In the last few years, the equity markets experienced large and frequent movements both higher and lower due to many macro events including the introduction of COVID in the winter and spring of 2020, the economic recovery in late 2020 and through 2021, supply chain issues, inflation, and the Federal Reserve increasing interest rate in 2022.
Introduction:
In a recent article, I asked, are we in a higher volatility regime? This article references a similar question from a VOLQ price distribution perspective and how the recent price distribution compares to past periods. The Nasdaq-100 Index (NDX) is the underlying equity index for the VOLQ volatility index.
This topic begs a simple but powerful question: What is VOLQ’s price distribution? It’s a simple question because it’s about recognizing the frequency of the VOLQ price distribution and how much of the distribution occurred before COVID and the distribution shape since the inception of COVID in 2020. It’s a powerful question, because examining the data may give some insight into VOLQ’s behavior in various environments, where VOLQ prices tend to live, and what VOLQ may experience in the near term depending on the economic environment.
In 2022 the equity markets trended lower with NDX experiencing a year-long drawdown of 35.5% (black dashed line) from a peak on November 19, 2021, to a recent bottom on November 3, 2022. Volatility indices usually rally due to the decline of their respective underlying equity index, also known as downside volatility. This current selloff of equities equates to a sustained higher level of volatility pricing as observed in several volatility indices including VOLQ. See figure 1.
Figure 1: Daily prices of NDX spot prices and VOLQ spot prices
Source: Bloomberg
Statistics:
Figure 2 shows the descriptive statistics from 2014 to 2022 in the first column, Time 1. The second column, Time 2 is before COVID. The third column, Time 3 starts about the time COVID began to appear in the United States. The data suggest distinct volatility price differences between Time 2 and Time 3.
Figure 2: Descriptive statistics of VOLQ’s daily prices
Source: Bloomberg
Figure 2 notes the Time 3 average, median, and minimum prices are almost twice the price of Time 2’s statistics. Time 3’s maximum price is a little more than twice Time 2’s maximum. This suggests that downside volatility has been sustained at an elevated level for almost three years.
The Time 2 histogram (figure 3) graphically demonstrates the VOLQ price frequency before COVID. The orange dash line aggregates the percentage of the distribution. For example, 91% of the price distribution falls below 23. The highest frequency of price ranges from 12 to 17 equating to 66% of the total price distribution falls between these two prices. Hence offering possible support and resistance levels. About 1.2% of the distribution occurred beyond 28. This suggests VOLQ’s closing prices during Time 2 were clustered in a small range.
On the other hand, the price range of Time 3 (Figure 4) between 12 and 17 is only 13.5% of the price distribution. To reach 66%, the price range expands to 27. This suggests Time 3 pricing distribution is flatter than Time 2, as 91% of the distribution falls below 36.
Price Distributions:
Figure 3: Time 2 price distribution
Source: Bloomberg
Some of the price distribution differences between Time 2 and Time 3 include Time 2 and Time 3 are positively skewed distributions at 1.4 and 1.63 respectively. Meaning Time 3 VOLQ prices stretch farther from the mean (more outliers from the mean). Hence, the Time 3 price distribution contains more variance and a wider dispersion of prices. The variance is also noted in their respective standard deviations of 4.19 (Time 2) versus 7.97 (Time 3).
Figure 4: Time 3 price distribution.
Source: Bloomberg
The Y axis in Figure 4 is a smaller scale making it easier to see the Time 3 price distribution.
Figure 5: Time 3 and Time 4 on the same scale.
Source: Bloomberg
The comparison of the price distributions of the two time periods is very noticeable in Figure 5. Time 2 data is clustered in a small price range versus Time 3’s VOLQ price distribution implies a flatter and wider price distribution, meaning there can be more variance of volatility pricing and a higher price level.
Summary:
The VOLQ price distribution before COVID tended to be clustered between 12 and 17. The period post-January 2020 is a flatter distribution with more outliers and sustaining a higher price. If the markets stay in a higher volatility regime, the Time 3 price distribution is a likely scenario of VOLQ prices for 2023.
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The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.
Mark Shore
Mark Shore, a well-known expert on alternative investments with over 30 years of experience in the capital markets. He is currently a Clinical Professor of Finance at DePaul University, Executive Director of the Arditti Center for Risk Management, and Chief Research Officer of Shore Capital Research.
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