‘Tis the Seasonal Volatility

Dmitry Pargamanik and Will McBride, the cofounders of Market Chameleon, join IBKR’s Jeff Praissman to discuss implied volatility and looking for and interpreting seasonal trends.

Summary – IBKR Podcasts Ep. 219

The following is a summary of a live audio recording and may contain errors in spelling or grammar. Although IBKR has edited for clarity no material changes have been made.

Jeff Praissman 

Hi, everyone. My name is Jeff Praissman with Interactive Brokers, and it’s my pleasure to welcome to the Interactive Brokers Podcast studio Market Chameleons, Dmitry Pargamanik and Will McBride. Hey, guys, how are you? 

Dmitry Pargamanik 

Hi, Jeff. Thanks for having— 

Will McBride 

—having us. 

Jeff Praissman 

Oh, my pleasure. It’s always great to have you guys swing by the podcast studio. You just wrapped up a great webinar on implied volatility seasonality, and now we’re lucky enough to have you in the studio to continue diving into that subject. So I kind of want to start off for our listeners—because some people may have seen the webinar, and some may not. Could you guys briefly define implied volatility, just to give everyone a base before we dive into seasonal implied volatility? 

Dmitry Pargamanik 

Yeah, so implied volatility is something that is derived from option prices. It’s not like a stock price that we can just observe. Implied volatility is derived using an option pricing model. We reference option prices, input them into the model, and come up with this implied volatility, which reflects what the options market expects in terms of future volatility. 

Because we have lots of different options, the question becomes, where do you get this implied volatility? We have different strikes, different expiration months, calls, puts, bids, and offers. So we can calculate many different implied volatilities because of all these options to reference. 

To create a benchmark that we can track—since we want to see how it changes over time—we use a standard. One such standard is the 30-day constant maturity option, which is like an industry benchmark. The one we were looking at is the 30-day constant maturity at-the-money option. 

This involves a formula, like the VIX, where we use a weighted average and reference expirations that straddle 30 days. Using this weighted average, we include the options surrounding the at-the-money level to create a 30-day constant maturity at-the-money benchmark. Essentially, this reflects what the options market is implying for the stock’s volatility over the next 30 days. 

Jeff Praissman 

A huge part of option pricing. Then can you tell listeners what you mean by seasonal implied volatility? Is it a subset of implied volatility? Is it different? What exactly do you mean by that? 

Dmitry Pargamanik 

Yeah, so when we look at implied volatility, let’s say we’re tracking the 30-day implied volatility, that volatility moves around—it doesn’t stay constant. It can go up or down. One analysis is to track it and observe if there are any seasonal patterns or behavior based on the calendar year. 

If we think of examples in our economy, there are things that exhibit seasonal patterns for good reason. For instance, if you look at beach house rentals, rents tend to be much higher during the summer months and then drop off in the winter. Year after year, this recurs because of higher demand in summer when it’s warm and lower demand in winter when it’s cold. 

Similarly, in implied volatility, we can overlay historical data and try to detect seasonal patterns. For example, how does implied volatility behave during summer months? Does it go up or down? What about around holidays like Christmas, Thanksgiving, or New Year’s? We can also track it around recurring events like earnings. Stocks with earnings tend to show distinct volatility movements heading into or out of earnings. 

So that’s the idea—analyzing historical data to identify implied volatility seasonality trends. 

Jeff Praissman 

I really like that beach house analogy because it makes a lot of sense. The rents go up when there’s high demand, similar to implied volatility increasing during earnings or other events. What time frame do you guys use for the benchmark, and why do you use that time frame? 

Dmitry Pargamanik 

That’s a good question. It often comes up: What data set are we using, and how far back do we go? For seasonality analysis, we can’t use just one year of data because that doesn’t point to any seasonality. Seasonality is about patterns that reoccur year over year, so we need to overlay data across multiple years. 

If we look at just two years, that’s not enough either. So we aim for a longer period. In our case, we go back 10 years for seasonality analysis. Now, you might ask, why not go back further? Markets are dynamic, and things change. If we go back 30 years, the data might no longer be relevant because companies, stocks, technologies, and trading systems have changed. 

So, going back 10 years gives us enough data to analyze while staying relevant. That’s why we chose this time frame. 

Jeff Praissman 

Another point is that some stocks didn’t even exist 10 years ago, or they didn’t have options. 

Dmitry Pargamanik 

Exactly, yeah. 

Jeff Praissman 

Or the company could have existed, but it may have pivoted to offer a completely different service or product. 

Dmitry Pargamanik 

Right, or even the makeup of the S&P 500 looks different now. 

Jeff Praissman 

How do you handle outliers? Let’s say one July out of ten had significantly higher or lower implied volatility than the others. What’s your approach? 

Dmitry Pargamanik 

That’s a great question. There’s a good example of this in our data. If you look at the last 10 years, you’ll see a big spike in March due to an outlier year—2020, when we had the coronavirus. There was a lot of uncertainty, and implied volatility spiked to 52. The previous high was only in the 20s, so it was a huge outlier. 

If you include that data, it looks like March always spikes, but that’s not a true seasonal trend—it’s due to that one outlier. To handle this, we compare the average implied volatility to the median. The median isn’t as affected by outliers, so if the average is much higher or lower, it signals an outlier in the data. We also visually inspect the data to identify any unusual spikes and understand the context. 

