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dc.contributor.advisor Ensor, Katherine
dc.contributor.advisor Dobelman, John A.
dc.contributor.authorAiman, Jared
Iglesias, Vicente
Sarkar, Sumit
dc.date.accessioned 2021-07-13T14:14:44Z
dc.date.available 2021-07-13T14:14:44Z
dc.date.issued 2021
dc.identifier.citation Aiman, Jared, Iglesias, Vicente and Sarkar, Sumit. "Modeling SPX Volatility to Improve Options Pricing." (2021) Rice University: https://hdl.handle.net/1911/111012.
dc.identifier.urihttps://hdl.handle.net/1911/111012
dc.description.abstract In this project, we develop a model to predict future stock market volatility and facilitate more accurate options pricing. The Black Scholes model gives an expected premium for an options contract; however, it uses an unknown fixed parameter referred to as volatility. We advance this by using a modified Glosten-Jagannathan-Runkle Generalized Autoregressive Conditional Heteroskedasticity (GJR-GARCH) model that uses previous returns, as well as the market’s expectation of future volatility, to better predict future volatility. Additionally, we apply an Autoregressive Moving Average (ARMA) model to predict the value of future stock prices. We find that our model is able to model volatility better than using either the market volatility or a traditional GJR-GARCH model alone. This is particularly true due to our model’s ability to capture the dependence between the S&P 500 returns and the changes in the market’s expectation of volatility.
dc.format.extent 15 pp
dc.language.iso eng
dc.publisher Rice University
dc.rights Copyright is held by author.
dc.title Modeling SPX Volatility to Improve Options Pricing
dc.type White paper
dc.type.dcmi Text


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