Model-based evaluation of synthetic financial time series data: A comparative study with multi-metric validation

Key Points

• Synthetic financial time series data provides a robust framework for evaluating predictive models in finance, and demonstrates superior performance in volatility assessment.
• Results indicate improved accuracy, with model predictions aligning closely to real market behavior across multiple metrics in comparative evaluations.
• Analysis of various modeling techniques reveals the strengths of synthetic datasets in capturing complex financial dynamics, enhancing overall data reliability.
• This comparative study emphasizes the importance of multi-metric validation, suggesting broader application in financial data modeling and future research directions.