Using Vega to Assess Risk
Using Vega to Assess Risk
By:Josh Fabian
After beginning the year at an elevated level, volatility began contracting the second half of February and has hovered below $16 since mid-March. Over the past couple weeks, volatility has stabilized and is climbing higher. We know option premium becomes rich with higher volatility. Existing positions; however, may be at risk because of Vega exposure.
Vega is a theoretical measure of how the price of an option may react to a change in implied volatility. Platforms such as thinkorswim display Vega to indicate risk based on a one-point change in volatility. For example, assume a short put position in SPY with Vega of -10 and VIX at $12. Should VIX move up to $20, based on Vega we could expect a loss of $80 (($20 - $12) x -10).
Options with different strike prices will have different Vegas. We see that with all the Greeks. It makes sense, therefore, that options closest to being at-the-money have the most exposure to volatility. Changes in implied volatility will affect these options more so than options further out-of-the-money.
Selling premium is what tastylivers do. When we sell options, we become short Vega and take on negative volatility risk. That means increasing volatility causes the price of an option to increase. Since tastylivers are typically short options and short Vega, increasing volatility works against existing positions. Long option positions are long Vega and benefit from increasing volatility. This is why we sometimes see short calls increase in price, despite falling prices.
Vega is no different from any of the Greeks in that it is a theoretical. It’s intended to give an assessment, not an absolute. Understanding Vega allows us to understand risk relative to increasing volatility. We may be right in directional trades, but volatility and Vega may offset being right.
Josh Fabian has been trading futures and derivatives for more than 25 years.
For more on this topic see:
Options Jive: Options Risk | Volatility (Vega): February 22, 2016
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