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A Hybrid Particle Swarm Optimization to Forecast Implied Volatility Risk

Computers, Materials & Continua 2022年第11期73卷 4291-4309页

作者： kais tissaoui Sahbi Boubaker Waleed Saud Alghassab Taha Zaghdoudi Jamel Azibi University of Ha’il Applied CollegeHail CitySaudi Arabia University of Tunis El Manar Faculty of Economic Sciences and Management of Tunisthe International Finance GroupTunisTunisia Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Research Unit on Study of Systems and Renewable Energy National College of Engineering of MonastirUniversity of MonastirMonastirTunisia LAREQUAD&FSEGT University of Tunis El ManarTunisTunisia University of Jendouba Law FacultyManagement and Economic Sciences of JendoubaTunisia

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.

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

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