How the options market informs investment decisions in the spot market: Analyzing options volumes and the vol smile (pt 1)
Analyzing options volumes and the vol smile in Python
While we frequently hear of articles and discussions like these suggesting price targets, one major issue of such recommendations is that they are simply recommendations.
Those giving the recommendations need not be held responsible for their predictions. They can simply pluck figures out of thin air and quote whatever they wish since they do not have any skin in the game.
Skin in the game.
Or in the language of financial markets, have capital committed.
Is there a way to see the extent that the market believes in a bullish or bearish trend enough to commit capital?
Why care about the options market of spot assets
The trading of options is highly restricted in many legal jurisdictions, due to their volatile and risky nature.
Even if you live in a country where you are forbidden from trading options directly, there is merit in understanding how they function.
As an indicator of price, I am of the opinion that options activity represents the true sentiments behind the behaviour of an asset.
After all, analysts can debate til the cows come home over what the fair value of an asset should theoretically be. What is arguably more important is what the overall market as a whole believes what the value of the asset may be.
Aren’t options highly complex and mathematical in nature?
The answer to that question is a resounding yes. However, there are also many layers of understanding of varying levels of complexity.
For the purpose of this article, we only require the readers to understand the math to a superficial extent.
For this analysis, we shall be examining 2 indicators of an options chain.
Volume and the volatility smile.
Code
We shall execute a short python script to extract the equity options chain for Apple calls expiring on 26 Jan 2024.
Quick disclaimer:
As of today, options data sources are highly fragmented and generally require premium data sources with paid subscriptions.
If you are using free data sources, do be prepared for the possibility that the data has missing entries and is outdated. While such data is fine for historical analysis, please refrain from using it for live trading.
import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
#Enter security ticker
ticker = "AAPL"
aapl = yf.Ticker(ticker)
#Enter expiration date in YYYY-MM-DD format
expiration = '2024-01-26'
option_type = "calls"
#Extract calls from Options Chain
calls_df = pd.DataFrame(aapl.option_chain(expiration).
calls).fillna(0.0)
#Drop empty/problematic columns
#Limitation of free data sources => Lots of missing information.
calls_df = calls_df.drop(["bid",
"ask",
"change",
"percentChange",
"openInterest",
"inTheMoney",
"contractSize",
"currency"], axis=1)
print(calls_df)
x = calls_df["strike"]
y1 = calls_df["volume"]
y2 = calls_df["impliedVolatility"]
y3 = calls_df["lastPrice"]
figure, axis = plt.subplots(3, 1, sharex='col')
axis[0].bar(x, y1, label=f"{ticker} {expiration} {option_type}")
axis[0].legend(loc='upper right', prop={"size":14})
axis[0].set_title("Volume", fontsize=16)
axis[0].set_ylabel('Number of contracts')
axis[0].grid(True)
axis[1].plot(x, y2, "g")
axis[1].set_title("Implied Volatility", fontsize=16)
axis[1].set_ylabel('Measure of Vol')
axis[1].grid(True)
axis[2].plot(x, y3, "r")
axis[2].set_title("Last Transacted Price", fontsize=16)
axis[2].set_xlabel('Strike Prices ($)')
axis[2].set_ylabel('Option Premium ($)')
axis[2].grid(True)
plt.xticks(np.arange((x.min()), x.max(), 10))
plt.show()
Volatility Smile
“The primary driver of volatility skew is the collective expectations and behavior of market participants.
If investors expect a significant price movement in one direction, these investors may be willing to pay more for options that would profit from that move. This increased demand can drive up the IV of those options, creating a skew.”
When does a skew become a smile
For assets that exhibit a slow and steady uptrend, the IV for ITM calls should be extremely low as the collective expectation is that they will remain ITM.
The IV against strike graph would show a steady and upward trend.
However, if there is sudden bearish turn of events which causes sentiment to turn, we see the market react heavily.
Through plotting a simple graph of Implied Vol (IV) against strikes, we the familiar shape of the smile.
On 19 Dec there was overwhelming consensus that AAPL will trend higher, with a very low chance of falling significantly below its current levels.
On 23 Dec, the consensus shifted and there is fair chance that AAPL trades sideways or even falls slightly at the expiration date.
Options volume
At a glance, we can see that the volume of calls represent a slightly biased normal distribution centred around 195 strike. We see the greatest number contracts of around approximately 1400.
There appears to be a greater number of contracts bought for above 195 than below 195.
We can infer that to mean that the collective sentiment of the market holds. a view that AAPL is likely to be in the vicinity of 195. With a greater likelihood of being above 200.
Of all the contracts, the top 3 strikes with the greatest volumes are 195, 200 and 205.
Withe AAPL hovering at 193 at time of publication (23 Dec 2023), we can use this information to make a bullish bet that AAPL is likely to be between $195–$205 on 26 Jan 2024.
Part 2
For this article, we covered how to use an options chain to inform us how the potential price targets of assets in the spot market.
In the next article, we will cover how to utilize the Cumulative distribution function to calculate the probability that the spot asset and compare the profitability of between trading the spot asset and trading a Call Option Spread (Bull Call).
*No information contained here constitutes an investment recommendation. Options are inherently risky instruments, please do your due dilligence.
