The object of this work is investment in financial derivatives. The subject of this article is empirical strategies for evaluating options and methods of hedging risks in investments in options. The methodological basis of the study was the fundamental concepts of risk assessment using option sensitivity parameters (Delta, Gamma, Theta, Vega) and Black-Scholes option pricing model. Empirical methods of research included experimental mathematical modeling in the Python software environment of option price strategies. Predicting the prices of basic assets and assessing the traits that influence volatility was done using machine learning algorithms Random Forest and Extreme Gradient Boosting (XGBoost). The scientific novelty of the research is in explaining the approach, which integrates volatility metrics and probabilistic stock price forecasts obtained using machine learning technologies into the option strategy selection process. The assessment of the importance of the characteristics that influence price volatility in basic option assets is particularly practical for making sound investment decisions. The decomposition of factors determining price volatility provides additional analytical arguments for choosing an investment strategy. The main result of this article demonstrated that in case of a volatile market for basic assets, it is necessary to use ML models to make investment decisions, which give a probabilistic idea about the direction of price movement and factors influencing the volatility of prices of basic assets. Thus, in this article the strategies of evaluation of options are analyzed on the example of an option with a basic asset - a stock; calculations were made for strategies Long Straddle, Long Strangle, Calendar Spread. It is shown that the application of ML models provides an empirically grounded insight into the significance of the characteristics determining the volatility of the price of a basic asset, which simplifies the choice of investment strategy.
Vladimir Petrovich Mischenko (Sun,) studied this question.