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VAR – Value at Risk



What is VAR or Value at Risk?


What is the maximum amount I might lose on this investment?


Almost every investor who has invested or is considering investing in a risky asset has asked themselves this question at some point. Value at Risk makes an attempt to provide an answer, if only within a fair range. In fact, considering Value at Risk, or VaR, as it is commonly called, as an alternative to risk-adjusted value and probabilistic techniques, is incorrect.

In layman's words, Value at Risk is the maximum loss that a portfolio position is anticipated to sustain (in the future) over a holding period with a given probability (confidence level). VAR analysis is a market risk metric that is equal to one standard deviation of the possible returns on a portfolio of holdings.


Value at Risk (VaR) is a metric that measures the wide range of possible financial losses inside a company, portfolio, or position over a given period of time. Investment and commercial banks use this indicator to assess the magnitude and likelihood of prospective losses in their institutional portfolios.


VaR is a tool that risk managers use to assess and manage risk exposure. VaR calculations can be applied to individual positions or entire portfolios, or they can be used to assess the risk exposure of an entire company.


The historical, variance-covariance, and Monte Carlo approaches can all be used to calculate this statistic.


Due to the risk of independent trading desks unintentionally exposing the business to highly correlated assets, investment banks frequently use VaR modeling to assess firm-wide risk.

Value at Risk (VAR or VaR), has been termed the "new science of risk management," yet you don't have to be a scientist to use it.


VAR analysis is simple to understand, Let’s take an example –


A financial institution may find that an asset has a 4 % one-month VaR of 3%, indicating that there is a 4 percent possibility that the asset will lose value by 3 % in that time frame. The probabilities of a 3 % loss were one day every month when the 4 % risk of occurrence is converted to a daily ratio.


What is Normal Distribution?


Value at Risk (VaR) is a metric that depicts a normal distribution of losses in the past. The computation produces a confidence interval about the likelihood of exceeding a specific loss threshold when the measurement is applied to an investment portfolio.


The normal distribution, also known as the Gaussian distribution, is a symmetric probability distribution centered on the mean, indicating that data around the mean occur more frequently than data far from the mean. The normal distribution will show as a bell curve on a graph.

Points to keep in mind –


· A probability bell curve is referred to as a normal distribution.

· The mean of a normal distribution is zero, while the standard deviation is one.

· Not all symmetrical distributions are normal, whereas, all normal distributions are symmetrical.

· Most pricing distributions are not perfectly normal in reality.


Understanding Normal Distribution


In technical stock market analysis and other sorts of statistical analyses, the normal distribution is the most commonly assumed type of distribution. There are two parameters in the standard normal distribution:


1. The mean, and

2. The standard deviation


For a normal distribution, 68 % of the observations are within +/- one standard deviation, 95 % are within +/- two standard deviations, and 99.7 % are within +/- three standard deviations.

The Central Limit Theorem motivates the normal distribution model.


Regardless of the type of distribution from which the variables are collected, this theory states that averages calculated from independent, identically distributed random variables have nearly normal distributions (provided the sample has finite variance). The symmetrical distribution is frequently confused with the normal distribution.


A symmetrical distribution is one in which a dividing line produces two mirror images, but the real data, in addition to the bell curve that shows a normal distribution, could be two humps or a sequence of hills.


Advantages of Value at Risk (VaR)


1. Easy to understand


The value at risk (VaR) of a portfolio is a single number that shows the level of risk in that portfolio. Value at Risk is expressed as a percentage or in price units. As a result, VaR is rather easy to perceive and comprehend.


2. Applicability


Bonds, stocks, derivatives, currencies, and other assets are all covered by Value at Risk. As a result, different banks and financial organizations can simply utilize VaR to assess the profitability and risk of various assets and allocate risk based on VaR.


3. Universal


Because the Value at Risk number is so extensively used, it has become a benchmark for purchasing, selling, and recommending assets.

Calculating Value at Risk (VaR)

The VAR methodologies for calculation are truly the key to the estimate's dependability. Depending on the data availability, multiple VAR methodologies can be used to calculate the possible value at risk over the same time period.


Let us take an example: If VaR is estimated for a relatively short time range, such as intraday, the amount of data may be insufficient to offer a consistent or credible answer, yet further actual data may not be available (due to trade changes or other restrictions). In such instances, the sample size must be artificially extended using re-sampling procedures in order to get a better picture of the current condition and raise the VaR's dependability.

Let’s discuss the methods used for value at risk Calculation,


There are basically three methods used for Value at Risk Calculation -


1. Historical Method

The Historical Method is the most straightforward of the three methods for calculating Value at Risk. Historical market data is utilized to calculate the percentage change in each risk component for each day, and then-current market prices are added to create a hypothetical data set. The assumption behind this strategy is that history will repeat itself. However, it does have a few drawbacks for the analyst:


• It necessitates a significant amount of data, usually three years or more.

• It only analyses historical data to calculate risk, which may or may not be accurate for future conditions.

2. Parametric method


The parametric technique, often known as the variance covariance method, is the most frequent method for determining VaR. This strategy presupposes that the portfolio's return is normally distributed and that the expected return and standard deviations can completely represent it.


3. Monte Carlo


Value at risk is calculated using this method by applying a nonlinear pricing model to create a number of different future possibilities. When a wide range of risk measurement issues is present, this method is appropriate.

VAR analysis can help you to assess the risk associated with the investment that helps you to limit your risk.

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