| Abstract [eng] |
This master thesis explores the complexities of option pricing, with the main focus on both plain vanilla and exotic options, to the evaluate the effect of change in parameters essential in order to determine option contract value. The Master thesis is structured into three primary sections: literature analysis, research model and its results, with conclusions and recommendations. The literature review explores the background of derivatives, highlighting their complexities and focusing on option contracts and their sensitivity to changes in parameters such as strike price, time to maturity, spot price, dividend yield, volatility, and risk-free rate. Additionally, it reviews and compares the historical and theoretical development of option pricing models, from the Black-Scholes model to advanced AI-driven techniques. It is noteworthy that the integration of macroeconomic variables and ESG factors, along with the impact of recent global events on market dynamics, is gradually being examined and incorporated into the valuation of option contracts. Following the literature analysis, the research uses a scenario-based approach to compare trading strategies, employing the Greeks to evaluate sensitivity to market changes. It also utilises a modified Black-Scholes-Merton model to assess the pricing of European and chooser options, focusing on key parameters like volatility, strike price, and time to expiration. Empirical data analysis is performed using Excel, offering insights into the practical application of theoretical models. The findings indicate that while chooser options provide better risk resistance and cost efficiency compared to European options and certain trading strategies, their pricing is significantly affected by market conditions and specific parameters. The thesis concludes by highlighting the importance of integrating diverse factors, including artificial intelligence (AI) and computational models, in option pricing to navigate future market uncertainties and optimise option pricing while effectively managing associated risks. |
