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Options Trading Strategy Based on ARIMA Forecasting The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2399-1747.htm Options Options trading strategy based on trading ARIMA forecasting strategy Pierre Rostan Department of Business, American University of Afghanistan, Kabul, Afghanistan 111 Alexandra Rostan American University of Afghanistan, Kabul, Afghanistan, and Received 13 July 2019 Revised 1 January 2020 Mohammad Nurunnabi Accepted 23 April 2020 Department of Accounting, Prince Sultan University, Riyadh, Saudi Arabia 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 Paper type Research paper 1. Introduction Options securities have been traded using a variety of strategies with more or less success. Most of the strategies applied by traders are based on expectations regarding most © Pierre Rostan, Alexandra Rostan and Mohammad Nurunnabi. Published in PSU Research Review. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http:// PSU Research Review Vol. 4 No. 2, 2020 creativecommons.org/licences/by/4.0/legalcode pp. 111-127 Conflict of interest statement: Disclosure of potential conflicts of interest: All authors declare that no Emerald Publishing Limited 2399-1747 potential conflict of interest exists. DOI 10.1108/PRR-07-2019-0023 PRR commonly the trend of the underlying asset price and the level of the volatility of returns of 4,2 the asset price. These expectations may either rely on: (1) their intuition; (2) their access to information. For example, Hsieh and He (2014) found that foreign institutional investors in the Taiwan market are the most informed traders, with their predictive ability being more apparent in a downward market. They tend to 112 use out-of-the-money options to achieve high leverage, along with medium-term options to obtain large delta exposure and low theta risk, whilst also sacrificing liquidity by forgoing the use of short-term options; and (3) these expectations may finally be obtained from fundamental, technical or econometric models. This paper presents an original option trading strategy based on the well-known auto regressive integrated moving average (ARIMA) econometric model. ARIMA (1, 1, 1) is benchmarked to the generalized auto regressive conditional heteroscedastic (GARCH) (1, 1) model. Section 2 reviews the literature on option strategies. Section 3 explains the methodology underlying the options trading strategy proposed in this paper. Section 4 presents and discusses the results. Section 5 wraps up the main findings of the paper. 2. Literature review Options are traded over-the-counter (OTC) through a dealer network or on centralized exchange markets. OTC markets are less transparent and operate with fewer rules than do exchanges. OTC markets are mostly accessible to institutional investors and companies. Market participants of the options exchange market include professional traders working for proprietary trading firms and for institutional investors such as banks, investment companies, insurance companies or pension funds. In the past two decades, easy access of retail investors to the options exchange market offered by brokerage firms on internet have added myriads of individuals to the number of options market participants. Bauer et al. (2009) have shown that the profile of these individual investors carries some similarities: many of them are gamblers looking for a new source of entertainment with the trade of options providing a huge leverage, often leading to much larger losses than the ones from equity trading. The reasons of their poor performance are attributed to a lack of market timing due to overreaction to past stock market returns, high trading costs and the profile of gamblers that usually lack discipline and rational behavior. This addition of unskilled investors is obviously a benefit for the options exchange market with an increased market depth because of higher trading volumes, higher open interests and tighter spreads. It is also beneficial to professional traders who see their revenues growing by placing opposite trades to the ones of individual investors. As a result, Fahlenbrach and Sandås (2010) found that options strategies are used both by traders who possess non-public information about future volatility and by uninformed speculators who appear to follow unprofitable trend chasing strategies. Options traders implement a variety of strategies, the most common strategies rely on the expectations of two parameters, the trend of the underlying asset price (bull, bear or neutral market) and the volatility of the returns of the underlying asset price (increasing, decreasing or neutral volatility). In this two-dimension framework, traders will apply long call or long strap if they expect a bull market and an increasing volatility, long straddle, short butterflyorlong strangle if they expect a neutral market and an increasing volatility, short straddle, long butterfly or short strangle if they expect a neutral market and a decreasing volatility, etc. These Options strategies are extensively explained in manuals specialized in financial derivatives (Hull, 2017), trading options trading books (Bittman, 1998; Ansbacher, 2000; Trester, 2003; McMillan, 2004 or Kraus, strategy 2010). Exchanges specialized in derivatives also offer training on options trading such as the Chicago board of options exchange (CBOE) (CBOE seminars, 2017)ortheMontreal Exchange seminars (2017). In addition, Chaput and Ederington (2003) examined option spread and combination trading, identifying, which positions are commonly traded (spreads, straddles, strangles) and reviewing the reasons for trading combinations. 113 Yang et al. (2019) identifies the relationship that exists between volatility trading and investors’ types. By analyzing intraday volatility information trading according to the demand for options, these authors determine the types of investors that are informed about future spot market volatility and conduct volatility information trading in a highly liquid options market. Among the studies assessing the performance of options strategies, Guo (2000) showed that after accounting for transaction costs, using the implied stochastic volatility regression method or the GARCH method to forecast the volatility of foreign exchange rates, both delta-neutral and straddle trading strategies involving currency options lead to observed profits not significantly different from zero. Over a decade, Hemler and Miller (2013) analyzed options-based investment strategies vs a buy-and-hold strategy in the underlying stock for 10 stocks. They found that based on monthly returns from five strategies, covered combination and covered call strategies generally outperform the long stock strategy, which in turn generally outperforms the collar and protective put strategies regardless of the performance measure considered (Sharpe ratio, Jensen’s alpha, Treynor ratio or Sortino ratio). Identifying profitable options strategies in the literature, Maris et al. (2007), using volatility forecasts obtained from the combination of an artificial neural network (a two-layer), a short- term oriented naïve method and a mid-term oriented moving sverage of 13-week-model, they endedupwithaprofitable trading strategy with options on FTSE/ASE 20 index. Sheu and Wei (2011) introduced an effective option trading strategy based on superior volatility forecasts using actual option price data from the Taiwan stock market. They found that, implementing a long or short straddle based on the 60-day in-sample-period volatility forecasting, 15 days before options’ final settlement day, achieved a significant return. Simmons (2012) forecasted the ASX 200 index with a differential evolutionary algorithm and applied an option trading strategy based on forecasts that shown to perform better
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