Market makers quote two-sided markets to provide liquidity and depth to the markets. Market makers can delta hedge to reduce any directional risk that they may have. As markets move, the market makers' hedging requirements can shift, resulting in them adjusting their risk exposure by trading the underlying.
In options trading, Delta Hedging can be defined as the process of reducing or hedging directional risk associated with price changes in the underlying.
To understand structural flows, one must understand what good liquidity is. Good Liquidity can be defined by tight spreads and abundant quotes on both sides of the order book. In markets where liquidity is bountiful, market makers have less trouble reducing risk exposure through delta hedging.
It’s important to note that implied volatility directly reflects market liquidity. When the market is liquid, large orders have less of an impact as the order book can more easily absorb the large orders. It is difficult to move a liquid market. In these conditions, there will be lower realized volatility and, therefore, lower implied volatility. However, when liquidity is low or one-sided, the order book will be thin, which means any large trade can more easily move the market. Consequently, this activity results in higher realized volatility and, therefore, higher implied volatility.
Now that market liquidity as it relates to implied volatility is understood let’s delve into the relationship between Gamma and Delta Hedging. The amount of delta-hedging necessary to reduce risk is dependent on important variables, one of which is Gamma.
Exploring the relationship between Gamma and Delta Hedging
The following three variables are used to judge when an option’s delta will change:
Underlying Price Changes
Changes in Implied Volatility
The passage of time.
Gamma is the change in delta with respect to the underlying price.
Vanna is the change in delta with respect to the implied volatility.
Charm is the change in delta with respect to the passage of time.
This group of Greeks (Gamma, Vanna, Charm) are second-order Greeks which measure the sensitivity of first-order Greeks to changes in factors of the underlying. Arguably the most important and best-known second-order Greek is Gamma. In this article, we will focus primarily on Gamma as it relates to delta hedging.
Gamma is important because it allows us to read important structural flows in the market. Using Gamma, traders can see the potential delta-hedging activity by market makers.
Long Gamma vs. Short Gamma
Long gamma and short gamma are used to describe positions that participants take.
When market makers and dealers are long gamma, it means they have positive gamma exposure. A position that is long gamma will have a delta that increases when the underlying increases and a delta that decreases when the underlying decreases. To be long gamma, a trader can buy options (either calls or puts). When market makers and dealers are long gamma, they hedge risk exposure by selling when the market rallies and buying when the market drops.
When market makers and dealers are short gamma, it means they have negative gamma exposure. A position that is short gamma will have a delta that decreases when the underlying increases and a delta that increases when the underlying decreases. To be short gamma, a trader can sell options (either calls or puts). When market makers and dealers are short gamma, they hedge risk exposure by buying when the market rallies and selling when the market drops.
If market makers and dealers are “long gamma,” they are buying price decreases and selling price increases, which acts as a stabilizing factor on the market. If the gamma exposure is significant, such as around areas of large open interest, it works as a magnet and pins the underlying around a certain price range, also effectively increasing liquidity and reducing volatility. As OpEx approaches, the gamma for options increases significantly and can further dampen moves. If market makers and dealers are “short gamma,” they are buying price increases and selling price decreases, which can further intensify moves in the underlying. As a result, liquidity is removed, and volatility is elevated.
Using the Net Gamma Exposure tool on the Quant Data platform, we can read the gamma positioning of the market in real-time to get a better understanding of important structural flows that impact the market.
Where can I access the Gamma Exposure tools?
To access the Gamma Exposure tools, you’ll want to click the “Options Exposure” button on the sidebar to the left of the Quant Data platform. After doing so, you will see the following page:
On this page, you will be able to see Gamma Exposure, Vanna Exposure, & Delta Exposure. The data updates in real time with an approximate refresh rate of 5 seconds. By default, the Gamma Exposure tools will be displayed. By clicking the dropdown that says “Gamma,” you can change the tools to display Delta or Vanna data.
You can see each of the three exposure types by:
- Total Exposure by strike per 1% move or $1 move (Tool on the top left).
- Net Exposure by strike per 1% move or $1 move (Tool on the top right).
- Total Exposure by expiration per 1% move or $1 move (Tool on the bottom left).
- Net Exposure by expiration per 1% move or $1 move (Tool on the bottom right).
Using the tools, you can see the exposure of any optionable ticker, and you have the ability to filter by any expiration date, including 0DTE. Additionally, you can view the total exposure across all strikes and expirations.
What factors affect Gamma?
The impact Gamma has is dependent on different factors such as open interest, time to expiry, liquidity, and volatility. Understanding how these different factors affect Gamma is essential in understanding how to use it to read structural flows.
The more abundant the open interest is at a particular strike that has a near-term expiration date, the more likely it is that the Gamma Exposure will have an impact on the market. When there is a significant amount of open interest, it indicates that there is an elevated interest in traders holding positions in the underlying asset, which leads to more activity and more exposure.
