a structured Bayesian approach

Tan, Teik Kheong (2015) Implied volatility of S & P 500 companies during earnings announcement a structured Bayesian approach. Doctoral thesis, Asia e University.

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Abstract

Can an earnings announcement provide a volatility arbitrage opportunity which allows an investor to profit from a sudden, sharp drop in implied volatility that triggers a similarly steep decline in an option's value? Tan, Merouane, and Connor (2015) developed a methodology that allows an investor to profit from this volatility crush phenomena in weekly options. In addition to managing the risk, this profitable strategy relies on a set of qualifying parameters including, liquidity. premium collection, volatility differential, expected market move and market sentiment. While the effects of persistence and leverage have been thoroughly investigated in the literature, very little has been revealed thus far on the effects of market sentiment and liquidity. Building upon this framework, the effects of market sentiment and liquidity are investigated in the earnings event scenario to further reduce the risk associated with trading options during, earnings announcements. The results of exploratory and confirmatory factor analyses of a four factor model on the dynamic of implied volatility during earnings announcement from the S&P 500 (N= 1060) supported by data collected for the past 15 years are presented. Structural equation modelling (SEM) is used to compare, confirm and refine the model. Bayesian analysis is used to further improve estimates of the model parameters. By comparing values derived from Bayesian and the Maximum Likelihood Estimates (MLE), one can verify the accuracy of the CFA model. Using Bayesian estimation and implied volatility differential to proxy for differences of opinion about term structures in option pricing, anomalous behaviour can be detected, if any.

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