$VIX Explodes: What Happens After 1 Day $VIX Gains of 30+%?

Today the $VIX, or CBOE Volatility Index (aka the fear index) posted a one day gain of 34%. Let’s look at the ramifications of this rare event.

Using $VIX history going back to 1990:

  • There are only 19 times in the past 23 years that $VIX posted a 1 day gain greater than 29.99%
  • The largest 1 day $VIX gain was 64.22% on February 27, 2007.
  • $VIX posted 1 day gains of greater than 29.99% on 4 occasions in 2011, the most of any year
  • In 2012, $VIX never posted a 1 day gain greater than 29.99%

What’s Happened Over the Next 100 Days?

We will buy $VIX or $SPY at the close after $VIX posts a 1 day gain greater than 29.99% No commissions or slippage included.

SPY and VIX after VIX Surge

  • There were 19 occurrences of the setup on $VIX with 11 trades held the full 100 days.
  • There were 14 trades made on $SPY with 9 trades held the full 1o0 days.
  • The Left Axis has $SPY average profit or loss.
  • The Right Axis shows $VIX average gain or loss.

There are not many samples, so I am cautious of drawing strong conclusions. However, it is safe to say that this setup is not bullish. I like to think about the effect of a volatility explosion being similar to the effect of throwing a large rock into a still pond. As the waves travel outward from the impact, they get smaller and smaller over time and distance. Although the immediate effect is over fairly quickly, the waves take some time to diminish. Indeed, we see that relationship expressed rather elegantly in the graph above.

Previous Posts on Volatility Explosions:

$VIX Makes Huge Gains: What Happens Next?

More Research on Large $VIX One Day Gains

 

Volatility Based Allocation, Part 2

Read Part 1 here: Volatility Based Allocation.

In Part 1 I examined the basic strategy as outlined in Empiritrage’s documentation. Using $SPY as a tradeable proxy for the S&P 500 total return index, the backtests of the strategy and other similar strategies were posted. Part 2 will take a look at how this strategy has worked with the 4 other asset classes, and will then run the strategy over 4 classes equally weighted.

My thoughts on the results from the backtests using $SPY were as follows:

The volatility-based allocation (VBA $SPY) was able to come close to the $SPY benchmark return while significantly lowering risk metrics.

  • Exposure was reduced by roughly 60%
  • Maximum System % Drawdown was reduced by 80%
  • Sharpe Ratio more than doubled compared to the other strategies.

If the goal is to beat the S&P 500 and include some downside protection, the ROC(5,252) and MA(2,12) have accomplished it. But with increased CAGR comes increased risk. I do not think it is possible to separate the good from the bad, but the VBA strategy shows on $SPY that it is possible to keep most of the good and throw out most of the bad.

Now let’s look at how the VBA strategy performed with the 4 other asset classes: $EEM (Emerging Markets), $IYR (Dow Jones US Real Estate), $EFA (Developed, Non-US Markets), $IEF (7-10 Yr. Treasury Bonds). All tests were run from the first trade date through 1.15.13. No commission or slippage were included.

$EEM Emerging Markets

¹See the definition of the risk metrics used at the bottom of the post.

$EEM benchmark CAGR was very good at 15.30%. None of the applied strategies were able to come close to achieving the benchmark CAGR. The VBA strategy was the worst performer and still had a fairly steep maximum system drawdown. These results demonstrate a simple but important maxim: As CAGR increases, so will the maximum drawdown. That relationship is best measured by CAR/MaxDD. Note that the benchmark CAR/MaxDD was 0.23. This ratio was maintained consistently throughout all the strategies. Only the ROC(5,252) strategy was able to improve it.

$IYR Dow Jones US Real Estate

All of the strategies were able to beat the benchmark CAGR for $IYR. The VBA strategy provided the 2nd highest CAR/MaxDD ratio and the highest Sharpe. The Volatility Regime strategy came in a close second.

$EFA Developed, Non-US Markets

$EFA had a very disappointing benchmark CAGR. All of the strategies were able to beat it. VBA again acheived the highest CAR/MaxDD ratio and the highest Sharpe.

$IEF 7-10 Year Treasury Bonds

$IEF also had a low benchmark CAGR, but this should not be that shocking considering it is a bond ETF. None of the strategies were able to beat the benchmark, and both of the VBA strategies lost money.

