Practice-relevant model validation: distributional parameter risk analysis in financial model risk management.

Area of Science:

• Quantitative Finance
• Risk Management
• Computational Economics

Background:

• Model validation is crucial for selecting effective financial models.
• Organizations face challenges in choosing between complex and operationally effective models.
• Parameter risk is a key consideration in financial model selection.

Purpose of the Study:

• To develop a model risk management methodology for assessing parameter risk.
• To guide financial quantitative analysis teams in model selection decisions.
• To address scenarios with data constraints requiring joint market calibration and historical estimation.

Main Methods:

• Devised a methodology for distributional assessment of parameter risk.
• Integrated market calibration and historical estimation procedures.
• Applied the approach to a natural gas storage modeling context.

Main Results:

• The methodology provides a meaningful distributional assessment of parameter risk.
• It enables joint application of market calibration and historical estimation.
• Demonstrated utility in a natural gas storage modeling scenario for P&L reporting and trading strategies.

Conclusions:

• The proposed distributional parameter risk approach offers an accessible technique for model selection.
• It effectively balances operational effectiveness and structural complexity in model validation.
• Supports informed decision-making in financial quantitative analysis under data constraints.