A Hybrid Particle Swarm Optimization to Forecast Implied Volatility Risk

查看全文 作　　者：Kais [1,2]Tissaoui;Sahbi [3,4]Boubaker;Waleed Saud [1]Alghassab;Taha [1,5]Zaghdoudi;Jamel [6]Azibi

机构地区：[1]University of Ha’il,Applied College,Hail City,Saudi Arabia;[2]University of Tunis El Manar,Faculty of Economic Sciences and Management of Tunis,the International Finance Group,Tunis,Tunisia;[3]Department of Computer and Network Engineering,College of Computer Science and Engineering,University of Jeddah,Jeddah,21959,Saudi Arabia;[4]Research Unit on Study of Systems and Renewable Energy,National College of Engineering of Monastir,University of Monastir,Monastir,Tunisia;[5]LAREQUAD&FSEGT,University of Tunis El Manar,Tunis,Tunisia;[6]University of Jendouba,Law Faculty,Management and Economic Sciences of Jendouba,Tunisia

出　　处：《Computers, Materials & Continua》索引2022年第11期,共19页

基　　金：This research has been funded by Scientific Research Deanship at University of Ha’il,Saudi Arabia through Project number RG-20210. 摘　　要：The application of optimization methods to prediction issues is a continually exploring field.In line with this,this paper investigates the connectedness between the infected cases of COVID-19 and US fear index from a forecasting perspective.The 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 parameters.We use the Hybrid Particle Swarm Optimization(HPSO)tool to identify the model parameters of nonlinear polynomial Hammerstein model.Findings 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,respectively.Statistical 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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A Hybrid Particle Swarm Optimization to Forecast Implied Volatility Risk

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