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A Co-evolutionary Approach for Heston Calibration

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  • Julian Gutierrez

Abstract

We evaluate a co-evolutionary calibration framework for the Heston model in which a genetic algorithm (GA) over parameters is coupled to an evolving neural inverse map from option surfaces to parameters. While GA-history sampling can reduce training loss quickly and yields strong in-sample fits to the target surface, learning-curve diagnostics show a widening train--validation gap across generations, indicating substantial overfitting induced by the concentrated and less diverse dataset. In contrast, a broad, space-filling dataset generated via Latin hypercube sampling (LHS) achieves nearly comparable calibration accuracy while delivering markedly better out-of-sample stability across held-out surfaces. These results suggest that apparent improvements from co-evolutionary data generation largely reflect target-specific specialization rather than a more reliable global inverse mapping, and that maintaining dataset diversity is critical for robust amortized calibration.

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  • Julian Gutierrez, 2025. "A Co-evolutionary Approach for Heston Calibration," Papers 2512.03922, arXiv.org.
    • Handle: RePEc:arx:papers:2512.03922
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