Test 8: IV

This week we are going to look at the implications that implied volatility has on iron condor trades. Can the implied volatility be a good indicator on when or where to enter a trade?

To start with, lets look at the implied volatility of the ATM options for the RUT for the first three month expirations over the last 9 years:

The front month is definitely more volatile as we would expect, but the 2nd and 3rd months look to track one another closely. We can see that the minimum is around 15 and the max is in the neighborhood of 70. Note that I am calculating the implied volatility of the option chain as a weighted-average of the ATM option’s IVs.

These tests will using the following IV thresholds:

0.10, 0.125, 0.15, 0.175, 0.20, 0.225, 0.25, 0.30, 0.40, 0.5, 0.6, 0.7

Note as the graph shows, we should not expect any trades with an IV under 0.10. Using this value serves as a sanity check on the results.

IVs Greater than Threshold

The first test run uses the IV as a minimum threshold to entering a trade. For examples, trades with a minimum IV of 25 would not likely enter a trade when this article was written (IV is around 16).

The Top row are the IVs.

The first thing to note is that trades requiring an IV above 30 are very hard to come by.  Trades over 30 occurred less than 14 times in the last 10 years. The data show above for these high vol trades are interesting but cannot be counted on due to the very low population size. But it is interesting to note that there may be an advantage for low delta trades has extremely high IVs over high delta trades.

In general I would say that there is marginal effect when using IV as the sole entry criteria until the IV starts getting north of 22.5. Between an IV of 22.5 and 30 there is a significant sample size and the IV threshold, especially at lower deltas, seems to benefit the trade.

Expected Returns over Minimum IV

Looking at a graph of the expected returns based on using a minimum IV threshold we can see a couple patterns. First, for the extremely high IV trades (which are very rare), the low delta Front Month trades perform very well, as do all the deltas in the 3rd month. But again, this is based on a very small sample size.

Second, many traders often note that condor trades when the IV is under ‘X’ are not worthwhile. As I write this, the VIX and RVX are at extreme lows, and this comment is a frequently shared sentiment. But according to this data, it doesn’t really stand up. The low IV trades do well. But we have to wait for a few more tests to be sure their effect is due to the low IV trades and not being propped up by other high-IV trades.

IVs Less Than Threshold

Now lets look at the reverse situation  What if we place an upper threshold on the entry IV of a trade.

Improvement in Profit Factor by maximum IV levels (IV in columns)

Now we can see that the very low IV trades were much harder to come by except for the Front Month. Setting a maximum IV threshold may be a detriment to high delta Front Month trades, but largely the effect is marginal. The Front Month, low IV trades (i.e under 18) appear to perform better than the baseline which provides evidence that there may be good IC trades in low volatility environments.

Most of the trades in the second month have marginal effect. But the Third month appears to have non-marginal improvements in trades with IV less than 20.

IV Between Thresholds

Now we can look at combine a lower and upper threshold and see if any patterns emerge.

Improvement in Profit Factor between Bounded IV ranges

If you were to only trade within a specific range, lower IV environments would actually give you a boost. There are also some good scenarios in the Front Month with IVs between 25 and 50. If you only want to trade in the very high IV ranges, you will be waiting a long time to initiate a trade, but you are also best advised to use the Back Month trades. From this table we can see the largest improvements are:

• IV between 13 and 18: Lower Delta in any Month.
• IV between 18 and 20: Very Low delta front month trades, or lower delta trades in third month.
• IV between 20 and 30: There is no clear winner. Most trades look marginally impacted.
• IV between 30 and 50: Low Delta Front Month
• IV over 50:  Third Month trades

So… can we use this to help pick better positions.

Expected Return with bounded IV ranges.

Above is a heatmap of the expected returns of all the trades with bounded implied volatilities. Those trades with very low number of trades are circled in red. Each column is colored according the the best and worst performers for that IV range.

Note the trend in the back months: high delta trades perform worse and worse as the IV range is increased, barring extreme levels.

Today, the IVs of the RUT are 15.3, 16.6 and 17.9 as of this writing. They all fall squarely in the second column. According to this data, it may be most advantageous to enter a Front-Month trade with Deltas between 15 and 22, or a back month trade with deltas below 22.

