Dynamic Hedging Strategy Of CSI 300 Stock Index Futures Based On Forecast Combination

With the increase of financial market volatility and the improvement of stock index futures trading system,the establishment of effective hedging strategy in combination with stock index futures plays a key role in controlling financial risks,maintaining market stability and protecting the interests of investors.The core of hedge strategy research is the estimation of hedge ratio,and the introduction of dynamic hedge ratio estimation and prediction with high-frequency data is a new direction of current research;on the other hand,the construction of hedge strategy belongs to the research domain of time series modeling and prediction,and for a variety of prediction models,adopting the Forecast Combination methods can usually improve forecasting accuracy and make up for the deficiencies of a single model.This paper first adopts a variety of models to predict the hedging ratio between CSI 300 index and futures.It includes traditional static hedging model(OLS,ECM);MGARCH-type dynamic hedging models based on low-frequency data(CCC-GARCH,DCC-GARCH,BEKK-GARCH);and Realized Minimum Variance Hedge Ratio based on realized measure and Minimum Variance Hedge theory,and adopts ARMA,ARMA-GARCHt/ARMA-GJR-GARCH-t model,ARFIMA/ARFIMAX(ARFIMA model with external interpretation)and Heterogeneous Autoregressive HAR/VHAR model.These models are used to model and forecast the Realized Minimum Variance Hedge ratio as the dynamic hedge models with high-frequency data.Then,this paper combines the above-mentioned low-frequency and high-frequency dynamic hedging models with a variety of Forecast Combination methods,including complex nonlinear combination methods such as kernel ridge regression(KRR)and support vector regression(SVR),and a new dynamic hedging strategy is constructed.Finally,through the MCS test and performance evaluation index,we make a comprehensive evaluation of the prediction effect of various models and the corresponding hedging strategies from multidimensional perspective,providing abundant empirical evidence for the research of hedging strategies and the Forecast Combination theory,and improving the deficiencies of dynamic hedging strategies under the traditional model.This paper uses the 5-minute high-frequency data of CSI 300 index and CSI 300 index futures.The sample range is from January 3,2016 to December 30,2019,totaling 975 trading days.MCS test and performance evaluation indexes are used to comprehensively evaluate the prediction effect of various models and corresponding hedging strategies.Get the following empirical conclusions:(1)The dynamic hedging strategy with the forecasting combination methods can optimize the performance of a single strategy in the performance evaluation,and has a better prediction effect,among which stepwise regression(Step OLS)and kernel ridge regression(KRR)has the most stable and excellent performance.(2)When there are many combination models,the complex timing-weight combination method is generally better than simple constant weight combination and ordinary OLS combination.(3)In the single models,the ARFIMAX model performs well in two aspects of performance evaluation and prediction.It shows that measuring asymmetry and long memory helps to improve the fitting degree of Realized Minimum Variance Hedge ratio,and then improve the forecasting ability and hedging effect.(4)In terms of performance evaluation,the high-frequency model is significantly superior to the low-frequency model(MGARCH)and the traditional static hedging model in all evaluation indexes,indicating the advantages of dynamic hedging strategy based on the realized minimum variance hedging ratio model.(5)In the aspect of prediction ability,the long memory model is generally better than the short memory model.