VIX Weeklys options continue to garner more interest. Over the past week, I came across a trade that is pinpointing higher volatility sometime before the end of February. In a handful of lots someone sold 10,000 VIX Mar 1^{st} 12 Puts for 0.50 and then purchased the same number of VIX Mar 1^{st} 13 Calls for 0.75 and a net cost of 0.25. It was nice to see a sizable trade executed in that market as a confirmation of there being enough liquidity in the non-standard expirations to facilitate a large trade like this. The payout at March 1^{st} settlement appears below, but as always, I’m going to assume a volatility spike between now and then would result in the call side of this trade being exited.

Last week the S&P 500 was up just over 1.5% and VIX rose over 5%. More on that after the table below.

I ran some numbers and this is on the 14^{th} time since 1990 we have had both rise by this amount. The previous 13 had mixed results the following week. Honestly, I was hoping for something more significant. I guess for now it’s back to crunching numbers for me.

SVXY is now up over 40% for 2017, VXX has dropped over 30% and the leveraged UVXY has given up over 50% this year.

In addition to VXST and VIX there were a handful of other volatility indexes that rose last week. However, it was a mix of indexes that rose, EuroCurrency, AAPL, AMZN, and Brazil for example with no real pattern among the leaders.

The low for VXX this past week came around the open Wednesday as the long VIX focused ETN was trading around 16.80 to start the day. One astute and large trader came into the market buying a few thousand VXX Feb 17^{th} 17.00 Calls for 0.20 and selling an equal number of the VXX Feb 17^{th} 17.50 Calls for 0.09 and a net cost of 0.11. I liked this trade for two reasons, first we can see how the trade worked out since those options expired on Friday. Second, because we get to see an example where getting long exposure to VXX works.

I highlight where VXX was when the trade was executed and Friday’s close. This trade is a perfect example of a strategic option trade that worked out as well as it was drawn up.

]]>As I mentioned my assumption was wrong and the specific trade involved selling 15,000 VIX Mar 15 Call at 0.74 and buying 60,000 VIX Mar 22 Calls at 0.24 each for a net cost of 0.22 per 1 x 4 spread. This trade was executed when VIX was at 12.10 and the March contract was at 13.00.

At expiration, this trade would not make money unless VIX settlement comes in around 24.40, but these types of trades are usually implemented with the intent of trading out of them on any sort of volatility spike. I usually state that without including an example of what I’m talking about. Today that comes to an end with the payoff diagram below.

Note I highlight the price of the March contract when the trade was executed, but neglect to include VIX. That’s because the best pricing vehicle for the March options is the corresponding future and the curved line on the payoff diagram is priced off a futures price assumption. The specific assumptions behind the half way to expiration line is 10 trading days to expiration and no change in each VIX option IV (unrealistic, but probably understanding the real profit). Note on a volatility spike the break-even for this trade is closer to 20.00 and when the at expiration trade breaks even, this trade would have an unrealized profit of 5.50.

]]>The reason we believe in the three-day weekend effect stems from how VIX is calculated. VIX is a calendar day measure, so when we are approaching a day where the markets will be closed the result can be a bit of extra downside pressure for VIX. Once the markets get back to trading VIX gets a little tailwind.

First, I took all trading days for VIX from January 2, 1990 through February 10, 2017 and pulled out all days where there was no interference of a holiday. For instance, only looking at Monday performance when the previous trading day is a Friday and the following day is a Tuesday. Using these days, I tallied up the number of days VIX rose and how often it was down on the day. The results appear on the table below.

The first column is a sum of all non-holiday impacted trading days and the total number of days VIX was higher and lower. VIX is slightly more likely to drop in value on any one of these days than to rise. Anyone that follows VIX is probably not terribly surprised by this. VIX tends to spike quickly to the upside and grind lower. This is a nice quantification of something we all ‘know’ with respect to the behavior of VIX and implied volatility.

