Empirical Analysis Of Option Pricing Based On SSE 50ETF

The SSE 50ETF option as the first floor option in Chinese capital market listed on the Shanghai Stock Exchange on Feb 9, 2015. The naissance of this option not only provides more investment variety for investors but also indicates that Chinese capital market has entered a new era of options. Options can be used to risk management, price discovery and wealth appreciation in capital markets. Therefore, in-depth research on the pricing of SSE 50ETF options is of great theoretical and practical value to promote and perfect the financial market of China.The classical option pricing model proposed by Black and Scholes in 1973(hereafter called BS OPM) as a commonly pricing model is widely used in practice and theoretical research. This model provides a BS formula for European option pricing without paying dividends under a series of ideal conditions. In the BS formula, the five influencing factors of option pricing, except volatility,other factors: the underlying securities' price,risk-free interest rate, exercise price and remaining maturity can be directly observed from the market. Accordingly, volatility is even more important for option pricing.In this paper, we discuss totally four volatility option pricing modes: BS OPM assuming volatility to be a constant, AHBS OPM which approves that volatility is assumed to be the function of the options' moneyness, GARCH OPM supporting that volatility changes over time and HESTON OPM holding the opinion that volatility subjects to stochastic process and empirically study the 4 models' pricing error based on SSE 50ETF option. We screen and alter the option trading data between Jun 2015 and Oct 2016 to ensure the validity of sample according to the trading volume and the remaining maturity and other indexes. Finally we receive 7837 call options and 7829 put options respectively, and classify the options by moneyness. Then, the parameters of the 4 models are estimated by using statistical methods such as least squares estimation, time series analysis and maximum likelihood estimation by Python and Eviews software programming, and we get the theoretical price of the 4 models by using the corresponding pricing formula. Ultimately, we compare the differences of the 4 models'pricing error using the ME, MAPE and MSE error criteria both in in-sample fitting performance and out-sample forecasting performance. The empirical results shows that in the case of each error criterion. HESTON OPM performances best, AHBS OPM takes a second place, GARCH OPM and BS OPM do a little bit worse as a whole both in-sample and out-sample. Under the standard of ME, the 4 models all underestimate options' price for whether out-the-money call option or in-the-money put option.