To me, this sort of dispels the belief that there are not good returns in low volatility environments. In fact according to this data, the front month trades in the 13 to 20 delta range out performed those in 20 to 25. Quite the opposite of what I understand many to believe. BUT, these trades are all held to expiration, and that is generally not practiced…

Conclusion

Low IV environments may not be as bad as many seem to claim. In fact the Mid-IV range has historically shown to perform slightly worse. But in either case, IV can be a very useful tool in selecting which month and which strikes to enter.

Believe it or not, this test took me quite a while. I thought it was going to be trivial to implement, but I kept finding interesting nuggets to investigate (as well as a lot of refactoring to my codebase). But the effort is worth it and I can’t wait to test these concepts across a broader base of instruments.

Test Files:

Minimum IV Data Results

Maximum IV Data Results

Bounded IV Data Results

Test 7: IV/HV

I thought it would be fun today to test whether it makes sense to use implied volatility as an entry criteria to our tests. Specifically, I am looking at whether the ratio of IV to HV (historic vol) will be beneficial.

I’ve heard that generally speaking it is a bad practice to sell premium when the HV is larger than the IV. We like to sell in high IV environments with less underlying movement (actually that is ideal). When IV is lower than HV, can we get enough premium to justify the potential large swings in the underlying? We are actually under that situation right now with the VIX at 13.49.

For this test, I construct rules that only allow trades to be entered when the IV/HV ratio is greater than (1.0, 1.1, 1.25, 1.5, 1.75, and 2.0). No other entry or exit parameters are used. Note that in this test I am only using the implied volatility of the front-month option chain calculated based on a weighted average of the ATM call and put IVs.

Results

1 Month 2 Months 3 Months All
Improved PF 9(17%) 38(70%) 38(70%) 85(52%)
Improved MaxDD 54(100%) 54(100%) 54(100%) 162(100%)
Improved ExpRet 9(17%) 38(70%) 38(70%) 85(52%)

The initial pass is interesting; the back months show a much higher benefit that the front month. The heatmap shows this in even more detail:

The obvious conclusion is that if you find yourself with a 2x IV over HV, make the trade, but the data shows these are extremely rare (i.e. 6 trades in 9 years). Discounting these trades, we can see that generally speaking in the other cases using IV/HV as a marginal impact.

There are several trades that really stand out to me, all in the 2nd month. The 10-Delta, 15-Delta, and 18-Delta trades with IV/HV of 1.25 and 1.5 all saw at least 25 trades during this period and had substantial improvements. The 10-Delta 1.5 IV saw only winners. What is so special about this cluster of trades? Digging into the specific trades they show that they only have a few losses, but their neighbors have about the same number of trades and just 1 or 2 more losses. Those extra losses were enough to dramatically skew the results considering this is an expiration test and the losses were all max losses.

Now, what happens if we also include the early exit trigger from the previous test?

In this case we start to see pretty consistent and significant improvements in the back month trades. These are also fairly significant improvements over the early exit tests themselves. And note the cluster in the second month trades is no longer as significant.

Conclusion

On its own, the IV/HV ratio is not a great indicator to help enter trades. While there are scenarios where the ratio seems to help a great deal, these come at the cost of significantly less trades. However, it seems that paired with other triggers it may be more beneficial than not.

The higher the IV/HV ratio, the less number of trades that are going to occur. Is it worth it to wait until the IV is greater than the HV, in my opinion… yes if you are looking at back months. I am actually quite surprised that there was not a more dramatic effect for the front month since this test was using the front-month IV in its calculation. In the last 9 years there are only a few cases where the IV/HV ratio was less than one (i.e. HV greater than IV), and only one period where the HV was higher for sustained period of time.

Overall, I’m not convinced this tool is critical unless one is already in a low vol environment. If the HV is high, but the IV is also high, we would expect to be paid a decent premium and be able to get wider strikes, possibly nullifying the effect.

It is also interesting to note that the HV is a lagging indicator and the IV is a current sentiment indicator based on the market’s perception. Realized volatility in the past may not carry though to the time when HV is greater than IV. This would result in potentially missing trades good trades, and possibly allowing some bad ones.

