#modellingstandards

Modeling standards refer to the best practices and guidelines for creating and using models in various fields, such as engineering, finance, and data science. Here are some common modeling standards: 

Documentation: Models should be properly documented, including information on the model's purpose, assumptions, inputs, and outputs. 

Validation: Models should be validated by comparing their outputs with actual results or data. The validation process should be documented and include sensitivity analyses. 

Sensitivity analysis: Sensitivity analysis is the process of varying the inputs of a model to assess the impact on the outputs. It should be performed to understand how sensitive the outputs are to changes in inputs. 

Transparency: The assumptions and logic used in the model should be transparent and understandable to users. 

Robustness: Models should be designed to be robust and able to handle different scenarios and inputs. 

Calibration: Models should be calibrated to reflect current market conditions or the current state of a system. 

Testing: Models should be tested thoroughly to ensure they perform as expected under various conditions. 

Maintenance: Models should be regularly reviewed and updated to ensure they remain relevant and accurate over time.