Value at risk definition and example


What Is Value at Risk (VaR)?

Value at Risk (VaR) is a statistic that quantifies the extent of possible financial losses within a firm, portfolio, or position over a specific time frame (Investopedia). This metric is most commonly used by investment commercial banks to determine the extent and probabilities of potential losses in their institutional portfolios.
The value-at-risk measure was developed by John Hull in 1992 and it has become one of the most widely used measures for assessing market risks.
The VaR model calculates an estimate of potential losses from all possible outcomes based on historical data. It's also called “expected loss” because it represents the expected amount lost if certain events occur. For example, you can calculate how much money your company would lose if its stock price dropped to $10 per share.
However if you try to find each of definition from VAR, you will get
  • Value at Risk (VaR) Explained - Value at risk (VaR) is a statistic that quantifies the level of financial risk within a firm, portfolio, or position over a specific time frame.
  • Value at risk - Wikipedia ( An Introduction to Value at Risk (VAR) - Volatility is not the only way to measure risk. Learn about the "new science of risk management" in using value at risk (VAR).
  • VALUE AT RISK (VAR) - In fact, simulations are widely used to measure the. VaR for asset portfolio. 2. The focus in VaR is clearly on downside risk and potential losses. Its use in ...
  • Value at Risk (VaR) - Value at Risk (VaR) estimates the risk of an investment. VaR measures the potential loss that could happen in an investment portfolio over a period of time.
  • Values at Risk ( Value-at-risk (VAR) definition - - Value-at-risk is a statistical measure of the riskiness of financial entities or portfolios of assets. It is defined as the maximum dollar amount expected to be lost over a given time horizon, at a pre-defined confidence level. For example, if the 95% one-month VAR is $1 million, there is 95% confi...
  • How Accurate are Value-at-Risk Models at Commercial Banks? - VaR models have been sanctioned for determining market risk capital requirements for large banks by U.S. and international banking authorities through the 1996 ...
  • History of Value-at-Risk: 1922-1998 - Jul 25, 2002 ... VaR also has roots in portfolio theory and a crude VaR measure published in 1945. This paper traces this history to 1998, when banks started ...
  • ( ( What Is a Cyber Value-at-Risk Model? - Learn how a cyber value-at-risk model can help you quantify and manage cybersecurity risk from the business perspective.
  • Value-at-Risk: Theory and Practice, Second Edition - by Glyn A. Holton - The definitive book on value-at-risk (VaR) is out in a second edition distributed free online. Start reading now.
  • An Overview of Value at Risk ( Procyclical Leverage and Value-at-Risk - Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policymakers, and business professionals.
  • ( ( ( PAC-Bayesian Bound for the Conditional Value at Risk - Conditional Value at Risk (CVaR) is a family of "coherent risk measures" which generalize the traditional mathematical expectation. Widely used in mathematical finance, it is garnering increasing interest in machine learning, e.g., as an alternative approach to regularization.

The usage of Value at Risk 

Risk managers use VaR to measure and control the level of risk exposure. One can apply VaR calculations to specific positions or whole portfolios or use them to measure firm-wide risk exposure.

Understanding Value at Risk (VaR)

VaR modelling What Is Value at Risk (VaR)?

The value-at-risk measure was developed by John Hull in 1992 as an alternative to traditional measures such as standard deviation and historical volatility. VaR differs from these other statistics because it takes into account not only past returns but also future losses. This means that VaR can be used to determine how much money you could lose on any given day if your stock price were to fall below its current market valuation. The formula for calculating VaR is:

determines the potential for loss in the entity being assessed and the probability that the defined loss will occur. One measures VaR by assessing the amount of potential loss, the probability of occurrence for the amount of loss, and the timeframe.

A financial firm, for example, may determine an asset has a 3% one-month VaR of 2%, representing a 3% chance of the asset declining in value by 2% during the one-month time frame. The conversion of the 3% chance of occurrence to a daily ratio places the odds of a 2% loss at one day per month.