Note on non-holiday Mondays VIX is up 63% of the time and lower 36%. For those doing the quick math, there are a few unchanged days in there so the total is less than 100%. More importantly VIX rises on Monday after the impact of two non-trading days benefitting from that tailwind I mentioned above.

Finally, note that on non-holiday Fridays VIX is down 62% of these observations. That’s the result of that headwind I noted with respect to the market being closed for a couple of days.

My next test involved looking at long weekends. We can have three day weekends that run from Thursday to Monday or from Friday to Tuesday. I segmented both types of long weekends out for the data below. On the left side are data for the pre-holiday Thursday and Friday on the right side we see how VIX performs the first trading day after a long weekend.

Pre-holiday Thursdays see VIX rise 37% of trading days and 40% of pre-holiday Fridays experience higher VIX. These numbers were honestly surprising to me. Since they are basically in line with non-holiday Fridays. It may just be that there is no true three-day weekend effect, we are just seeing the normal end of week impact, but paying more attention to it because of the pending holiday.

Finally, Monday and Tuesday after long weekends tell a different story. 85% of post three-day weekend Mondays and 74% of Tuesdays have seen a higher VIX. This is higher than the 63% number associated with non-holiday Mondays. My theory on this is if there is an extra headwind going into long weekends it may kick in two days before the long weekend. For now, that’s a theory, but I’ll probably follow up with a little more digging into the data soon and definitely report back in this space.

]]>CBOE SKEW Index values, which are calculated from weighted strips of out-of-the-money S&P 500 options, rise to higher levels as investors become more fearful of a “black swan” event — an unexpected event of large magnitude and consequence. The value of SKEW increases with the expected tail risk of S&P 500 returns. If there were no tail risk expectations, SKEW would be equal to 100. Historically, SKEW has varied in a range of 100 to 147 around an average value of 115.

The FAQ on the CBOE SKEW Index notes that –

“The price of S&P 500 skewness is inconvenient to use directly as an index because it is typically a small negative number, for example -.8, -2.3, or -4.3. SKEW converts this price as follows: SKEW = 100 – 10 * price of skewness. With this definition, a price of -2.1 translates to a SKEW value of 121. S&P 500 options with 30 days to expiration are generally unavailable. SKEW is therefore interpolated from two “SKEW” values at the maturities of nearby and second nearby options with at least 8 days left to expiration.”

For more information please visit **www.cboe.com/SKEW**.

**Histograms and Profit-and-Loss Diagrams**

Tools that can be helpful to investors who are attempting to assess the utility of various options-based strategies include (1) histograms with analyses of monthly returns for several CBOE benchmark indexes, and (2) profit-and-loss diagrams. CBOE provides more than 30 strategy benchmark indexes that can help investors compare and contrast the hypothetical performance of different options strategies in different market scenarios. www.cboe.com/benchmarks.

Below are 11 histograms that compare past performance of CBOE option-related benchmark indexes and related stock indexes. These histograms can provide valuable information to investors who have high aversion to losses or a desire for more upside potential.

As shown in the 11 histograms below, the “best” big-loss-avoidance past performance by an index — in terms of avoiding monthly losses of 6% or more – was by the CBOE S&P 500 Iron Butterfly Index (BFLY). In the 30+ year period from July 1986 through January 2017, the number of months that indexes had loss of worse than 6% were –

**2**months for the CBOE S&P 500 Iron Butterfly Index (**BFLY**)**6**months for the CBOE S&P 500 95-110 Collar Index (**CLL**)**7**months for the CBOE S&P 500 Risk Reversal Index (**RXM**)**11**months for the CBOE S&P 500 PutWrite Index (**PUT**) (which writes at-the-money SPX put options)**12**months for the CBOE S&P 500 BuyWrite Index (**BXM**) (which writes at-the-money SPX call options)**19**months for the CBOE S&P 500 30-Delta BuyWrite Index (**BXMD**) (which writes out-of-the-money SPX call options)**26**months for the**S&P 500 Index**

The CBOE VIX Tail Hedge Index (VXTH) buys and holds S&P 500 stocks, and also often buys 30-delta call options on the CBOE Volatility Index® (VIX®).

