The article considers the possibility of using synthetic data to model a portfolio in conditions of instability in financial markets. Methods based on the analysis of historical data need to be revised to account for rare events that are missing from historical records. The use of generative models makes it possible to generate synthetic data, creating conditions for modeling situations that go beyond the known scenarios. The purpose of the article is to consider the impact of synthetic data on asset management methods in conditions of high market volatility. The methods of creating synthetic data using generative adversarial networks and their role in modeling situations with limited access to necessary information are described. The use of synthetic data in scientific papers confirms their effectiveness in adapting asset management strategies, which contributes to improving results. This underscores the need for their application and guarantees the stability of financial systems in the external conditions that determine the present. In conclusion, it is noted that generative models that create synthetic data increase the accuracy and flexibility of financial portfolio management strategies. The considered approaches open up opportunities for forecasting and decision-making. Due to this, the information contained in the work will be useful to investors and bank employees.

Synthetic data, generative models, volatility

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