Jeff Praissman 

That makes sense. And for recurring events like FDA decisions in pharmaceutical stocks, I guess those outliers might actually be valid patterns. 

Dmitry Pargamanik 

Exactly. For events like that, which happen consistently, you’d want to include them as part of the analysis. 

Jeff Praissman 

And when you’re looking at seasonality, do you consider recency bias? For example, let’s say you’re analyzing stock ABC, and the implied volatility for the past three Augusts was much higher than the other seven. Is that something you guys consider? 

Dmitry Pargamanik 

Yeah, for sure. Once you identify a seasonal pattern, it’s useful to analyze how recent data compares to older data. For example, you can compare the most recent three years to the middle years or the oldest years in your dataset. One way to approach this is by using weighted averages, where you give more weight to recent data than to older data. 

On an initial pass, we typically give all data equal weight. But there’s no single right way to do this—it depends on the specific analysis. That said, weighting more recent data can help capture trends that are more relevant to current market conditions. 

Jeff Praissman 

Absolutely. And in our monthly conversations, we always try to emphasize why whatever we’re talking about is important to traders. So, why is tracking and referencing seasonal implied volatility important to traders? 

Dmitry Pargamanik 

It’s one factor in your overall analysis. It’s not the only factor, but it’s an important one. Knowing seasonal tendencies in implied volatility can help traders prepare for periods when volatility might move lower or higher. This can be valuable for risk management and identifying potential opportunities. 

For example, implied volatility tends to decline heading into Thanksgiving. If you know this pattern, you can factor it into your strategies. Similarly, during earnings season, implied volatility often spikes before earnings and drops after. Historical charts can provide a useful guide to the typical ranges of these spikes and drops, helping traders anticipate potential moves and manage their portfolios more effectively. 

Jeff Praissman 

What other data do you look at alongside implied volatility? We’re not operating in a vacuum, so what else is helpful? 

Dmitry Pargamanik 

When analyzing implied volatility seasonality, we like to connect it with other related data. For instance, we might overlay it with seasonal trading volume. Is there a correlation between trading volume and implied volatility? Does a drop in trading volume correlate with lower implied volatility, or vice versa? 

Another factor is realized volatility, which measures the actual volatility of the underlying stock. Comparing implied volatility to realized volatility can reveal whether the market tends to overestimate or underestimate future volatility. This can be particularly useful for strategies like trading implied volatility versus realized volatility in a Black-Scholes framework. 

By analyzing these relationships, we gain a more comprehensive view of how implied volatility behaves relative to other market factors. 

Jeff Praissman 

And I imagine news cycles play a role too, right? Things like earnings, economic indicators, or other major events? 

Dmitry Pargamanik 

Absolutely. News cycles can have a big impact, especially during specific times of the year. For instance, Fed meetings or other economic events might cause implied volatility to spike. Identifying these patterns and understanding the reasons behind them is crucial. It’s not enough to observe that volatility tends to rise or fall—you need to validate the reasoning. 

For example, we talked earlier about beach house rents rising in summer. That makes sense because demand increases. Similarly, if implied volatility tends to rise before earnings or during a Fed meeting, you want to understand the underlying cause and whether you expect it to happen again in the future. 

Jeff Praissman 

Circling back to my earlier question about why tracking seasonal implied volatility is important, I’d like to dig a bit deeper. What are some of the strategic applications for traders? 

Dmitry Pargamanik 

Seasonal patterns can inform strategies in several ways. One is by analyzing the term structure of implied volatility—looking at implied volatility across different expirations, from short-term to long-term. Comparing the term structure to historical seasonal patterns can help identify potential mispricings or opportunities. 

For traders, it’s also a way to manage risk. If you have a portfolio with long vega exposure, knowing that implied volatility tends to decline in summer can help you assess your risk. For instance, if implied volatility drops by 10–20%, how would that impact your portfolio? Is your risk acceptable? 

In addition to finding opportunities, seasonal analysis helps traders anticipate and manage potential risks more effectively. 

Jeff Praissman 

With everything involved in trading, there’s always risk to consider. How do you use seasonal implied volatility as a research tool? 

Dmitry Pargamanik 

Seasonal analysis is based on historical data, so it’s a guidance tool. However, you have to consider the current environment. For example, during the coronavirus pandemic, the market environment was unprecedented, and historical seasonal patterns weren’t valid. 

Seasonal patterns provide a framework for understanding historical behavior and ranges, but markets are dynamic. They change constantly, so you have to adapt and factor in current events. History is a guide—it’s not a guarantee. 

Jeff Praissman 

This was great. Any final thoughts, Dmitry, you’d like to leave the listeners with? 

Dmitry Pargamanik 

When doing research and analyzing your options trading portfolio, seasonal implied volatility is a valuable tool. It can reveal patterns or risks you might not have considered. If markets behave differently than historical seasonal patterns suggest, that divergence could signal something important worth investigating further. 

Overall, it’s a useful tool for quick reference, especially when trading strategies like calendar spreads or time spreads. It can help provide additional context for your decision-making. 

Jeff Praissman 

Well, this was a great way to kick off 2025 with our first podcast of the year. Every month, we’re lucky to have you guys join us. For our listeners, you can find this podcast on our website, Spotify, Apple Music, Amazon, and all the usual platforms. Looking forward to next month. Thanks again, guys! 

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