Applying VaR to Long-Short Volatility-based Strategy

a short overview of how volatility benchmarks can be utilized along with the market-neutral approach for active management of investment portfolios

Applying VaR to Long-Short Volatility-based Strategy

Volatility benchmarks are very helpful tools for figuring out the right strategy to managing investment portfolios. However, applying these benchmarks correctly is the key to effective portfolio management. This article explains how volatility estimate - VaR (Value at Risk) is applied in our Long-Short volatility-based strategy designed to yield the most predictable alpha.

When investors invest in various assets such as stocks, options, and other investments, they seek to find out the probability of the price of the investment shifting upwards or downwards, leading to a profit or a loss. In the securities markets, volatility often corresponds with significant swings in either direction. For instance, when the stock market goes up and down more than one percent over a long time, it is called a "volatile" market. The volatility of an asset is a key factor for determining the prices of options contracts.

Typically, the higher the volatility, the riskier the security. Volatility is a statistical measure of the dispersion of returns for a particular security or market index. Volatility is often measured as either the standard deviation or variance between returns from that same security or market index.

When applying standard deviation or variance to risk management, analysts intend to figure out how the annual interest rate is spread out, which gives an idea of how risky the investment is. Securities with a wider range of price shifts maintain more risk for an investor, as there is more uncertainty associated with the direction of the price.

For example, a growth-oriented stock is typically subject to sharp fluctuations with recurring spikes and reversals, and the direction of the price may be uncertain for quite some time. An investor seeking a higher return would prefer such volatile stocks since they offer a greater return potential if the circumstances are right. Pretty stable stocks carry low risk since they are likely to remain within the same price range for a long time. A stock that yields 7%-10% during a full trading year is an example of a relatively stable stock.

Variance is a statistical measurement in finance that, when applied to an investment’s annual rate of return, helps to track this investment's historical volatility. 

Variance is represented by σ - Sigma, and it is the main mathematic value defining asset volatility. As you may have noticed, the name of our managing company starts with Sigma, which highlights the fundamental importance of the value in application to our advanced long-short volatility-based strategy.

The term “variance” in this context refers to the extent of dispersion of the price values from the price range mean, which is calculated as the average of the squared deviation of each price value from the price range mean. The formula for a variance can be derived by adding up the squared deviation of each price value and then dividing the result by the total number of price values within the price range.

Mathematically, it is represented as,

σ2 = ∑ (Xi – μ)2 / N


Xi = Certain price value in the given price range

μ = Price range mean

N = Number of price shifts in the price range

σ2 = ∑ (Xi – μ)2 / N

However, to apply volatility correctly, we must identify a relatively stable stock first, which is the key task before opening positions utilizing a long-short volatility-based strategy. There are several various metrics and methods used for determining a relatively stable stock. We use an estimate based on 99% VAR (Value at Risk).

According to Investopedia definition, Value at Risk (VAR) is a statistical measure that quantifies the extent of possible financial losses within a firm, portfolio, or position over a specific time frame. This metric is most commonly used to determine the extent and probabilities of potential losses in their institutional portfolios. Risk managers use VaR to measure and control the level of risk exposure.

VaR is calculated as = [Expected Weighted Return of the Portfolio − (z-score of the confidence interval × standard deviation of the portfolio) × portfolio value

The most important metrics in calculating VaR estimate for every particular security are the calculated VAR values or quantiles of the threshold range of 95% or 99% VAR.

The smaller the dispersion of the security’s closing price within VaR limits and the lower the quantity and VaR tail values, then the more stable is the stock.

We can use this security in the future for calculating volatility and threshold deviations from volatility based on normal, or in some more complex cases, abnormal distribution models.

Not only quantity and quality of VaR values and range limits are important factors but we also need to tie them in with the confidence level of the VaR estimate and recalculate it for every additional period, shifting each trading day to the right. If we calculate that the confidence level of the VAR estimate is within the stable volatility range, we can use this security in further transactions within the range limits of critical volatility range limits, namely: within two to four sigma values depending on the volatility type that can be directed, non-directed, extended, narrowed or normal.

Watch out for our next posts, where we will put securities together in long-short pairs factoring in the confidence level of VAR estimate and the designated critical volatility range based on Sigma value.

Terms of use

We process information about your visit using cookies in order to improve our website.
By continuing to browse, you agree to our privacy policy.

Agree Terms of Use