I chose $IEF because I found it to be the closest tradeable proxy for the bond index used by Empiritrage. Perhaps it was not the best bond ETF to choose. Because both VBA strategies lost money using $IEF, I removed $IEF from the Core Equal Weight test.

To summarize: VBA yielded little benefit on $EEM, was the best performer for $IYR and $EFA, and lost money on $IEF.

Let’s examine how VBA performed as a strategy applied to a portfolio holding equal weights of each asset class.

Core 4 Equal Weight

Again we see that VBA has beat the other strategies across all risk metrics including CAR/MaxDD and Sharpe.

The bottom line is that using VBA can allow for an asymmetrical relationship between risk and CAGR. This relationship between risk and CAGR is nothing new. One generally accepts greater risks for greater returns. In seeking to mitigate risk, VBA does punish CAGR, but not as much as it mitigates risk.

These studies represent merely a jumping off point for VBA and should not be assumed to be as good as it gets. In fact, one of the authors of the strategy is actively seeking testing and experimentation of this strategy. I believe that the strategy can be tweaked to increase CAGR while maintaining similar risk metrics.

My intuition tells me that tweaking the $VIX moving average lookback periods is a good place to start. I would also experiment with aligning the monthly re-balancing period with moving averages created with monthly rather than daily bars. Finally, I would want to add other asset classes and indices to the portfolio such as commodities and the Nasdaq.

Bonus: Equity Curve for the VBA Core 4 Equal Weight Strategy

¹Risk Metric Definitions:

 

Volatility-Based Allocation

A friend recently sent me this publication from Empiritrage: Volatility-Based Allocation. I encourage reading the document as the strategy is interesting, and a basic understanding of what they are testing will be necessary to understanding my article.

The general idea behind the strategy is to use two $VIX moving averages (10 and 30 day) and a 12 month moving average (250 day) to create Risk-on, Risk-off regimes for 5 asset classes:

  • SP500 Index ($SPY)
  • FTSE NAREIT All Equity REITS Total Return Index—benchmark for REITs ($IYR)
  • MSCI EAFE Index—benchmark for investment in equity markets outside of U.S. and Canada ($EFA)
  • MSCI EEM Index—benchmark for investment in emerging markets ($EEM)
  • Merrill Lynch 7-10 year government bond index ($IEF)

A quick glance at slide 3 of the publication will provide a graphic presentation of how VBA works.

The backtested results presented in the publication were decent enough for me to consider adding VBA to my own portfolio. But before doing so, I wanted to see what would happen if tradeable securities were used instead of the total return indices (which are not able to be bought and sold).

I’m not sure what to make of the dividend distributions that are part the total return indices but not the price indices. My data is all price indices, meaning it is dividend adjusted. In real life, would trading in and out of $SPY or other tradeable asset classes mean missing some dividend distributions? I think it would, and I’m not sure how Empiritrage took that into account. I have sent them an email with a link to this post in case I have erred or in case they want to provide some clarity.

We can assume, since I will be using price indices (ETFs, actually) for testing the system, that my results will not be as good as their results since dividends will not be included. Other considerations are that ETFs do not perfectly track their underlying indices and can be subject to bid/ask and liquidity issues. I am also including a return on cash via $SHY (iShares 1-3 Treasury Bond ETF), but I have not calculated the return the same as Empiritrage as they used T-Bills. My goal is to test how VBA would work in real-life for a real person who chooses to manage his or her long term accounts. I will test the strategy over the data and securities that such a person is likely to have available.

Backtested Results

I will present my results much the same way Empiritrage did, for the sake of easy comparison. All results are frictionless, meaning commissions and slippage have not been included. Trades are made and portfolios are rebalanced once a month, on the first trading day of the month.

It has been a rough decade for these asset classes. While $EEM returned 15.30%, its maximum system % drawdown was a killer. The Core 5 EW, which is simply all five classes held in equal weights, also had a killer maximum drawdown. What Empiritrage is seeking to accomplish is to replicate the returns without the risk.

Let’s see if their volatility-based allocation strategy is able to do that.

The volatility-based allocation (VBA $SPY) was able to come close to the $SPY benchmark return while significantly lowering risk metrics.

  • Exposure was reduced by roughly 60%
  • Maximum System % Drawdown was reduced by 80%
  • Sharpe Ratio more than doubled compared to the other strategies.

If the goal is to beat the S&P 500 and include some downside protection, the ROC(5,252) and MA(2,12) have accomplished it. But with increased CAGR comes increased risk. I do not think it is possible to separate the good from the bad, but the VBA strategy shows on $SPY that it is possible to keep most of the good and throw out most of the bad.

