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## Sensitivity Analysis

This is a statistical tool that is used to compute the impact an independent variable has on a particular dependent variable under defined assumptions. Statistical analysis can also be referred to as the what-if analysis and can be used for any system or activity. This type of analysis works on the principle of changing the model and observing the behavior. Sensitivity analysis can be performed using the following two approaches:

### Local Sensitivity Analysis

This type of sensitivity analysis is based on derivatives. The term “local” denotes that the derivatives are from one point. While this method is not feasible for intricate models, it is apt for simple cost functions. This is a “one at a time” method. It is used to evaluate the impact of a parameter on a cost function at a given period, while other parameters are kept constant.

### Global Sensitivity Analysis

Monte Carlo methods are often used to implement this type of analysis. This method explores the design space using a global set of samples.

## Fault Tree Analysis

Fault tree analysis uses graphs to study the factors that cause system-level failures. This type of analysis is based on the Boolean logic by combining a sequence of events that are lower level. Fault tree analysis is a top-down approach that is usually used to measure failures in the basic events that lead to system-level failure. Logic gates and events are the two elements that makeup fault tree analysis.

## Volatility Model

One of the sources of uncertainty for financial institutions is the market risk for assets. Market risk is the possibility that the value of the asset will reduce due to changes in particular market factors. Some of these factors may include changes in interest rates, currency rates, price of securities, etc. Also, market risk can affect the value of the exposed financial institution. Institutions involved in security markets must estimate unpredicted changes, which can lead to potential losses. The most heavily used approach of estimating the exposure of a financial institution to market risk in practical applications is the Value-at-risk methodology. The volatility model plays a significant role in this methodology. Volatility is the risk on the value of returns from holding risky assets over a period. If volatility is correctly estimated, the financial institution can gain a substantial advantage