Options trading strategy based on ARIMA forecasting

Pierre Rostan, Alexandra Rostan and Mohammad Nurunnabi

PSU Research Review, 2020, vol. 4, issue 2, 111-127

Abstract: Purpose - The purpose of this paper is to illustrate a profitable and original index options trading strategy. Design/methodology/approach - The methodology is based on auto regressive integrated moving average (ARIMA) forecasting of the S&P 500 index and the strategy is tested on a large database of S&P 500 Composite index options and benchmarked to the generalized auto regressive conditional heteroscedastic (GARCH) model. The forecasts validate a set of criteria as follows: the first criterion checks if the forecasted index is greater or lower than the option strike price and the second criterion if the option premium is underpriced or overpriced. A buy or sell and hold strategy is finally implemented. Findings - The paper demonstrates the valuable contribution of this option trading strategy when trading call and put index options. It especially demonstrates that the ARIMA forecasting method is a valid method for forecasting the S&P 500 Composite index and is superior to the GARCH model in the context of an application to index options trading. Originality/value - The strategy was applied in the aftermath of the 2008 credit crisis over 60 months when the volatility index (VIX) was experiencing a downtrend. The strategy was successful with puts and calls traded on the USA market. The strategy may have a different outcome in a different economic and regional context.

Keywords: Index options; Trading strategy; Options strategy; ARIMA model; GARCH model; Forecasting (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eme:prrpps:prr-07-2019-0023

DOI: 10.1108/PRR-07-2019-0023

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