Complex Volatility
Volatility is simultaneously one of the essential concepts for options trading and one of the most misunderstood.
You can hardly turn on investment news without hearing about volatility; whether about the CBOE VIX index, options volatility, or overall Market volatility.
As it pertains to options trading, volatility, specifically implied volatility, is one of seven factors in the options contract’s price. However, it is the one variable that is unknown and is responsible for most of the price changes with a particular contract.
As always, our goal here at Lantern is to demystify the options trading market and provide actionable education to our users on to optimize their options trades the most.
Today, let’s dive into the volatility and how it impacts our Trading strategies.
Volatility Defined
At its core, volatility represents how much a Securities price can move in any particular direction. Specifically, it is a mathematical representation of the dispersion of the Securities returns.
This means for Traders that the higher the volatility of an options contract, the riskier of an investment it can be.
The positive correlation between risk and volatility is that the higher the volatility, the wider the potential prices and returns can be for a given contract.
Remember that we used the option Greek letter Vega to denote the price change of an option for every 1% change in volatility for the underlying security. Options with higher Vega are more susceptible to price changes due to volatility.
Implied Volatility Vs. Historical Volatility
Now that we have discussed the basics of investment volatility, it’s time to unpack how volatility directly affects your options trading and trading strategies.
The first component of options volatility is determining whether you are talking about historical volatility or implied volatility.
Historical Volatility
Historical volatility is a defined metric representing how volatility has affected individual securities over predetermined periods.
Historical volatility can be measured daily, weekly, monthly, or yearly, depending on what you are looking for. Historical volatility is an important metric because it measures the price changes of a particular security in the past so investors can see the realized impact of volatility on that security.
You may be wondering if historical volatility can predict future volatility. The answer to that question is similar to the old investment adage “past performance is not indicative of future results.” while past historical volatility does not guarantee how a security will respond in the future, it is possible to gain valuable insight for Hypothetical trading scenarios.
An example of utilizing historical volatility in future price analysis would be to say if XYZ stock had an average historical volatility of 30% over 90 days, and the historical volatility for the past ten days is 15%, that would be indicative of that stock trading at lower than normal volatility.
The key takeaway from historical volatility is that it is a useful metric to compare current prices and metrics against; however, since it is not forward-looking, it is not good to project future volatility.
Implied Volatility
Wild historical volatility is a set measurement that can only be seen in hindsight; implied volatility is the forward-looking estimation of future volatility in option prices.
Let’s look at the seven factors that make up an options price based on the Black-Scholes model. We will see that six out of the seven are readily available to any investor, but implied volatility is a Perpetual unknown.
- Current price of the underlying stock
- Strike price
- Option type
- Time to option expiration
- Risk-free interest rate
- Underlying stocks dividends
- Implied volatility
Since the implied volatility is impossible to predict perfectly, it is important to calculate volatility calculations with a grain of salt and margin of error.
Thankfully, certain supply-and-demand metrics can make implied volatility easier to read, And quarterly stock earnings are an excellent example.
Whether a company is performing well or poorly, some options typically experience a significant increase in implied volatility leading to the earnings announcement. Investors are trying to capitalize on the direction they think the stock will go after the earnings are announced.
Once the earnings are announced, the implied volatility of those options contracts typically subsides because the excitement and anticipation are over.
While implied volatility is not a fixed science, the most crucial component of options pricing is the Known Unknown that dictates how options prices react to various stimuli.
Experienced Traders will take data from the market and synthesize it with historical volatility to create estimations of where they think implied volatility will be at a given time in the future.
Volatility Surface
Since implied volatility can be a confusing topic, some traders rely on a three-dimensional representation of implied volatility called the volatility surface.
The volatility surface represents the relationship between the black-scholes model prediction of implied volatility and how accurate the equations assumptions were.
The x-axis of the volatility surface is the time to maturity for the option contract. The z-axis is the options strike price, and the y-axis is the options estimated implied volatility.
In a perfect world, The volatility surface would be flat since it would be perfectly in line with be model’s pricing strategy; however, since it is impossible to protect implied volatility truly, this is not the case.
In Practice
Over time, traders, have noticed some patterns within the volatility surface model. It has been observed that options with lower strike prices may have higher implied volatility than higher-priced contracts. This may be since it is easier to trade lower-priced contracts, but it is not definitive.
Another observation of the volatility surface is that as the option’s time to maturity reaches Infinity, overall volatility across various strike prices seems to converge at a constant level which appears to be an inverted volatility smile.
For example, options with much shorter maturities have a multiple of volatility compared to their longer-dated counterparts.
Trading Applications
Let’s look at a simple trading application and strategy for capitalizing on implied volatility.
Spreads
Spreads are accessible and fairly straightforward trades that allow Traders to profit and mitigate losses simultaneously.
Now the question is, how do we respond when implied volatility is rising and when implied volatility is falling. The answer is a combination of contract affordability and overall risk.
Credit
Traders often sell a credit spread when implied volatility is approximately 50% or higher. As implied volatility rises, option prices rise with it to reflect the increase in risk.
We capitalize on the potential price increase and keep the most of your premium with a credit spread because there is a smaller likelihood of the options expiring in the money.
Debit
Conversely, debit spreads are purchased by Traders when volatility is low, typically between 0% and 50%, because the contracts are much cheaper, and they are looking to capitalize on the appreciation of their options in anticipation of rising volatility.
Wrapping Up
Volatility is by its nature unpredictable and confusing. Still, the more you study different implied volatility components, the more it will make sense when you look at options chains, Trends we publish on Lantern, and much more.
It’s best to learn about volatility by applying and structuring your trades around observable metrics such as the option Greeks, volatility predictions, and comparing two historical volatility.
If you’re interested, feel free to drop me a DM: teklets
I’m also working on Lantern: We have an API here and an App here.