Using a firm-wide VaR assessment allows for the determination of the cumulative risks from aggregated positions held by different trading desks and departments within the institution. Using the data provided by VaR modelling, financial institutions can determine whether they have sufficient capital reserves in place to cover losses or whether higher-than-acceptable risks require them to reduce concentrated holdings.

VaR Methodologies

There are three main ways of computing VaR. The first is the historical method, which looks at one's prior returns history and orders them from worst losses to greatest gains following from the premise that past returns experience will inform future outcomes.

The second is the variance-covariance method. Rather than assuming the past will inform the future, this method instead assumes that gains and losses are normally distributed. This way, potential losses can be framed in terms of standard deviation events from the mean.

Image by Julie Bang © Investopedia 2020

A final approach to VaR is to conduct a Monte Carlo simulSince many trading desks already computed risk management VaR, and it was the only common risk measure that could be both defined for all businesses and aggregated without strong assumptions, it was the natural choice for reporting firmwide risk. ( . This technique uses computational models to simulate projected returns over hundreds or thousands of possible iterations. Then, it takes the chances that a loss will occur, say 5% of the time, and reveals the impact.

Example of Problems with Value at Risk (VaR) Calculations

There is no standard protocol for the statistics used to determine asset, portfolio, or firm-wide risk. Statistics pulled arbitrarily from a period of low volatility, for example, may understate the potential for risk events to occur and the magnitude of those events. The risk may be further understated using normal distribution probabilities, which rarely account for extreme or black-swan events The assessment of potential loss represents the lowest amount of risk in a range of outcomes. For example, a VaR determination of 95% with 20% asset risk represents an expectation of losing at least 20% one of every 20 days on average. In this calculation, a loss of 50% still validates the risk assessment.

financial crisis of 2008 that exposed these problems as relatively benign VaR calculations understated the potential occurrence of risk events posed by portfolios of subprime mortgages. Risk magnitude was also underestimated, which resulted in extreme leverage ratios within subprime portfolios. As a result, the underestimations of occurrence and risk magnitude left institutions unable to cover billions of dollars in losses as subprime mortgage values collapsed.1 risk management, value at risk Value-at-Risk Management in Practice: A Practical Guide to Risk Control for Financial Institutions by John P. Walsh Value at Risk and the Basel Committee on Banking Supervision's new capital requirements Value at Risk and Value-at-Risk are two terms that have been used interchangeably over time. The term VAR is a statistical measure of expected loss based upon historical data. It has become an important tool in financial institutions' risk analysis as well as their regulatory reporting. This article discusses how these concepts relate to each other and provide examples from real-life situations where they can be applied.

The author also explains why it is necessary to understand both concepts when designing or implementing a risk control program. He concludes with some suggestions about what you should do if your institution needs help understanding this topic.

Example Value at Risk (VaR)

It estimates how much a set of investments might lose (with a given probability), given normal market conditions, in a set time period such as a day. (

Over a short time period, up to a one-year horizon, VaR is recognized as the best balance between implementation feasibility for the past and future evaluation; qualities such as longer-term risk measurement are not as obvious. (

A VAR statistic has three components: a time period, a confidence level and a loss amount (or loss percentage). (

Key Elements of Value at Risk Specified amount of loss in value or percentage Time period over which the risk is assessed Confidence interval Example VaR Assessment Question If we have a 95% confidence interval, what is the maximum loss that can occur from this investment over a period of one month? 1. (

It is an estimate of the minimum loss that is expected to be exceeded in a specified time period with a given level of probability. (

Backtests have shown that this kind of generator has to use, for optimization, a very special utility function including skewness and CVaR as measures of risk and a one-year estimate of a bear market move as an expected return; these backtests showed that this kind of utility function was the best. (

Handbook of Statistics, 2012 8 Conditional VaR As noted by Artzner, Delbaen, Eber and Heath [1999], VaR is not a coherent measure of risk because it fails to be subadditive. (

This clearly becomes problematic for stakeholders who aim to hedge tail risks, especially for making catastrophe reinsurance purchasing decisions or for assessing capital-based requirements. (

The Energy Risk Asia Awards recognises excellence across Asian commodities market as well as providing a unique opportunity for companies across… White papers Latest white papers from Risk Library This whitepaper looks at the events and market data of 2020 and discusses the possible takeaways for the risk professional in planning for future economic crises. (

You can learn more about the standards we follow in producing accurate, unbiased content in our editorial policy. (

Key Takeaways

• The value at risk, also known as expected shortfall, is an important tool for measuring market risks. It measures how much loss could be incurred on average if all assets were lost with certainty.

