The Empirical Analysis On Option Pricing And Trading Strategies Of SSE 50ETF

The SSE 50ETF option has been officially traded in the Shanghai stock exchange in February 9,2015, China’s derivatives system is increasing. Op-tions can promote the stability of the financial market, which is conducive to investor’s risk diversification. Investors have a more convenient tool in such aspects as hedging, risk aversion. In this paper, we discussed such as the calculation of the volatility, the pricing of options and trading strategies.In this paper, we introduce three models for calculating the volatility, the historical volatility model, the GARCH(1,1) model and the SABR stochastic volatility model, and use them to calculate the volatility of the SSE 50ETF. About historical volatility model and GARCH(1,1) model, we use daily closing price series of SSE 50ETF from February 9,2015 to February 26,2016, to calculate volatility, we introduce the concept of "volatility cone" and compute "volatility cone", and predict the volatility of February 29,2016 to March 25,2016 and comparison of iVIX, it was found that the calculation result is accurate. About SABR model, we give the detailed process of the parameters estimation, using the 1 minute cycle data from February 29,2016 to March 25,2016 to estimate the parameters of the model and we use the estimated parameters, to fit the implied volatility, it was found that the model is effective in the SSE 50ETF option market. Finally, we use the three model’s volatility in the Black Scholes option pricing formula, it was found, to identify the put option pricing more accurate, and the call option pricing is generally high.Option’s trading strategies is very rich diversity, the main two types are about the volatility and the trend. The main task of volatility transactions is to find the spread of actual volatility and implied volatility, if the spread is significantly different, we can through the corresponding options trading to obtain benefits. The trend strategy is to predict the underlying asset price movements for the appropriate options trading. Hurst exponent can predict the trend of the time series, we use 50ETF daily closing price data from February 23,2005 to March 31,2016, to calculate the Hurst exponent, it was found, SSE 50ETF is in a random walk in long-term, and is trend or reversal in the local, the high Hurst exponent indicates that time series has long memory effect and the trend of the time series is quite obvious. We calculated the Hurst exponent and apply a simple trend strategy, it was found that Hurst exponent has a positive correlation with the income series. We find that only use the Hurst exponent can not fully determine the price trends, but trend strategy has a good performance in the time of high Hurst exponent, so Hurst exponent is an important indicator of the judgment on the price trend.