A Hybrid Particle Swarm Optimization to Forecast Implied Volatility Risk

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