Value at Risk (VAR) is a scale used to quantify the maximum possible risk associated with an investment. VAR calculates the potential for loss for an investment portfolio over a specified time period, such as a day, week, month, etc. VAR calculates risks under typical market conditions, where there are no extreme market movements. VAR is mostly used by institutional investors, such as banks, fund houses, etc., to ensure they invest according to the amount of risk they afford.
Value-at-Risk (VAR) became a popular way to assess market risk in trading portfolios during the 1990s. Its roots are seen in the capital requirements the New York Stock Exchange set on member companies in 1922. VAR is mentioned in portfolio theory, which was published in 1945.
The VAR metric has become popular due to the importance of assessing the risk of a portfolio. Understanding risk is important because an investment works best when investors invest according to their risk tolerance. Some investors may be able to afford more risk than others. Moreover, large investors like fund houses use VAR to ensure that the risk is according to the fund’s theme.
Value at Risk (VAR) is computed using three different methodologies’ the historical method, the variance-covariance method, and the Monte Carlo simulation. Period, confidence level, and the extent of the potential loss are the three factors that determine VAR.
What is Value at Risk (VAR)?
VAR is a method of calculating the risk of prospective losses for a portfolio or investment. Investors, including retail and institutional investors, frequently utilize value at risk to assess the magnitude and likelihood of prospective losses in a given portfolio.
VAR is used by fund managers to gauge and manage their total level of risk exposure. Investors utilize it to aid decision-making and choose the stocks to invest in. Banks are an institution that uses VAR mostly. VAR is frequently used by banks to calculate how much bank capital needs to be set aside. Banks are frequently required to set aside a portion of their VAR as capital by bank authorities. According to regulators, the bank should set aside 3-5 times the VAR as reserve capital for the same period if it expects to encounter the amount of risk indicated by the VAR. Banks are better equipped to respond to unanticipated events in this way.
Value at risk is an important technique for measuring possible loss because it is simple to grasp and implement. It provides investors and managers with a comprehensive analysis of prospective losses and their likelihood. Value at risk is a more accurate risk indicator than market volatility since it indicates the direction of variations and aids in the decision-making process.
How does Value at Risk (VAR) work?
Value at risk works by aiding in estimating the possibility of a loss by utilizing a confidence interval. VAR assists in defining the probability distributions of individual risks, the correlation between these risks, and the value impact of such risks. In reality, simulations are commonly employed to calculate the VaR of an asset portfolio. VaR places a strong emphasis on downside risk and potential losses. Its use in banks indicates their concern about a liquidity crisis, in which a low-probability catastrophic event causes a loss that wipes out the capital and causes a mass flight of clients.
Banks are the most probable use case of value at risk. Value at Risk has evolved into a risk assessment tool used by most banks and other financial services companies over the past ten years. The inability of risk tracking systems, which were in use until the early 1990s, to identify traders who were taking dangerous risks, has led to their implementation in these organizations. Banks primarily use VAR to assess the size and likelihood of a prospective loss on advances.
VAR is used primarily by banks, investors, fund managers, etc., to assess the risk of their investment. The risk assessment can help make investment decisions. A fund manager could decide the amount of risk associated with a particular investment to see if it matches the theme of the fund. An aggressive fund could afford the higher risk, but a conservative risk may not be able to, and this is judged by calculating the VAR.
Investing risk may be quantified by calculating the value at risk (VaR). It predicts how much money an investment portfolio may lose in a certain amount of time (say, one day) under typical market circumstances. VaR is widely used by financial institutions and authorities as a measure of the safety net’s size.
The p VaR is the highest potential loss for a particular portfolio and time horizon, after discounting all worse possibilities with a combined probability of at most p. Mark-to-market pricing and no portfolio changes are assumed.
What is the History of Value at Risk (VAR)?
Francis Edgeworth first used value at risk, and it dates back to 1888. But VAR started being widely used in the 1990s. VAR is linked to the early 20th-century capital requirements for US securities firms, beginning with a rudimentary capital test that the New York Stock Exchange (NYSE) began imposing on member companies around 1922.
The VAR became an important regulatory tool also. Under the original NYSE rule, companies were required to keep capital equal to 10% of their assets, including proprietary positions and client receivables. This evolved into a mandate that businesses hold capital equivalent to 5% of debits to customers, 10% (minimum) on holdings in municipal or government bonds, 30% on holdings in other liquid securities, and 100% on holdings in all other securities.
Regulators eventually took control of establishing capital requirements. The US Securities and Exchange Commission (SEC) introduced the Uniform Net Capital Rule (UNCR) in 1975 for US broker-dealer’s trading non-exempt securities. This included a “haircut” system applied to a firm’s capital to protect it from market losses that might occur while holdings were being liquidated. Twelve categories, including corporate debt, government debt, convertible securities, and preferred stock, were used to categorize financial assets. Some of them were further divided into subgroups, mainly based on their level of maturity. Long and short positions were netted within subcategories to reflect the impact of hedging, but only a little netting was allowed across subcategories.
