Advantages and disadvantages of sensitivity analysis The tool used for calculating the impact on dependent variables by various values of independent variables is known as sensitive analysis. Calculated under a specified set of rules and regulations, the process of sensitive analysis is widely used in financial models.

Advantages and disadvantages of sensitivity analysis

The tool used for calculating the impact on dependent variables by various values of independent variables is known as sensitive analysis. Calculated under a specified set of rules and regulations, the process of sensitive analysis is widely used in financial models. Other than that, biology, economics, engineering, and geography also use this process (Munikoti et al., 2021). Scenario analysis and switching values are two approaches for sensitive analysis. The scenario analysis approach considers various situations that might affect future value. It then questions these scenarios and recalculates the expected net present values. The switching value approach analyses how much the value of some given variable needs to change to finally affect the choice of an option. These approaches are relative to each other. Both have their advantages and disadvantages. The scenario analysis approach allows managers to verify and change the values of potential variables and different situations. Therefore, the advantage of this process provides a full understanding as well as tests the results to get a profit situation. With future outcomes predicted from before, one can stay prepared for that. The disadvantage of scenario analysis is that the entire process becomes ineffective if one value of the model is incorrect. On the other hand, the advantage of switching values is that by altering discount prices and exchange rates, many scenarios can be predicted (Cinelli and Hazlett, 2020). This is a crucial advantage of ‘what if’ analysis. The disadvantage of switching value analysis is that, with any misinformation about the risks, the accuracy of the process would be hampered. It would not be able to accurately determine the robustness of the preferred option.

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Sensitivity analysis refers to a technique that is used in assessment of variation in the input parameters of a model affecting the output. There are various approaches to sensitivity analysis and each of them have their, merits and demerits that are related to each other. The key approaches in this section include; The first one is One- factor- at a time approach. This parameter develops interest in only one input parameter at time as the other inputs are kept constant. This method is advantageous in that it is easy to understand and also easy to carry out the implementation. This approach however assumes on the independence of the input parameters towards each other. This case is rare in the real word applications. The second one is the local sensitivity analysis that enhances measurement in change in the output as a result of a minute perturbation around a distinct point. This method is applicable in continuous and differentiable input parameters. The merit of this method is that it generates more information based on models behavior. The demerit in this case is that it is computationally time consuming in calculating derivatives for each formula. (Borgonovo, E., … & Maier, 2021)

Global sensitivity approach it conducts investigation based on the input parameter affects the output parameter in that respective manner. This approach provides more detailed information. The disadvantage in this approach is that the process of computation is a bit expensive. The final one is the regression based methods that involves the fixation of the regression model to the output. (Mulani, S. B., & Walters, R. W, et.al, 2021)

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