Application of volatility forecasting methods in trading selected option strategies

This thesis focuses on the use of volatility forecasting models for trading option strategies aimed at volatility speculation. It is divided into a theoretical and a methodological-practical part. The theoretical part first introduces financial derivatives and derivative markets, followed by a detailed discussion of option derivatives, particularly their characteristics, parameters, and types. Furthermore, the structure of the option premium, the Black-Scholes model including Greek variables and the principles of delta and gamma hedging are also discussed. Finally, the theoretical part introduces the straddle, strangle, iron condor and butterfly option strategies. The methodological-practical part describes the properties of volatility and presents selected forecasting models such as EWMA, HAR, GARCH, and their modifications. These models are then applied to a sample of five selected stocks to generate daily and weekly volatility forecasts, which subsequently serve as signals for evaluating option strategies. The most robust results were achieved by the HAR and AHAR models, which, in combination with the short iron condor strategy, provided the most stable returns. However, it was shown that a trading approach based solely on the quintile distribution of volatility premium is not universally effective and requires the addition of risk management, particularly in handling loss-making positions.