Goodness-of-fit represents a statistical measure or assessment that indicates how well a theoretical model or distribution corresponds to observed data. It is frequently employed to evaluate the appropriateness of a model for a specific dataset. This metric validates model accuracy.
Mechanism
In quantitative analysis, goodness-of-fit is determined by comparing observed frequencies or data points with those predicted by a theoretical model. Statistical tests, such as chi-squared tests or Kolmogorov-Smirnov tests, quantify this correspondence. These tests yield a p-value or a specific test statistic.
Methodology
Within crypto trading and risk systems, goodness-of-fit is applied to validate predictive models for asset prices, volatility, or liquidity. It assesses if simulated market data accurately reflects real-world conditions or if an options pricing model adequately captures observed market prices. This ensures the reliability of algorithmic trading strategies and risk calculations.
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