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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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Did weekly economic index and volatility index impact Us food sales during the first year of the pandemic?

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Financial Innovation 2023年第1期9卷 1502-1524页

作者： Narasingha Das Partha Gangopadhyay Economists for Peace and security-Australia Chapter SydneyAustralia Western sydney University SydneyAustralia

We explore the impacts of economic and financial dislocations caused by COVID-19 pandemic shocks on food sales in the United states from January 2020 to January *** use the Us weekly economic index(WEI)to measure economic dislocations and the Chicago Board Options Exchange volatility index(VIX)to capture the broader stock market *** validate the NARDL model by testing a battery of models using the autoregressive distributed lags(ARDL)methodology(ARDL,NARDL,and QARDL specifications).Our study postulates that an increase in WEI has a significant negative long-term effect on food sales,whereas a decrease in WEI has no statistically significant(long-run)***,policy responses that ignore asymmetric effects and hidden cointegration may fail to promote food security during pandemics.

关键词： COVID-19 Food sales Us weekly economic index cboe’s volatility index ARDL model Bewley transformation NARDL model QARDL model

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