The SEC updated these haircuts in 1980 as a result of the fluctuation in US interest rates. New haircuts were introduced based on a statistical analysis of previous market data. They were designed to represent a 95-quantile of the potential financial loss suffered by a company during a one-month liquidation period. 7. This was a value-at-risk statistic, but it wasn’t termed that at the time. The haircut computation used by the SEC was a value-at-risk metric.
Later, new regulatory value-at-risk procedures for banks or securities businesses were put into place. These include the Basel Committee’s 1996 value-at-risk measure, which was largely based on the CAD building-block measure, the 1993 “building-block” value-at-risk measure of the Capital Adequacy Directive (CAD) in Europe, and the 1992 “portfolio” value-at-risk measure of the UK Securities and Futures Authority.
In order to calculate the market risk component of bank capital requirements, the Basel Committee permitted the limited use of proprietary value-at-risk techniques in 1996. Regulatory actions inspired the creation of proprietary value-at-risk measurements in this and other ways.
What are the methods for calculating Value at Risk (VAR)?
VAR can be calculated using the historical method, the variance-covariance method, and the Monte Carlo simulation. The historical and variance-covariance methods are the more conventional methods, while Monte Carlo takes the help of advanced calculations.
1. Monte Carlo Simulation
The returns on securities or portfolios are distributed using the Monte Carlo method of computing VAR. This is carried out in accordance with the analyst’s inputs regarding the historical return and security’s standard deviation. This approach performs several simulations to cover all potential scenarios for security mobility.
This method’s drawback is that it makes a lot of assumptions. To compute the standard deviation of a portfolio of 100 stocks, for instance, we would need to enter each stock’s standard deviation and the correlations between them all. For this, around 5,000 correlations are necessary. So, this approach is only as good as the data it receives.
Let’s suppose the CAC40 index is the asset we care about and use Monte Carlo simulations to determine its VaR.
Selecting a stochastic model for our random variable’s behavior is the first stage in the simulation (the return on the CAC 40 index in our case).
The normal distribution is often used, and in this context, it provides a straightforward basis for calculating VaR. When the asset or the stochastic model is very complicated, making it harder to calculate the VaR, the Monte Carlo simulation technique becomes more significant. VaR (unconditional) must be calculated using Monte Carlo simulation techniques, for instance, if we believe that returns follow a GARCH process. Similarly, when options and other complicated financial instruments are taken into account, VaR calculations need the use of Monte Carlo simulation techniques.
This article examines the differences and similarities between the Monte Carlo simulation approach, the historical method, and the variance-covariance method. As a result, we use the GARCH (1,1) model to replicate CAC40 index returns.
A GARCH simulation of the CAC40 index’s daily returns and volatility is shown in Figures 1 and 2, respectively.
If we choose a confidence level of 99%, for instance, our VaR estimate will fall at the 1st percentile of the probability distribution of daily returns (the lowest 1% of returns). That is, we expect a loss of less than 99% of our VaR estimate (at the 99% confidence level) rather than more. To the same extent, the VaR for a 95% confidence level is equal to the returns in the lowest 5% of the distribution.
2. Historical Method
Historical method is the most straightforward way to determine Value at Risk using historical data. The percentage change for each risk factor on each day is determined using market data from the previous 250 days. Then, 250 future value scenarios are presented using the current market values for each percentage change. The portfolio is valued using complete, non-linear pricing models for each possibility. Below is a mathematical representation of the method.
Value at risk = Vm (Vi/Vi-1)
The number of variables on day 1 is Vi. The number of previous data-gathering days is m.
3. The Variance-Covariance Method
The variance-Covariance Method approach makes the assumption that stock returns are normally distributed and only calls for the estimation of two variables: the expected return and the standard deviation, which permits the creation of a normal distribution curve.
The variance-covariance approach is comparable to the historical method, except instead of using actual data, it makes use of a well-known curve. The normal curve displays the locations of the worst 5% and 1% of the curve. They depend on the standard deviation and the desired level of confidence.
VAR is calculated using the variance-covariance Method using the below equation,
Investors can calculate the portfolio’s potential loss value for various holding times and levels of confidence. If returns are considered to be distributed normally, the variance-covariance technique aids in measuring portfolio risk. The portfolio’s underlying assumptions, such as return normality and constant covariances and correlations, might not hold true in practice.
What role does Value at Risk (VAR) play in the stock market?
VAR plays a vital role in stock markets by helping investors and fund managers choose an investment strategy based on risk tolerance. investors frequently consider risk profiles before making an investment decision. Investments work best when an individual investor can invest according to their risk appetite, especially when it comes to fund managers. Every mutual fund has a level of acceptable risk to its investors. Some funds may be aggressive, while others may be conservative. The fund manager uses VAR to gauge whether the investment they plan to make is in accordance with the allowed risk of the fund.
