Prediction And Application Of Volatility Based On HAR Model

In recent years,with the development of information technology,HAR-type models based on high-frequency data have shown excellent prediction effect in the researches of various financial markets volatility.Gold is a hard currency which has both commodity attribute and monetary attribute.It is also a kind of excellent refuge financial assets.Gold futures volatility is closely related to the overall level of risk and risk aversion of investors,but there are few literatures about using HAR-type models to research gold futures volatility.So this paper supplements this research field.In this paper,5-minute high-frequency data is used to construct HAR-type models,which contains more intraday trading information.Moreover,studies have shown that financial assets have long-term memory and market heterogeneity,so it is very appropriate to establish the HAR-type models.Other previous researches have shown that financial assets exist structural breaks and macro factors may affect the gold market volatility.So in this paper,HAR-type models are introduced into the structural breaks and macroeconomic variables,which extend prediction ability and enrich the composition of the models.This step has certain innovation value,and it provides reference significance for research scholars in this field.From the perspective of application value,the optimal HAR model obtained through MCS test can be combined with VaR calculation to more effectively control and manage the tail risk of the gold futures market and provide guarantee for investors to make effective decisions.This paper constructs relevant variables from the perspectives of historical information,structural break and macroeconomic environment.The research results based on China gold futures market can help investors to manage risks and improve the effectiveness of investors' decision.In this paper,on the basis of four classic HAR-type models,there are four HAR-type models with structural break variables and four HARtype models with both structural break variables and macro variables.Then,5-minute high-frequency data of the Shanghai futures exchange contract,the dollar index and U.S.economic policy uncertainty index from January 2014 to November 2019 is used to compose the research sample.The data is used to provide the in-sample evidence and present out-of-sample prediction.The proceed tests the superiority of various variables for forecasting gold futures volatility and compares prediction ability of the different HAR-type models.Finally,the best HAR-type model is applied in VaR calculation to explore the application value of the model in tail risk management of gold futures market.Based on empirical research of gold futures volatility,this paper draws the following conclusions.Firstly,the gold market yield presents fat-tail distribution.The volatility of gold futures is asymmetric and its realized volatility shows significant autocorrelation characteristic.It means that the realized volatility of gold futures has a long memory.Secondly,the gold futures market has multiple points of structural breaks detected by ICSS algorithm.Most of these points caused by some major economic event,and the gold futures market volatility changed significantly near these points.The fact explains that structure breaks play an important role in forecasting gold futures volatility.Thirdly,in the sample estimation,the explanatory power of continuous sample path variation,discontinuous jump variation and the realized semivariances are closely related to time limit.So the gold futures market is heterogeneity.Most coefficients of structure breaks and macroeconomic variables are significant under different term structures,which explains structure breaks and macro variables can effectively improve the prediction accuracy of HAR-type models.So the two factors can not be ignored in modeling.Fourthly,the optimal prediction model under different term is different through MCS test.HAR-RV-SB-DE model and HAR-RV-SJd-SB-DE model perform better in forecasting short-term volatility of gold futures.In forecasting mid-term volatility,HAR-RV-SJd-SB-DE model has a best performance.HAR-RSVSB-DE model outperforms than others in long-term.It indicates that signed jump variation contain more short-term and mid-term information for forecasting gold futures volatility.Historical information of realized volatility also has good effect to predict short-term volatility.In terms of the long-term volatility forecast of gold futures,the realized semivariances provide more effective information.Fifthly,through the failure frequency test,combining with the generalized geometric Brownian motion and HAR-RV-SB-DE model can significantly improve the accuracy of the VaR model in the process of Monte Carlo simulation.It helps investors better measure and manage risk.Therefore,HAR-RV-SB-DE model has great application value,which can effectively predict the gold futures market volatility and also can improve the accuracy of the tail risk measurement.