How do you choose and calibrate the appropriate risk models and parameters for your analysis?

- ?? Ensure data accuracy, completeness, consistency, and relevance. High-quality data is essential for reliable model outputs. - ?? Check the frequency, timeliness, and accessibility of your data. Ensure data is updated and readily available. - ??? Choose risk models that align with your data characteristics and analysis goals. Common models include logistic regression, decision trees, and Monte Carlo simulations. - ?? Adjust model parameters based on historical data and validation tests to enhance predictive accuracy. - ?? Regularly review and recalibrate models to adapt to new data and changing conditions, ensuring ongoing relevance and accuracy.

Assess data quality and availability for risk modeling: Verify accuracy, completeness, consistency, and relevance. Ensure data reliability, representation, and sufficiency. Access historical and current data covering relevant time periods, markets, and instruments. Prioritize frequency, timeliness, and accessibility. Best practices: Rigorous validation, comprehensive documentation, and continuous monitoring ensure data integrity for robust risk analysis.

?Data quality: Accuracy,Completeness,Consistency,Relevance ?Data availability:Frequency, Timeliness, Accessibility ?Data characteristics: Reliability,Representativeness,Sufficiency ?Data sources:Historical & Current data, Coverage ?Data validation techniques:Cross-referencing multiple sources,Statistical tests & Time series analysis ?Data governance framework:Data quality stds,Data stewardship roles & Data quality audits ?Data transformation needs:Normalization of data,Handling of missing values & Aggregation or disaggregation of data points ?Assess potential data biases:Selection,Survivorship & Reporting bias ?Plan for data updates and maintenance:Processes for data refreshes, Version control for datasets, Documentation of data lineage

Une chose qui bien expliquer et intéressante Effectivement quand nous parlons de la qualité des données nous incluant disponibilité Donc je complète ce article avec deux apports au niveau de chaque critère Au niveau de la qualité : il faut citer la sécurité des données Et au niveau de la disponibilité : le taux de non réponses qui ne doit pas dépasser 15% pour avoir une bonne disponibilité

Understand risk model assumptions and limitations: Classify risk models as parametric, non-parametric, or hybrid. Parametric models assume specific distributions and dependence structures. Non-parametric models rely on empirical data. Hybrid models combine approaches. Choose a model aligning with risk factors and desired measures. Consider model suitability, accuracy, and applicability to risk analysis goals.

In my experience, all models have limitations that are important to understand. I used a parametric model that made assumptions of normality. This simplified the analysis, but failed to capture tail risk. Based on personal experience, non-parametric models have fewer assumptions, better capturing tail risk, but require more data. Hybrid models balance robustness and efficiency. Monte Carlo simulation incorporates parametric assumptions while allowing sensitivity analysis. I've found model choice involves tradeoffs between computational complexity, data needs, and risk factor dynamics represented. Rigorously examining assumptions and limitations, then conveying these while applying professional judgement, builds model credibility.

All models have limitations. If you haven't documented yours, then why should others trust you. Often, the limitations are more important than the model predictions.

-Parametric models:Assume specific functional of distribution(normal,lognormal,copula models) -Non-parametric models: No assumptions about distribution Rely on empirical data(HS,bootstrapping) -Hybrid models:Parametric & non-parametric (MCS,EVT) -Selection criteria: Best capture characteristics & dynamics of risk factors Consistency with risk measure & horizon -Assess model robustness: Sensitivity to input parameters Performance under stress scenarios -Consider model complexity: Balance between accuracy & interpretability Computational requirements -Evaluate model adaptability: Ability to incorporate new data & changing market conditions -Understand model limitations: Model risk & Areas where the model may break down

Apply suitable calibration methods and criteria: Utilize techniques like maximum likelihood, least squares, or Bayesian inference to estimate model parameters. Evaluate performance with goodness-of-fit tests, loss functions, or information criteria. Choose methods compatible with risk model, data quality, and computational resources to minimize estimation errors and biases.

Advanced approaches such as adaptive calibration frameworks, multi-objective optimization, ensemble methods, and uncertainty quantification enhance the calibration process. Innovations like explainable calibration, continuous monitoring, and cross-asset techniques further refine the process. These sophisticated approaches not only minimize errors and biases but also provide a more comprehensive understanding of risk dynamics. Ultimately, this leads to more accurate, reliable, and adaptable risk models, enabling more informed and effective risk management decisions in complex financial environments.

Validate risk model and parameters: Employ techniques like backtesting, stress testing, or sensitivity analysis. Utilize metrics such as confidence intervals, p-values, or error statistics. Use multiple validation techniques and metrics to assess reliability, stability, and identify potential flaws. Iteratively refine and improve model based on validation results.

Complemented by quantitative validation metrics like confidence intervals, p-values & error statistics. The integration of advanced approaches, including dynamic validation frameworks, synthetic data validation, & cross-model validation, significantly enhances the process. Innovative elements such as behavioral risk integration, blockchain-based tracking, & AI-powered anomaly detection further refine the validation methodology. Real-time dashboards, regulatory scenario integration, peer benchmarking, & explainable AI contribute to a more comprehensive & transparent validation process. This multi-faceted approach not only ensures regulatory compliance but also provides deeper insights into model behavior under various conditions.

Excluding data from the last 12 months and evaluating whether the model can accurately predict the trailing 12 months is another way to validate the model. This technique can uncover any potential overestimation of volatility, which may lead to overestimating overall risk. It's worth noting that changes in the world usually happen gradually rather than abruptly and unpredictably. Therefore, it's been observed that models often overreact to changes based on my experience.

Très intéressant la validation permet d'avoir une bonne données et aussi de réduire le risques de manipulation à certains niveaux

That being said, it is advisable to disclosure the reasons behind relevant choices made during all the process (e.g. outlier treatment, performance metrics, selection of train/test sets, etc.). The documentation detail should be enough for an independent analyst to replicate the process, if needed, without further inquiries.

Comply with regulatory requirements and standards: Adhere to rules and guidelines set by authorities like Basel, Solvency, or FRTB. Follow specified levels of risk models, calibration methods, validation techniques, and reporting formats. Ensure transparency, consistency, and auditability of risk models and parameters. Stay updated with evolving regulatory landscape.

Take a look at conformal prediction. It’s a very promising field of study that surely will help you with more reliable predictions with a better understanding of prediction intervals.