Options Trading Using Econometric Models

Key Takeaways

• ARIMA(1,1,1) correctly values calls 64% of time
• Calls using ARIMA outperform GARCH in bull market
• GARCH excels for puts during high volatility periods
• Put profit peaks coincide with VIX spikes
• Study validates ARIMA for S&P 500 index forecasting

Summary

A 2020 study applied an ARIMA(1,1,1) model to forecast the S&P 500 index for options trading, comparing it against a GARCH(1,1) benchmark. The authors bought undervalued calls and sold overvalued puts based on forecast‑price versus strike‑price differentials. Results showed ARIMA generated higher discounted net profits on calls, correctly identifying 64% of undervalued call opportunities, while GARCH outperformed ARIMA on puts during volatile periods. The research concludes ARIMA is a viable, and in some contexts superior, tool for index‑option strategies.

Pulse Analysis

Econometric modeling has long been a cornerstone of quantitative finance, yet the choice between time‑series approaches remains contentious. While GARCH models excel at capturing conditional volatility, ARIMA’s strength lies in its parsimonious structure and focus on mean reversion. In the context of S&P 500 index options, the study leveraged ARIMA(1,1,1) to generate price forecasts that directly inform buy‑or‑sell decisions for calls and puts. By aligning forecasted index levels with strike prices and option premiums, the authors created a rule‑based trading framework that sidesteps the need for complex volatility surface estimation.

The empirical results reveal a nuanced performance split. During the examined bull market, ARIMA‑driven call trades achieved a 64% hit rate on undervalued opportunities, translating into higher discounted net profits than the GARCH‑based counterpart. Conversely, GARCH’s volatility‑aware dynamics proved advantageous for put positions, especially when the VIX surged, indicating that volatility spikes amplify put profitability. This bifurcation underscores the importance of matching model characteristics to the option’s payoff profile and prevailing market regime, rather than adopting a one‑size‑fits‑all approach.

For practitioners, the study suggests a hybrid strategy: deploy ARIMA for directional call trades in trending markets and switch to GARCH when volatility is elevated and put exposure is desired. Such model agility can improve risk‑adjusted returns while keeping computational overhead modest. Moreover, the research invites further exploration into adaptive model selection algorithms that dynamically assess market conditions, potentially integrating machine‑learning classifiers to trigger the optimal econometric tool in real time.