Sensitivity analysis

1. IIMSKILLS Sensitivity analysis

2. Introduction Sensitivity analysis involves exploring how alterations in independent variables affect a dependent variable within a defined set of conditions. This method assesses the resilience of models or systems by examining how varying inputs influence outputs. It is critical in decision-making, financial modeling, engineering design, and risk assessment, offering insights into how outcomes react to shifts in crucial factors or assumptions. By pinpointing which variables wield the most substantial influence, sensitivity analysis enables informed decision-making and effective risk management.

3. General Process • Identifying different types of variable inputs that can affect potential outcomes. These variable inputs depend upon a variety of factors including quantity, price, rate of interest (fixed or variable), investment, and various other parameters affecting the results. • The next step in the process is ascertaining or deciding how much range and value of variable inputs to consider in the analysis. The ranges can be varied according to the requirements of a specific project. • After deciding the ranges and values of variable inputs the next process is selecting a method of analysis. The method of analysis can depend on the complexities and objectives of the specific project or model. Common types of methods or techniques can be utilized including one-way analysis, two-way analysis, and multi-way analysis. • In this step, the simulation of multiple scenarios can be determined by putting one variable in the system or model and the other variables constant. By conducting this approach decision-makers can predict the different types of outcomes directly proportional to variables.

4. SENSITIVITY ANALYSIS IN THE FINANCE SECTOR • The finance sector is one of the most powerful and dynamic sectors of an economy. Without finance, no economy or country can thrive or achieve its desired goals. • In the finance sector there are many decisions related to finance are taken by experts and concerned decision-makers. • Sensitivity Analysis is considered a major financial instrument that assists and helps business owners and decision-makers to investigate or evaluate the repercussions of various factors on investments, mergers & acquisitions, risk management, financial models, and more.

5. PROCEDURE TO APPLY SENSITIVITY ANALYSIS • One Variable Analysis • In this method, putting or involving one variable in a system or model while keeping all other variables constant, When the output is generated decision makers can examine or analyze the result and this process repeats until the decision-maker can assess the impact of the overall outcome. • Scenario Analysis: • Scenario analysis is considered one of the methods to apply sensitivity and uncertainty analysis. This analysis focussed on creating different types of scenarios and extracting how variable inputs influence the end results of certain models or systems along with decisions. • Sensitivity Indices: • The next procedure or method leveraged in implementing sensitivity and uncertainty analysis is the sensitivity indices. There are two types of sensitivity indices including Sobol indices or Moris method. Both indices are focused on providing a quantitative measure of the importance of input variables.

6. Tornado Diagram • Sensitivity and uncertainty analysis leverages tornado diagrams for a visual representation of analysis. With the help of a tornado diagram decision-makers understand the importance of different variable inputs which are directly proportional to the outcomes • Scatter Plot • This is another graphical method or procedure utilized in sensitivity and uncertainty analysis apart from sensitivity indices. This method is popular for analyzing multiple variables simultaneously. • In this graphical representation, the value of variable inputs is plotted on the x-axis while the value of outcomes is plotted on the y-axis and each data from the graph represents a specific combination of input and output values. • Monte Carlo Simulation • This procedure or method is observed as a powerful technique that is implemented to analyze the sensitivity and uncertainty of a model or system efficiently. • This process works by generating a huge number of random samples within the boundary and limit of variable inputs. Each sample is used to run models or systems that result in producing scenarios of different outcomes.

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