The Volatility-Based Allocation Equity Curve

Click on the charts to make them bigger…

Upon seeing the equity curve, I started thinking that it would be hard to stick with this system from 2003 – 2006 when the market was steadily trending up and the system was losing money. And therein lies the system trader’s dilemma.

The next post will take a look at how this strategy has worked with the other 4 asset classes, and will then run the strategy over all 5 classes equally weighted. If there are any questions, please let me know in the comments. I have glossed over quite a few of the specifics in order to make this post manageable.

Exit question: Is the market making a huge triple top?

 

$VIX Explodes: Bullish or Bearish for $SPY?

Over the last 5 days, $VIX has gained more than 25%. Is a large gain in the volatility index bullish or bearish for the S&P 500 over the next 50 days?

The Rules:

  • Buy $SPY at close when $VIX has gained more than 25% over the last 5 days.
  • Sell $SPY at the close X days later.
  • No commission or slippage included.
  • All available $SPY history used.

The Results:

Some Additional Stats:

Next Day Winning Percentage: 58.93%
5 Day Winning Percentage: 69.62%
Median Trade After 50 Days: 2.74%
Average Trade After 50 Day: 2.26%
Number of Setups: 112
Number of Trades Held 50 Days: 44

While my breadth indicators are not quite signaling that a bounce is imminent, this study has yielded bullish results.

$VIX closed at 19.48 and hasn’t closed above 20 since July, 2012. Let’s see what happens over the next couple of days. Another $VIX spike and an extreme breadth reading will make for a great short-term bottom.

 

 

What Does a Low $VIX Mean Going Forward? Expect $SPY Outperformance.

On Friday, August 17th, $VIX made a new 1000 day low. Financial journals broadcasted this event, and headlines such as this and this are making the rounds. What can we take away from these articles?

…the VIX has spent over half of its time over the past two decades (from 1992 through Tuesday) between 10-20. So the level it’s at today is very, very normal.

“Be careful if you think the VIX has nowhere to go but higher”….the VIX has a history of remaining depressed during long periods of time — like they did between 2004 and 2007 when stocks slowly drifted higher.

Okay, easy enough. What we are witnessing with $VIX is not abnormal, but that isn’t information isn’t specific enough to help traders and investors. Let’s break the $VIX history apart so that we have information that is actionable.

We are going to start by looking at what happens when $VIX crosses beneath various moving averages. It is currently trading beneath its 10, 20, 50, and 200 day moving averages. We would expect this with it making a new 1000 day low.

The rules are simple: Buy $SPY at Close when $VIX Closes Beneath its X Day Average.

$SPY Buy-n-Hold is calculated by taking all $SPY history, breaking it into 50 day segments, and averaging the segments.

When $VIX is beneath the shorter (10, 20) moving averages, $SPY tends to track or slightly underperform its historical average performance.  This is likely due to short-term  mean reversion: as $VIX dips and stretches farther beneath the shorter moving average it will reverse for a brief period of time.

When $VIX is beneath the longer (50, 200) moving averages, $SPY tends to outperform its historical average performance. As $VIX stretches farther and farther beneath these longer-term averages, $SPY has tended to trend higher in a low volatility environment, enabling the outperformance.

Let’s dig deeper and look at the percentage of winning trades.

There have been 69 trades made¹ when $VIX crosses beneath its 50 day average. 68.12% of those were higher after 50 days.

There have been 55 trades made when $VIX crosses beneath its 200 day average. 60% of those were higher after 50 days.

The bottom line is that when $VIX  is beneath its longer-term moving averages, $SPY has tended to outperform its historical average performance and there is a better than average chance that $SPY will be higher 50 days later.

The next post will look at what has happened after $VIX has made a new X day low.

¹Trades held the full 50 days. There are more than 69 trades made if each is not held the full 50 days.

 

$VIX Trades for 100 Days Below Its 200 Day Average: Bullish or Bearish?

I recently published a simple study which examined how SPY has performed after $VIX closed above its 200 day average. The flip side of that study is to examine how SPY has performed after $VIX has closed for X days beneath its 200 day average and then closes above it.

On June 1st, $VIX closed above its 200 day moving average after trading beneath it for 115 days. There are only six instances in the entire $VIX history where it has traded for more than 100 days beneath its 200 day moving average. Does the number of days it has traded beneath the 200dma have any discernible effect on SPY performance if SPY is bought at the close the day $VIX closes back above its 200dma?

