Affiliations: [a] School of Business, New Jersey City University, Jersey City, NJ, USA | [b] Teachers College, Columbia University, New York, NY, USA | [c] Kate Tiedemann School of Business and Finance, University of South Florida, FL, USA
Correspondence:
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Corresponding author: Huijian Dong, School of Business, New Jersey City University, 200 Hudson St, Jersey City, NJ 07032, USA. Tel.: +1 201 200 3168; Fax: +1 201 200 2004; E-mail: hdong@njcu.edu.
Abstract: The current practice of option price forecast relies on the outputs of various option pricing models. The expected value of the current option price is widely regarded as the best forecast for the future price, assuming the option prices evolve with a Brownian motion. However, volatility clustering, transaction illiquidity, and demand-supply imbalance drive the future option prices off the modeled price targets. Therefore, we suggest using the spline method to forecast option prices directly. The focus is the accuracy of the forecasted asset price in the next period, rather than if the pricing models correctly produce the current price. We use fifteen years of daily SPY American option contract prices to examine the spline model forecast accuracy. Among the 476,882 forecasts produced, the mean forecasting error size is $3.66 × 10-3, with a standard deviation of 1.33 and a median error of $5.54 × 10-17. The forecast accuracy is stable across contracts with different terms and moneyness. The spline forecast model incorporates the illiquidity issue and avoids the vital pitfalls in the current leading option pricing techniques.
