The Relation Between Option Mispricing and Volume in the Black‐scholes Option Model

Abstract

We investigate the relation between mispricing in the Black-Scholes option pricing (BSOP) model and volume in the option market. Our results indicate heavily traded call options are priced more efficiently and have lower mispricing errors than thinly traded options. However, this relation shifts significantly on days when call option trading is high. On high-volume days, the BSOP model mispricing errors are significantly larger than mispricing errors on normal-volume days. We believe large increases in volume may reflect new and changing market information, thus making pricing less efficient in the BSOP model.

Robust Artificial Neural Networks for Pricing of European Options

“…There is also evidence of outliers present in at-the-money long maturity options. Drawing on Long and Officer (1997) the long-maturity at-the-money outliers instead, may be attributed to microstructure effects. As Long and Officer show, excessive demand for certain options may also induce the presence of outliers.…”

“…Neural networks trained on the least squares error criterion are highly influenced by outliers, especially in the presence of nonGaussian noise (Bishop, 1995). Options data are known to be heavily influenced at least by noise due either to thin trading or to abnormal volume trading (Long and Officer, 1997;Ederington and Guan, 2005) and exhibit a strong time-varying element (Dumas et al, 1995;Cont and Fonseca, 2002). Consecutively, robust estimation is expected to improve out-of-sample pricing of options.…”

Information Transmission in the Shanghai Equity Market

“…This suggests a correlation between volume and pricing errors in asset pricing models. Long and Officer (1997) test for a correlation between mispricing by the Black‐Scholes option pricing (BSOP) model and option trading volume. They find greater pricing errors in the BSOP model on abnormally high volume days, but lower pricing errors for high average volume options.…”

Estimation of Tail Risk and Moments Using Option Prices with a Novel Pricing Model under a Distorted Lognormal Distribution

“…Precise option price is the basis of computing risk measurement with the model-free estimation. But many empirical results (see, e.g., MacBeth and Merville [15]; Gultekin et al [16]; and Long and Officer [17]) show that the traditional B-S-M model mispriced the options, sometimes undervalued options and sometimes overvalued options out of the money or in the money. So, we think this is one kind of investing psychology or behavior.…”