The Rules:

Buy SPY at the close if

  • $VIX has closed beneath its 200dma for more than X days
  • $VIX closes above its 200dma

SPY is sold Y days later. No commissions or slippage included. All SPY and $VIX history used.

The Results:

The results show that we can expect more sideways trading and volatility. While the >99 days results are promising, there were only 5 trades. The dates and results for these 5 trades are below. Perhaps 20 years from now we will have enough samples to draw some conclusions, but for now, we must only observe and try not to make major decisions on the results generated from only 5 trades.

EntryDate      % Gain/Loss

SPY   8/3/1999     -3.21%
SPY    3/10/2004  -2.63%
SPY    2/27/2007   8.36%
SPY    1/22/2010    9.00%
SPY    6/1/2012       3.71%

The >24 days below results were generated from 30 trades. 25 trades were held the full 50 days. While this is not a large sample, I believe it is large enough for us to expect more volatility and sideways trading.

$VIX Climbs Above its 200 Day Average. Bullish or Bearish?

On Friday, $VIX closed above its 200 day moving average. The last day it traded above this average was December 14, 2011. With volatility seemingly entering a bullish phase, is this a bullish or bearish setup for buying SPY?

The Rules:

Buy SPY at the close if $VIX closes above its 200 day moving average, and $VIX was not above the 200MA the previous day.

The Results:

Using all SPY history, there were 60 occurrences of this setup. After 50 days, 60.38% of trades were winners.

The win rate combined with the higher average winning trade has resulted in SPY averaging just over 2% after 50 days.

The market has a bullish bias. While this test reflects that, it also demonstrates that a climbing $VIX does not necessarily signify a death knell for the markets.

$VIX Explodes: What Happens After 1 Day $VIX Gains of 30+%?

Today the $VIX, or CBOE Volatility Index (aka the fear index) posted a one day gain of 34%. Let’s look at the ramifications of this rare event.

Using $VIX history going back to 1990:

  • There are only 19 times in the past 23 years that $VIX posted a 1 day gain greater than 29.99%
  • The largest 1 day $VIX gain was 64.22% on February 27, 2007.
  • $VIX posted 1 day gains of greater than 29.99% on 4 occasions in 2011, the most of any year
  • In 2012, $VIX never posted a 1 day gain greater than 29.99%

What’s Happened Over the Next 100 Days?

We will buy $VIX or $SPY at the close after $VIX posts a 1 day gain greater than 29.99% No commissions or slippage included.

SPY and VIX after VIX Surge

  • There were 19 occurrences of the setup on $VIX with 11 trades held the full 100 days.
  • There were 14 trades made on $SPY with 9 trades held the full 1o0 days.
  • The Left Axis has $SPY average profit or loss.
  • The Right Axis shows $VIX average gain or loss.

There are not many samples, so I am cautious of drawing strong conclusions. However, it is safe to say that this setup is not bullish. I like to think about the effect of a volatility explosion being similar to the effect of throwing a large rock into a still pond. As the waves travel outward from the impact, they get smaller and smaller over time and distance. Although the immediate effect is over fairly quickly, the waves take some time to diminish. Indeed, we see that relationship expressed rather elegantly in the graph above.

Previous Posts on Volatility Explosions:

$VIX Makes Huge Gains: What Happens Next?

More Research on Large $VIX One Day Gains

 

Volatility Based Allocation, Part 2

Read Part 1 here: Volatility Based Allocation.

In Part 1 I examined the basic strategy as outlined in Empiritrage’s documentation. Using $SPY as a tradeable proxy for the S&P 500 total return index, the backtests of the strategy and other similar strategies were posted. Part 2 will take a look at how this strategy has worked with the 4 other asset classes, and will then run the strategy over 4 classes equally weighted.

My thoughts on the results from the backtests using $SPY were as follows:

The volatility-based allocation (VBA $SPY) was able to come close to the $SPY benchmark return while significantly lowering risk metrics.

  • Exposure was reduced by roughly 60%
  • Maximum System % Drawdown was reduced by 80%
  • Sharpe Ratio more than doubled compared to the other strategies.

If the goal is to beat the S&P 500 and include some downside protection, the ROC(5,252) and MA(2,12) have accomplished it. But with increased CAGR comes increased risk. I do not think it is possible to separate the good from the bad, but the VBA strategy shows on $SPY that it is possible to keep most of the good and throw out most of the bad.