The CBOE S&P 500 Iron Butterfly Index (**BFLY**) tracks the performance of a hypothetical option trading strategy that 1) sells a rolling monthly at-the-money (ATM) S&P 500 Index (SPX) put and call option; 2) buys a rolling monthly 5% out-of-the-money (OTM) SPX put and call option to reduce risk; and 3) holds a money market account invested in one-month Treasury bills, which is rebalanced on the option roll day and is designed to limit the downside return of the index. Compare the CBOE BFLY Index histogram above with the iron butterfly profit-and-loss diagram below. It appears that certain iron butterfly strategies could have the potential to lessen the probability of huge upside and downside moves.

The CBOE S&P 500 95-110 Collar Index (CLL) purchases stocks in the S&P 500 index, and each month sells SPX call options at 110% of the index value, and each quarter purchases SPX put options at 95% of the index value.

The CBOE S&P 500 Risk Reversal Index (RXM) is a benchmark index designed to track the performance of a hypothetical risk reversal strategy that: (1) buys a rolling out-of-the-money (delta ≈ 0.25) monthly SPX Call option; (2) sells a rolling out-of-the-money (delta ≈ – 0.25) monthly SPX Put option; and (3) holds a rolling money market account invested in one-month Treasury bills to cover the liability from the short SPX Put option position.

The CBOE S&P 500 Zero-Cost Put Spread Collar Index (**CLLZ**) tracks the performance of a hypothetical option trading strategy that 1) holds a long position indexed to the S&P 500 Index; 2) on a monthly basis buys a 2.5% – 5% S&P 500 Index (SPX) put option spread; and 3) sells a monthly out-of-the-money (OTM) SPX call option to cover the cost of the put spread.

The CBOE S&P 500 Iron Condor Index (**CNDR**) tracks the performance of a hypothetical option trading strategy that 1) sells a rolling monthly out-of-the-money (OTM) S&P 500 Index (SPX) put option (delta ≈ – 0.2) and a rolling monthly out-of-the-money (OTM) SPX call option (delta ≈ 0.2); 2) buys a rolling monthly OTM SPX put option (delta ≈ – 0.05) and a rolling monthly OTM SPX call option (delta ≈ 0.05) to reduce risk; and 3) holds a money market account invested in one-month Treasury bills, which is rebalanced on option roll days and is designed to limit the downside return of the index.

With the at-the-money (A-T-M) buy-write strategy, an investor often takes in more options premium, but has no participation in stocks’ upside moves, when compared with the out-of-the-money (O-T-M) buywrite strategy. Compare right and left tails for the **BXM** Index above versus the **BXMD** Index below.

**MORE INFORMATION**

A representatives of **Wilshire** will discuss CBOE Benchmark indexes and downside risk at the 33rd Annual **CBOE Risk Management Conference (RMC)** next month **www.cboermc.com**.

For additional information about the **CBOE benchmark indexes** and related **white papers** on portfolio management, please visit www.cboe.com/benchmarks.

More information on tail risk and histograms is at **www.cboe.com/histograms**.

#CBOEhistograms

]]>However, there are some pockets of volatility in the US equity markets, we just need to know where to look. Since CBOE quotes several volatility indexes that are based on the US markets I went searching for places where the market is still pricing in a little concern about the future. Two areas that stood out – tail risk and small cap risk. Let’s start with tail risk.

The CBOE SKEW index is a measure that takes out of the money put option volatility and compares it to the implied volatility of SPX put options that are not as far out of the money. If SKEW is equal to 100 then the out of the money IV is in line with that if SPX puts with strikes closer to the levels of the S&P 500. SKEW is typically around 120 or so and came in at 130 yesterday. I like looking at volatility measures relative to other volatility measures and the outcome for SKEW appears in the chart below. This chart shows the daily levels for SKEW divided by VIX going back to 1990. Note it rarely moves over 12, but that’s where it is right now. This may be read as out of the money stock market protection is expensive relative to the cost of at the money SPX options.

