Student Bio
Isaac Mwaura is a highly motivated actuarial science graduate who is ambitious and dynamic with a major focus on providing solutions in real-world financial problems. His research project was focused on option pricing to avert risks emanating from the inaccurate computation of option prices due to unpredictable feature of asset prices. His research shows how the hybrid GARCH(1,1) European option pricing model is a highly innovative and effective method of option pricing. Isaac before starting his M.Sc has worked as a Content supervisor at Kenya National Bureau of Statistics (KNBS) during the 2019 census with tasks of training all the enumerators under his jurisprudence, delegating duties to the enumerators, undertaking census enumeration in the resampled households, and responsible for the census activities during enumeration exercise. He also worked as Voluntary Graduate Assistant at G-United in Taita Taveta county tasked with support school teachers by helping in remedial education through reading Aloud Sessions with young struggling learners, initiating social community projects within the local communities, which helped improve the quality of living. With a great passion, he aspires to use highly innovative option pricing models in the financial world to enhance risk management and selection of efficient investment portfolio.
Project Summary
Project Title :Hybrid GARCH(1,1) European Option Pricing Model with Ensemble Empirical Mode Decomposition
Abstract
Despite 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 financial time series aspects. E-E-M-D generally performs well in capturing volatility and option pricing of financial 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 different maturities by applying Monte Carlo simulation. Our empirical results clearly illustrates that the hybrid GARCH(1,1) European option pricing model eectively 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 significance of decomposing the original returns using E-E-M-D by reducing option pricing errors significantly. Therefore, the hybrid GARCH(1,1) European option pricing model is a highly innovative and effective method of option pricing.