But here is a question for you as an investor: Can you predict how much you might lose if an unexpected storm hits the global markets, causing chaos and panic overnight? Chances are, your answer would be a hesitant ‘no’.
However, if you were to ask the same question to a financial institution like a fund house, bank, or investment firm, they would likely have a precise answer for you. Why? Because they manage your money, and they understand exactly how much risk they are taking and what potential losses might occur if their investment strategies go wrong or uncertainty hits them. In other words, they are always calculating the value of the risk they are exposed to.
What is Value at Risk?
Value at Risk, or VaR, is a tool that helps investors, companies, and fund managers figure out how much money they might lose in the worst-case scenario.
Now, why would you want to use VaR?
Well, there are a few reasons. First, it helps you understand the absolute worst outcome for your investment. Second, it helps you check if you have enough money to cover those potential losses.If not, it is like a warning that you might be taking too much risk. Hence, you might want to diversify or liquidate your investments before it’s too late.
Calculating the Value at Risk
When calculating the Value at Risk of an investment, we ask ourselves: “What is the maximum amount I could lose on my investment, given a certain level of confidence, over a specific period?”
We know we just heard some new terms, but let’s break them down.
The time horizon is looking into the future. So, if you have invested Rs 1,000 in a stock and want to know how much you could lose in the next day or week. That is your time horizon. It is how far into the future you are trying to predict.
The confidence level is how sure you want to be. It is usually expressed as a percentage, like 95% or 99%. Let’s say you choose a 95% confidence level. This means you want to be 95% sure your prediction is correct.
Asset or Portfolio
VaR can be applied to a single asset, a portfolio of assets, or even an entire business operation. The choice depends on what you want to assess.
Now, let’s dive into the methods for calculating Value at Risk (VaR).
This method is like looking at the past to predict the future. Let’s say you have Rs 1,000 invested in a stock. You want to know how much you could lose in the next day (your time horizon) with 95% confidence (your confidence level).
As per the historical method, you will look at the past 100 days, and the worst loss was Rs 50 on one of those days. So, your VaR for one day at a 95% confidence level would be Rs 50.
Based on historical data, you are 95% sure you won’t lose more than Rs 50 in one day.
In the variance-covariance method, the volatility of the stock plays an important role. Let’s understand how.
So, in this method, we use math and statistics to make future predictions. You have to figure out two things: the average (mean) return of an investment and the stock’s volatility (standard deviation). For example, suppose a stock typically goes up by 5% on average and has a volatility of 10%. In that case, you can use these numbers to estimate how much you might lose over a certain period if your investment amount is Rs 1,000.
Formula for calculating VaR = [Average Return – (Z-score * Volatility)] * Investment Amount
Note that the Z-score is a term that corresponds to your chosen level of confidence. For a 95% confidence level, the Z-score is approximately 1.96.
So, if we plug in the values from our previous example:
Average Return = 5%
Z-score for 95% confidence = 1.96
Volatility = 10%
Investment = Rs 1,000
If you put in the numbers, you would get a VaR of −146. This means you might expect to lose around Rs 146 with a 95% confidence level if your investment is 10% volatile over the specified time frame.
But, if the volatility becomes 30%, the loss you might expect increases from Rs 146 to Rs 537. This tells us that the higher the volatility of the asset, the higher the losses you would see.
Now, you don’t need to sit and calculate the VaR of each stock in your Demat account because you can easily access the VaR on NSE and BSE’s websites. Here, VaR is updated six times a day.
For example, the VaR of Reliance Industries on NSE is 8.83%, while for SBI, it is 9.64% (*Data as of 4th September, 2:15 pm). This shows that SBI’s stock is more volatile than Reliance on a specific day.
How Does Value at Risk Play an Important Role in Determining Margins?
In the equity segment, every transaction involves a certain level of financial security known as margins. This is collected upfront by the clearing corporation from brokers for the trades they execute. This money ensures that you have the means to cover potential losses from your trade.
Margin requirements typically include VaR (Value at Risk) and ELM (Extreme Loss Margin).
While we have already discussed VaR, let’s delve a bit deeper into ELM.
ELM, or Extreme Loss Margin, is an additional margin complementing VaR. It is designed to provide coverage for potential losses that might exceed what is initially predicted by the VaR models. ELM represents a fixed supplementary margin that is imposed alongside VaR. Both of these margins are calculated based on the value of the trade and are expressed as a percentage of that trade’s total value.
Here is an example: Suppose an investor decides to acquire stocks with a total value of Rs 1 lakh, and the designated VaR and ELM margin for this particular stock is set at 12.50%. In this scenario, a sum of Rs 12,500 is temporarily held as a margin to secure the trade. This ensures sufficient funds are in place to cover potential losses and maintain the financial system’s stability.
To conclude, different firms use VaR in several ways. For example, VaR is used by clearing corporations to understand the margin they must collect, while fund managers use VaR to analyse and manage their total level of risk exposure. Investors can also use the VaR data while making an investment decision.
*The companies mentioned are for information purposes only. This is not an investment advice.
(The author is Vice President of Research, TejiMandi)
(Disclaimer: Recommendations, suggestions, views, and opinions given by experts are their own. These do not represent the views of the Economic Times)