• A common misconception about VaRs is that they are calculated using historical data only; however, this is not true. They are based on current information and future expectations.

• There are two types of VaRs: conditional and unconditional.

  • Value at Risk (VaR) is a measure of the maximum potential loss an organization could incur if a given event occurs. It is calculated using historical data and models.
  • A VaR calculation is used to determine whether a company's exposure to losses is within acceptable limits. If the VaR exceeds a certain level, then the company is said to be exposed to unacceptable levels of risk.
  • Value at Risk (VaR) is a measure of the potential losses that could occur if an investment portfolio were to fall below a certain threshold. It is used to determine whether an investment portfolio is sufficiently diversified to withstand unexpected losses.
  • A VaR calculation is based on historical data and estimates future volatility. The higher the number, the greater the risk.
  • Value at Risk (VaR) is a statistical measure used to quantify the potential financial losses associated with an economic event. The VaR is calculated using historical data and is used as a tool to determine the probability of a specific loss occurring within a given timeframe.
  • The calculation of VaR is based on the assumption that the data being analyzed represents a random sample drawn from the underlying population. In other words, if you were to repeat the same analysis multiple times, the results would be different each time.
  • This is because there is always some level of uncertainty involved in any decision-making Moreover when calculating VaR, we must take into account both positive and negative scenarios. This means that we need to consider what happens if things go well or badly.
  • In addition, we should also include the possibility of extreme values ​​in our calculations. These extremes may represent rare but extremely large losses.
  • What Are Market Risks? Market risk refers to the likelihood that something bad might happen.


Factual sentences referenced across top search results:


Questions used across top search results:

What Is Value at Risk (VaR)?
What is Value at Risk (VaR)? In summary, Value at Risk measures how much money your company could lose within a certain amount of time. It's calculated using historical market movements and past performance. The higher the number, the greater the potential loss. For instance, a 10 per cent value at risk indicates that your business could lose up to ten per cent of its total assets in a given year. What Does Value At Risk Mean?

How Do I Calculate My Company's Value at Risk?


Currently unrated



22nd Jul- 2020, by: Editor in Chief
524 Shares 4 Comments
Generic placeholder image
20 Oct- 2019, by: Editor in Chief
524 Shares 4 Comments
Generic placeholder image
20Aug- 2019, by: Editor in Chief
524 Shares 4 Comments
10Aug- 2019, by: Editor in Chief
424 Shares 4 Comments
Generic placeholder image
10Aug- 2015, by: Editor in Chief
424 Shares 4 Comments

More News  »

PhD Journey Day 29 - focus still on methodology

Recent news
2 days, 9 hours ago

PhD Journey Day 28 - Its not as easy as I thought

Recent news
4 days, 3 hours ago

PhD Journey Day 27 - Matlab, a new gem

Recent news

So for today progress, it's still about working with Data! 

read more
1 week ago

PhD Chapter - Deal with xls data in matlab

Recent news
1 week, 1 day ago

PhD Chapter - couple of important Matlab code

Recent news

Here are couple of important Matlab code 

read more
1 week, 1 day ago

PhD Journey Day 26 - Writing the VECM model from existing research

Recent news

So today goal is rewriting the research about VECM from Norwegian model!

read more
1 week, 2 days ago

PhD Journey Day 25 - still experimenting with Matlab data

Recent news
1 week, 2 days ago

PhD Journey Day 24 - Using data from MacVar to use cointegration

Recent news
1 week, 4 days ago

More News »

Generic placeholder image

Collaboratively administrate empowered markets via plug-and-play networks. Dynamically procrastinate B2C users after installed base benefits. Dramatically visualize customer directed convergence without