Option Pricing Regression Model is a financial derivatives pricing method used to estimate the theoretical price of options. This model is based on linear regression analysis and predicts future option prices by fitting historical data between option prices and related factors.

In option regression models, factors such as stock price, risk-free interest rate, exercise price, expiration time, etc. are usually used as independent variables, and the actual price at a certain time point or range in the market is known. Transaction data serves as a sample set. Statistical methods are then used to establish a linear relationship between these independent variables and the corresponding standardized returns (i.e., implied volatility), and derive a multivariate linear equation system.

Finally, based on the obtained multivariate linear equations and various parameter values ??in the current market, the theoretical value of the option under current market conditions can be calculated. This theoretical value can be regarded as a reference guide, and may be biased by other factors in actual transactions.

It should be noted that since this model relies on historical data and can only reflect mathematical formulas developed under past circumstances, there may be errors and uncertainties in real transactions. Therefore, when making investment decisions, it is necessary to conduct a comprehensive analysis combined with other information and experience.