Test Stats

 Permutations 162 Profitable Scenarios 121 75% Total Trades 7283 Profitable Trades 5212 72% Expired 2442 34% MaxDays 4841 66% Test Duration 1.38(min)

Excel Data

Test 6: Early Exits

Today I am going to test early exits. To date, my tests have held trades through to expiration unless a target or stop loss was reached. But many traders prefer to exit trades well before the expiration to minimize gamma risk. Of course, one would miss out on the largest theta decay, but theta and gamma often are opposing greeks.

The general sense I get is that those entering front month trades attempt to hold though to expiration, possibly exiting with 1 week left. Those entering the back months tend to be out of the trade with at least 3 weeks left to go. This run will test exiting 1 through 4 weeks prior to expiration, with no other entry or exit parameters. With this test, I am simply hoping to find a best practice depending on the expiration month.

Results

1 Month 2 Months 3 Months All
Improved PF 18(50%) 35(97%) 28(78%) 81(75%)
Improved MaxDD 36(100%) 36(100%) 36(100%) 108(100%)
Improved ExpRet 12(33%) 24(67%) 26(72%) 62(57%)

The table shows that where were substantial improvements in the Profit Factor for the back month. In all cases the risk was reduced, but in the majority of cases in the back months the expected returns were also improved.

Looking at a heatmap for the improvement in profit factor over the baselines:

We can wee that front month actually saw an improvement when the trades were exited 7 days prior to expiration. And very high delta trades may also see an improvement at all exit periods. However most other trades saw marginal or negative improvements as we might expect from these trades.

The back month trades are another matter. Clearly exiting early has a benefit on nearly all these trades. Exiting 3 weeks prior seems to be a general sweet spot for both back month trades, however, the 3-month 10-Delta trade saw the largest improvement when held to the last week.

So there is something to exiting early. Lets see if we can nail down some better rules. I reran the test, but this time included all days between 3 and 28 prior to expiration.

With this heatmap I removed all the weekend days and then color coded each row individually. Green reflects the most improvement and red the worst by delta. Red does not necessarily mean bad as any value over zero is an improvement over the baseline. The aim is to show the best exit point for each expiration month and delta. For example, if you were to enter a trade with the front month and a 30 delta, an exit with 21 days remaining may actually be in your interest.

• Exiting 22 days for the 3 month trade is generally optimal except for the delta-10 trades where holding to 4 days before seems to be beneficial. This is very anomalous.
• Exiting between 17 and 22 days for the 2 month trades has the highest benefit.
• Exiting early in the front month generally has marginal to negative improvement. Low delta trades may benefit from exiting just prior to expiration. Otherwise they are harmed. Higher delta trades show a marginal benefit to exiting early at most points with the largest gain being 17-22 days prior (note these would be very short term trades).
• Exiting 2 weeks prior on the Friday seems to perform much worse than if you were to exit a few days before or after that point. Why two weeks before? This pattern is not seen at 3 or 4 weeks on fridays.

Now, why is there an abnormality around the 3-Month, 10-Delta, Day-4 exit trade? It performs significantly better compared to its neighboring trades. The neighboring trades on the same delta have identical entry points and the same number of trades. However, when exited one day earlier (day 5) the scenario wound up with 4 more losses. Similarly, the neighbor in the 15-delta row had a few more total trades, but also experienced 4 more losses. Given that many of the trades were identical and that just exiting one day vs another made such a difference in 5% of the trades, I suspect this is just a statistical anomaly that would be smoothed out over time with more trades/data.

Conclusion

Clearly there is a benefit to exit back-month trades earlier. It substantially decreases the risk of the trade, and in many cases leads to higher expected returns probably by missing out on larger losses. Exiting between 18 and 22 days appears to lead to the largest benefit.

For most of my tests going forward unless noted otherwise, I exit trades for back month trades based on this data.

Test Stats

 Permutations 702 Profitable Scenarios 584 83% Total Trades 141185 Profitable Trades 91969 65% MaxDays 141185 100% Test Duration 1.56(min)

Excel Data