Sensitivity Analysis
Sensitivity analysis determines the impact of independent variable values on a dependent variable, given a set of assumptions. It’s commonly used in financial models to understand how various factors influence the model output. This method is essential in assessing the model structure’s robustness and the primary analysis’s reliability.
Example
Imagine a company trying to predict its revenue growth assumption for the next year. They might use a valuation model to forecast this. By altering the model inputs, like product price or sales volume, and observing the change in model response, they can gauge how sensitive their revenue formula is to these factors.
Types of Sensitivity Analysis
- Probabilistic Sensitivity Analysis: Uses probability distributions for uncertain model parameters.
2. Pattern Mixture Models: Focuses on binary and continuous clinical trial outcomes.
3. Financial Sensitivity Analysis: Concentrates on factors like Bond Price and other financial metrics.
4. Analysis of Computer Experiments: Evaluates computationally demanding models.
5. Multiscale Models: Analyzes models from micro to macro at various scales.
Benefits of Sensitivity Analysis
– Helps in identifying critical model inputs that influence output variables.
– Assists in refining complex models by highlighting areas of uncertainty.
– Enhances decision-making by providing a range of values for future outcomes.
– Validates model structure and ensures it aligns with observed outcomes.
Sensitivity Analysis vs. Scenario Analysis
While sensitivity analysis varies one parameter at a time to see its effect, scenario analysis considers multiple changes in model inputs simultaneously. Scenario analysis is more like a what-if analysis tool, examining various possible future events. In contrast, sensitivity analysis focuses on the potential variability of model output due to changes in individual inputs.
Frequently Asked Questions:
– Is sensitivity analysis a type of risk analysis?
Yes, sensitivity analysis can be considered a subset of risk analysis. It evaluates how uncertainty in model inputs can impact output variables, aiding in risk management.
What is sensitivity analysis in risk management?
-In risk management, sensitivity analysis helps understand the potential variability in outcomes due to changes in input variables. It’s crucial for making informed decisions and preparing for various future outcomes.
