Selecting Implied Volatility Pairs for Mean-Reversion Trading
Summary
The document explains how to identify candidate pairs for trading relative moves in equity-option implied volatility, even when the individual volatility series are already stationary. Cointegration is not essential: the relevant question is whether a spread or relationship between instruments is stable enough to support a mean-reversion strategy.
A simple screening method compares changes in implied volatility across instruments over a common horizon. It ranks pairs by a distance measure based on the squared correlation of their volatility changes, favoring pairs whose changes move together. Traders can then look for unusually wide or narrow volatility spreads and bet on a return toward the historical relationship. This is presented as a basic approach, not a complete trading system. The document provides no out-of-sample performance, transaction-cost analysis, risk controls, or evidence that the historical relationship will persist.
Key ideas
- Stationary implied volatility series can support mean-reversion trading without cointegration.
- Candidate pairs can be screened by comparing the correlation of their volatility changes.
- Pairs with highly correlated changes receive a smaller distance score in the suggested method.
- A trade bets that an unusually wide or narrow volatility spread will move back toward its historical level.
- The screening heuristic does not establish profitability or account for costs and risks.
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# How does Volatility Pairs Trading work?
# How does Volatility Pairs Trading work?
I've read some material related to pairs trading for equities and I understand the process of finding non-stationary pairs price series that can be cointegrated to form a stationary series. The basic idea being to trade on the oscillations about the equilibrium value of the spread. While i understand how this is accomplished with equities, i'm not sure how suitable implied volatility pairs on equity options are identified since both implied volatility series would already be stationary and so cointegration would not be required. Can someone please explain to how suitable implied volatility pairs are identified?
## Answer by James (score 5, accepted)
https://quant.stackexchange.com/a/14743
If you believe the process $Y_t$ to be stationary, you can try to profit from it via a mean-reversion strategy or any other way that exploits the stationarity. It doesn't matter whether $Y_t$ is obtained as a cointegrational combination of a few non-stationary processes, or as a linear combination of some processes that are stationary themselves.
In the early years of the so-called Statistical Arbitrage, they never even used the formal cointegration tests because they were not available at the time. The original simple idea was to pair "similar" equities and pick the pairs with the spread that was both "stable" and looked like it had some profit generating potential. I believe a similar approach is applied to the volatility pairs.
A (very) simplistic approach is as follows: take a bunch of volatility instruments and compute the implied volatility, $v_t$ over some horizon. Then for each instrument $i$ compute the volatility increments $\Delta_t^i = v_t - v_{t-1}$. For each pair of instruments $(i,j)$, compute the "distance" between them as $[1 - correlation^2(\Delta^i, \Delta^j)]$. The pairs with the smallest distance are the ones used for trading. For each pair, when the volatility spread becomes too wide/narrow compared to the historical average, you take a bet that it will narrow/widen in the future.
If you take a look at this well known early paper on StatArb and replace the term "stock price" with "implied volatility", you'll get a better idea.Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.