Lesson Objective: Understanding Vega in options trading
“Vega” (ν) represents the sensitivity of an option’s value to changes in the volatility of the underlying. Specifically, it measures the amount of change in an option’s value in response to a 1% change in the volatility of the underlying (all else being equal).
Vega can be any value, either positive or negative. All options (both calls and puts) will gain value with rising volatility, and vice versa. For instance, if Vega is 10, then the value of the option will move by +$10 if the volatility of the underlying rises by 1%. Long options have positive Vega while short options have negative Vega because long option traders benefit from option contract prices (premiums) being bid up and short option traders benefit from option contract prices being bid down.
Like any other “Greeks”, Vega changes frequently and rapidly for liquid options but most significantly when there are large price movements in the underlying. Vega is usually at its highest for ATM options because these options are more sensitive to changes in the volatility of the underlying. Vega is also directly correlated to the time left until expiration: Vega will tend to be much more significant for far-dated options than for near-dated options. This makes sense since the option will have more time for significant price movements if the expiration is far away.
As you may recall from our lesson on the pricing of options (lesson 10), volatility is the “plug” that will force the price of an option, as calculated by models, to match the price of the option resulting from the market force of supply and demand (at least for most underlyings, with highly illiquid assets being the exception potentially).
In other words, volatility reflects the market sentiment on a specific underlying. If demand for an option (whether a put or a call) is high (in comparison to supply), then volatility will increase and the price of the option will tend to rise. Conversely, if demand is low, then volatility will decrease and the price of the option will tend to decline.
However, in most cases, volatility will increase more when the price of an underlying drops than it will decrease when the price of the underlying surges. This is mostly due to two factors: (1) the overall net long positioning of large institutions and asset managers who purchase puts in order to protect their portfolio from drops in either the market or specific underlyings , and (2) the omnipresent fear factor of a black swan event that market participants, whether retailers or professional traders, find difficult to price or estimate which results in a skew towards put contracts for most equity and ETF underlyings.
As discussed in previous lessons, implied volatility has historically been overstated for most liquid underlyings in the past decade or so, which is a reason why sellers of options usually have an edge.
It is essential for a trader to understand the effect that volatility could have on an option’s price as expressed through Vega. More often than not, traders do not check Vega prior to entering a trade and may be taken by surprise.
For instance, you like stock XYZ currently at $100 and believe it will increase in value soon. Therefore, you decide to buy an at-the-money long-term call option on XYZ so, Delta is 0.5 (or 50). However, you fail to check Vega and it happens that implied volatility is quite high at the time of purchase due to an upcoming announcement of which you were not aware (Vega at 1.5). Right after you purchase the option, the announcement is made and XYZ moves to $106 but volatility drops significantly by 6%. What is the overall impact of the announcement on the price of your option (assuming the changes in time and interest rate are not significant)?
Well, given a 50% Delta, the increase in price of XYZ means a $3 increase in the option’s price due to the Delta effect (50%x (106-100)). However, this increase is more than offset by the decrease in volatility: 1.5 x (-6) = -$9. So, although you were right on the direction (the underlying increased in value significantly, e.g., by 6% in a short time frame), this was not enough to compensate for the magnitude of change in volatility as measured by Vega.
This example might seem extreme but similar situations happen frequently, hence the need and importance of understanding Vega and its significance.