A Hybrid Particle Swarm Optimization to Forecast Implied Volatility Risk

作     者：Kais Tissaoui Sahbi Boubaker Waleed Saud Alghassab Taha Zaghdoudi Jamel Azibi 

作者机构：University of Ha’ilApplied CollegeHail CitySaudi Arabia University of Tunis El ManarFaculty of Economic Sciences and Management of Tunisthe International Finance GroupTunisTunisia Department of Computer and Network EngineeringCollege of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Research Unit on Study of Systems and Renewable EnergyNational College of Engineering of MonastirUniversity of MonastirMonastirTunisia LAREQUAD&FSEGTUniversity of Tunis El ManarTunisTunisia University of JendoubaLaw FacultyManagement and Economic Sciences of JendoubaTunisia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第73卷第11期

页      面：4291-4309页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：This research has been funded by Scientific Research Deanship at University of Ha’il Saudi Arabia through Project number RG-20210 

主　　题：Forecasting Cboe’s volatility index COVID-19 pandemic nonlinear polynomial hammerstein model hybrid particle swarm optimization 

摘      要：The application of optimization methods to prediction issues is a continually exploring *** line with this,this paper investigates the connectedness between the infected cases of COVID-19 and US fear index from a forecasting *** complex characteristics of implied volatility risk index such as non-linearity structure,time-varying and nonstationarity motivate us to apply a nonlinear polynomial Hammerstein model with known structure and unknown *** use the Hybrid Particle Swarm Optimization(HPSO)tool to identify the model parameters of nonlinear polynomial Hammerstein *** indicate that,following a nonlinear polynomial behaviour cascaded to an autoregressive with exogenous input(ARX)behaviour,the fear index in US financial market is significantly affected by COVID-19-infected cases in the US,COVID-19-infected cases in the world and COVID-19-infected cases in China,*** performance indicators provided by the developed models show that COVID-19-infected cases in the US are particularly powerful in predicting the Cboe volatility index compared to COVID-19-infected cases in the world and China(MAPE(2.1013%);R2(91.78%)and RMSE(0.6363 percentage points)).The proposed approaches have also shown good convergence characteristics and accurate fits of the data.