VAR calculates potential loss by associating the same with an easily comprehensible number. Assume that a stock’s price fluctuates arbitrarily. There are 20 equally likely outcomes, ranging in value from -10 to +10 rupees. There is a 1-in-20 (5%) probability that each outcome will materialize. Your VaR is Rs.10 since the maximum loss in this example is that amount, thus, if you want to be absolutely certain about your possible daily losses, it is that amount. However, your VaR is Rs.9 if you set your confidence level to 95%. This is so because the chance of losing more than Rs.9 in a single day is only 5%.
VAR is widely used to measure market risks. One of the most crucial indicators of market risk is Value At Risk (VaR). At a high level, VaR represents the likelihood that losses will exceed a predetermined threshold depending on the amount of confidence during a holding period. VaR, which is a metric determined for a particular confidence level and, in essence, the line dividing the tail (losses) from the rest of the distribution, is a numerical figure. VaR provides us with information about the tail loss’s lower bound.
What is an example of Value at Risk (VAR)?
Three main factors come into play regarding VAR: Minimum loss, time period, and probability of exceeding that loss.
A 5% VAR of Rs.500 over the following week would indicate the lowest loss of Rs.500 and a 5% chance of this loss happening over the following week. According to the previous assertion, there is a 95% likelihood that the investor’s loss won’t surpass Rs.500 in the upcoming week.
Value at Risk (VAR) may also be expressed as a percentage of the portfolio. For instance, if the portfolio value is Rs.10,000 and the VAR is 5% over the next day, then the comparable VAR is 5% of Rs.200 (2% of Rs.10,000) over the next day.
What are the advantages of Value at Risk (VAR) in the stock market?
Simplicity: The main advantage of using value at risk when investing in stock markets is its simplicity. A single metric called “Value at Risk” describes the degree of risk present in a certain portfolio. An investor doesn’t have to look at any other metric to analyze risk. Furthermore, VAR is represented in both units and percentages, making understanding VAR even easier.
Flexibility: Value At Risk is often included in several kinds of financial software, making access even simpler. The calculation is made simpler by software because it handles your most complex calculations. This increases availability, which is a major benefit of VAR.
Comprehensiveness: All asset classes, including shares, bonds, currencies, derivatives, and more, are suitable for VAR. Investors thus rely on a single scale to measure the risk of all their investments.
Wide acceptability: Value At Risk is regarded as the gold standard and is an integral aspect of risk management 101 at financial institutions, despite the fact that there are differing views on whether its popularity is warranted. Of course, you have good reasons for deploying VAR when your rivals do, your clients demand it, and regulators advise it.
What are the disadvantages of Value at Risk (VAR) in the stock market?
True risk is not calculated: VAR’s main limitation is that it assumes that asset and portfolio returns are normally distributed. As a result, it’s possible to use inputs with artificial return distributions. As a result, the true risk will be underestimated. The historical simulation approach is impractical since it assumes that a portfolio’s previous success is a reliable prediction of its future performance. Numerous methods for calculating VAR, each of which relies on its own unique set of assumptions to arrive at a figure for the portfolio’s VAR, produce wildly divergent results.
Difficult to calculate: Estimating a portfolio’s VAR is a challenging process. It is not enough to simply analyze the risk and return of each security; an investor must also account for the correlations between them. As a result, it becomes more challenging to compute VAR if a portfolio has a wide variety of asset classes.
The worst-case scenario is ignored: 99% percent VAR indicates that the loss is anticipated to be more than the VAR amount in 1% of circumstances, which would be 2-3 trading days in a year with daily VAR. Value At Risk does not mention the size of losses within this 1% trading day, and it certainly does not mention the greatest loss that could occur. The worst-case loss may only be a few percent greater than the VAR, but it may also be significant enough to force the liquidation of your business. The “2-3 trading days each year” could include days when there are terrorist attacks, major bank failures, and other extraordinarily significant high-impact events.
Ineffective for larger portfolios: The return and volatility of individual assets and the correlations between them must be measured or estimated to determine a portfolio’s value at risk. The difficulty and cost of this task increase exponentially as the quantity and variety of positions in the portfolio increase.
Is using Value at Risk (VAR) effective?
Yes, using VAR is effective under most circumstances. Different asset classes and different portfolios can have their VAR measured and compared. Stocks, bonds, currencies, derivatives, and other price-sensitive assets are all subject to Value At Risk. This is why it’s so popular with banks and other financial institutions: it allows them to analyze the profitability and risk of various units and assign risk using VAR.
This approach is called risk budgeting.VAR is merely one metric that gives you a general indication of how much risk is present in the portfolio. Value At Risk is expressed as a percentage of the portfolio’s value or as price units. One of the main benefits of Value At Risk is that it is now effortless to evaluate and apply in analysis. Flexibility and easy interpretation makes VAR highly effective.
