Hybrid Garch(1,1) European Option Pricing Model With Ensemble Empirical Mode Decomposition

Abstract

espite the option pricing importance in risk management and selection of portfolios, it is challenging to accurately price options due to unpredictable feature of asset prices. There are numerous risks in the nancial markets, mainly emanating from the inaccurate computation of option prices. The inaccuracy is mostly attributed to volatility. Using GARCH or other stochastic processes directly is unsuitable for option pricing. There is the need to decompose original series with some properties to attain more nancial time series aspects. E-E-M-D generally performs well in capturing volatility and option pricing of nancial data with non-linearity and non-stationarity properties. We construct a hybrid GARCH(1,1) model with the ensemble empirical mode decomposition in European option pricing. Using E-E-M-D, we decompose the original daily returns into low frequency, high frequency, and trend terms, and use these terms in the hybrid GARCH(1,1) European option pricing model in options pricing. We obtain option prices for di erent maturities by applying Monte Carlo simulation. Our empirical results clearly illustrates that the hybrid GARCH(1,1) European option pricing model e ectively predicts volatility features and performs better than BSM73 and GARCH-M(1,1). The performance of the hybrid GARCH(1,1) European option pricing model incorporating just the low-frequency term further depicts the signi cance of decomposing the original returns using E-E-M-D by reducing option pricing errors signi cantly. Therefore, the hybrid GARCH(1,1) European option pricing model is a highly innovative and e ective method of option pricing.