The second area of high volatility relates to small cap stocks in the US. CBOE quotes VIX, but we also have the CBOE Russell 2000 Volatility Index (RVX) which gives us a consistent measure of Russell 2000 (RUT) implied volatility like VIX does for the S&P 500. With a handful of exceptions RVX has always closed at a premium to VIX. However, it rarely is at more than a 60% premium to VIX like it is as I write this blog. We can take that as option premiums for RUT options are relatively expensive when compared to SPX options or that traders are more willing to pay up for small cap protection than large cap protection.

]]>Probably the most notable thing on the table below is the rise in VVIX despite the stock market and VIX not doing much last week.

Focusing on the VIX related ETPs shows that the trend of short volatility being the place to be in 2017 continues to hold up.

Looking outside the broad-based index volatility space, there were a lot of gainers despite not a lot going on in the equity markets. There doesn’t seem to be any pattern among the gainers, just a variety of pockets of higher volatility.

On Monday I came across what appears to be the first of a two-step trade. With VXX around 20.00 someone came in and sold 200 of the VXX Feb 3^{rd} 21 Calls for 0.30 and purchased 200 VXX Feb 10^{th} 31 Calls for 0.05 resulting in a net credit of 0.25. Friday VXX closed well below the short strike, which leads me to believe whoever still owns the VXX 31 Call that has a week until expiration may be using that position to sell another call if they still have a short bias with respect to VXX.

As the front month starts to approach settlement date, VIX option traders start to focus on farther dated contracts, normally rolling to the following month. However, it seems skipping March is in vogue among VIX traders with many of last week’s block trades looking to April. This week’s trade review involves a common structure often discussed in this space.

Mid-morning Wednesday, a trader decided to sell VIX Apr 13 Puts 0.65, purchase the VIX Apr 17 Calls for 1.72 and complete the trade by selling the VIX Apr 22 Calls for 1.00. The trade size was 13,500 contracts at a net debit of 0.07. The outcome for this trade, if held to expiration, appears below. Of course a volatility spike would probably result in some trading around this position.

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The value of SKEW increases with the tail risk of S&P 500 returns. If there were no tail risk expectations, SKEW would be equal to 100.

The **FAQ** on the SKEW Index notes that –

*“The price of S&P 500 skewness is inconvenient to use directly as an index because it is typically a small negative number, for example -.8, -2.3, or -4.3. SKEW converts this price as follows: SKEW = 100 – 10 * price of skewness. With this definition, a price of -2.1 translates to a SKEW value of 121. S&P 500 options with 30 days to expiration are generally unavailable. SKEW is therefore interpolated from two “SKEW” values at the maturities of nearby and second nearby options with at least 8 days left to expiration.”*

**HIGHER SKEW VALUES IN RECENT YEARS**

The average value of SKEW (since the beginning of its data history in 1990) has been 118.4. Prior to 2014, the highest average daily closing value in a year for the SKEW Index was 122.5, but in each of the years 2014, 2015, 2016, and year-to-date 2017, the average daily closing level for the SKEW Index was 127.5 or higher.

* *

**30 ½ YEARS — BENCHMARK INDEXES AND SPX PUT OPTIONS**

For investors who wish to learn more about hypothetical long-term performance of strategies that use index options, CBOE provides more than 30 strategy benchmark indexes. Note in the two charts below that the left tail risk was higher for the S&P 500 Index than it was for two indexes that use SPX put options – the CBOE S&P 500 PutWrite Index (**PUT**) sells cash-secured SPX options, while the CBOE S&P 500 5% Put Protection Index (**PPUT**) buys out-of-the-money protective put options on the SPX Index.

**MORE INFORMATION**

For more information on skew and use of options for protection and income, please visit www.cboe.com/SKEW and www.cboe.com/benchmarks.

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