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This research aims to evaluate investment strategies in financial derivatives, particularly focusing on option pricing and risk hedging.

An empirical examination of investments in derivative financial instruments using the example of option strategies modeling

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This research aims to evaluate investment strategies in financial derivatives, particularly focusing on option pricing and risk hedging.
Utilized mathematical modeling and the Black-Scholes option pricing model in the Python software environment.
Applied machine learning algorithms Random Forest and XGBoost for predicting asset prices and volatility.
Analyzed various option strategies such as Long Straddle and Long Strangle.
Using machine learning models in volatile markets improved investment decision-making by predicting price direction.
Identified key characteristics influencing the volatility of basic assets through empirical analysis.
Demonstrated that integrating volatility metrics with probabilistic forecasts simplifies strategy selection.

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Randomized trial demonstrates the importance of machine learning in option investment decisions, suggesting better strategies.

Vladimir Petrovich Mischenko (2026) studied this question.

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