At the individual contract level, the gamma is highest for at-the-money options, and the gamma decreases as the option moves further ITM or OTM. Gamma increases as the contract nears its expiration date, especially for at-the-money options. This means that as the expiration date nears, its delta becomes more sensitive to underlying price changes.
The relative strength of the impact that gamma exposure will have is affected by the liquidity of the market. In highly liquid environments, the market is more likely to absorb any structural flows related to delta-hedging and therefore has less of an impact. However, in low-liquid environments, the delta-hedging flows become more noticeable and tend to have a higher impact. Additionally, in environments where volatility is elevated, delta becomes less receptive to price changes in the underlying asset.
How do I use the Gamma Exposure tool?
Now that gamma is understood at the fundamental level, let’s explore how the gamma exposure tools on the Quant Data platform can actually be used.
It’s evident that different factors affect the potency of gamma exposure on the market. There are different ways that individuals visualize, calculate, and read gamma exposure. In the following paragraphs, I will outline what our community looks for as well as what we’ve observed that works well. Following this explanation will be case studies along with some things to keep in mind.
In the previous section, I explained how elevated interest at a particular expiration is important. Using the Net Options Exposure by Expiration tool on the platform, we can identify the expiration dates with the greatest net exposure. In the image below, you can see an example:
In the image above, the most significant exposure is at the short-dated expiries, with the daily expiration holding the greatest net gamma exposure. Most of the time, the nearest expiration will hold the greatest amount of exposure. Once the expiration date of interest has been identified, we can then navigate to the Net Gamma Exposure by Strikes tool and filter by the expiration date of interest to see the strikes with the greatest net call/put exposure. In the picture below, you can see that the Net Gamma Exposure tool has been filtered by the daily expiration:
Using the tool above, users can identify the strikes or groups of strikes with the greatest gamma exposure. The tool dynamically updates as new trades are being made in the market, so it functions as a great tool for analyzing the live options activity. As Call exposure increases on a particular strike, the underlying tends to gravitate towards the strike with the greatest exposure. The strike acts as a supply zone on the underlying. If there is a group of strikes with elevated exposure, the price tends to pin that group. On the contrary, when Put exposure is elevated, it acts as a demand zone as the price of the underlying accelerates downward. In situations where the market is trending sideways, and exposure is picking up drastically in a particular direction, the price tends to gravitate toward the direction with the greatest exposure.
As I previously described, there are different ways to calculate, visualize, and read gamma exposure. In the following section, I will go through several different case studies demonstrating on real data how the aforementioned method of using the gamma exposure tools function in greater detail.
Case Study #1:
The image above was posted to Twitter at 11:58 AM ET on April 26th, 2023. SPY was trading at approximately $407.22. The Net Gamma Exposure tool was filtered by the 0DTE expiration. It’s evident that the $405 strike had elevated Put Exposure. Additionally, in the tweet, I mentioned that the $405 exposure had been increasing since that morning. Less than 20 minutes later, SPY began its reversal to the low of the day. As time went on, the put exposure continued to increase at and around the $405 strike. In the image below, you can see the price action at the time of the trade. The blue arrow indicates the time that the Gamma Exposure image above was at:
Case Study #2:
The image above was posted to Twitter at 11:51 AM ET on April 24th, 2023. SPY was trading at approximately $411.04. The Net Gamma Exposure tool was filtered by the 0DTE expiration. It’s evident that the $412-$413 strikes had elevated Call Exposure. Additionally, in the tweet, I mentioned that the exposure had been increasing at the $412-$413 strikes. Shortly after, SPY reversed to $412 and pinned between the $412-$413 strikes. As time went on, the Call exposure continued to increase on the $412-$413 strikes. It’s evident in this example that the exposure acted as a supply zone that price gravitated toward. In the image below, you can see the price action at the time of the trade. The blue arrow indicates the time that the Gamma Exposure image above was at:
Case Study #3
The image above was posted to Twitter at 1:14 PM ET on April 6th, 2023. SPY was trading at approximately $408.77. The Net Gamma Exposure tool was filtered by the 0DTE expiration. It’s evident that the $410 strike had elevated Call Exposure. I selected this example because SPY was consolidating in the early afternoon. As the price was consolidating, GEX was building at $410. Shortly after, SPY moved to just under $410 before rejecting back to the zone where it was consolidating. This case provided a clear example of price gravitating toward and rejecting the strike with the largest exposure. In the image below, you can see the price action at the time of the trade. The blue arrow indicates the time that the Gamma Exposure image above was at:
Things to keep in mind
Differing Liquidity environments can strengthen or dampen the effect of delta hedging on the market.
Large and elevated exposure at groups of strikes is more likely to be significant than an individual strike.
There are different ways to interpret Gamma Exposure and different strategies that it can be used for.
Time to expiry, volatility, and open interest impact gamma.
Gamma is one of the second-order greeks. There are other second-order greeks, such as vanna and charm, which can also be used to read structural flows in the market.
It's never suggested to rely solely on one tool for making trading decisions. Tools should be used in confluence with each other.
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