Now let’s look at how the VBA strategy performed with the 4 other asset classes: $EEM (Emerging Markets), $IYR (Dow Jones US Real Estate), $EFA (Developed, Non-US Markets), $IEF (7-10 Yr. Treasury Bonds). All tests were run from the first trade date through 1.15.13. No commission or slippage were included.

$EEM Emerging Markets

¹See the definition of the risk metrics used at the bottom of the post.

$EEM benchmark CAGR was very good at 15.30%. None of the applied strategies were able to come close to achieving the benchmark CAGR. The VBA strategy was the worst performer and still had a fairly steep maximum system drawdown. These results demonstrate a simple but important maxim: As CAGR increases, so will the maximum drawdown. That relationship is best measured by CAR/MaxDD. Note that the benchmark CAR/MaxDD was 0.23. This ratio was maintained consistently throughout all the strategies. Only the ROC(5,252) strategy was able to improve it.

$IYR Dow Jones US Real Estate

All of the strategies were able to beat the benchmark CAGR for $IYR. The VBA strategy provided the 2nd highest CAR/MaxDD ratio and the highest Sharpe. The Volatility Regime strategy came in a close second.

$EFA Developed, Non-US Markets

$EFA had a very disappointing benchmark CAGR. All of the strategies were able to beat it. VBA again acheived the highest CAR/MaxDD ratio and the highest Sharpe.

$IEF 7-10 Year Treasury Bonds

$IEF also had a low benchmark CAGR, but this should not be that shocking considering it is a bond ETF. None of the strategies were able to beat the benchmark, and both of the VBA strategies lost money.

I chose $IEF because I found it to be the closest tradeable proxy for the bond index used by Empiritrage. Perhaps it was not the best bond ETF to choose. Because both VBA strategies lost money using $IEF, I removed $IEF from the Core Equal Weight test.

To summarize: VBA yielded little benefit on $EEM, was the best performer for $IYR and $EFA, and lost money on $IEF.

Let’s examine how VBA performed as a strategy applied to a portfolio holding equal weights of each asset class.

Core 4 Equal Weight

Again we see that VBA has beat the other strategies across all risk metrics including CAR/MaxDD and Sharpe.

The bottom line is that using VBA can allow for an asymmetrical relationship between risk and CAGR. This relationship between risk and CAGR is nothing new. One generally accepts greater risks for greater returns. In seeking to mitigate risk, VBA does punish CAGR, but not as much as it mitigates risk.

These studies represent merely a jumping off point for VBA and should not be assumed to be as good as it gets. In fact, one of the authors of the strategy is actively seeking testing and experimentation of this strategy. I believe that the strategy can be tweaked to increase CAGR while maintaining similar risk metrics.

My intuition tells me that tweaking the $VIX moving average lookback periods is a good place to start. I would also experiment with aligning the monthly re-balancing period with moving averages created with monthly rather than daily bars. Finally, I would want to add other asset classes and indices to the portfolio such as commodities and the Nasdaq.

Bonus: Equity Curve for the VBA Core 4 Equal Weight Strategy

¹Risk Metric Definitions:

 

Volatility-Based Allocation

A friend recently sent me this publication from Empiritrage: Volatility-Based Allocation. I encourage reading the document as the strategy is interesting, and a basic understanding of what they are testing will be necessary to understanding my article.

The general idea behind the strategy is to use two $VIX moving averages (10 and 30 day) and a 12 month moving average (250 day) to create Risk-on, Risk-off regimes for 5 asset classes:

  • SP500 Index ($SPY)
  • FTSE NAREIT All Equity REITS Total Return Index—benchmark for REITs ($IYR)
  • MSCI EAFE Index—benchmark for investment in equity markets outside of U.S. and Canada ($EFA)
  • MSCI EEM Index—benchmark for investment in emerging markets ($EEM)
  • Merrill Lynch 7-10 year government bond index ($IEF)

A quick glance at slide 3 of the publication will provide a graphic presentation of how VBA works.

The backtested results presented in the publication were decent enough for me to consider adding VBA to my own portfolio. But before doing so, I wanted to see what would happen if tradeable securities were used instead of the total return indices (which are not able to be bought and sold).

I’m not sure what to make of the dividend distributions that are part the total return indices but not the price indices. My data is all price indices, meaning it is dividend adjusted. In real life, would trading in and out of $SPY or other tradeable asset classes mean missing some dividend distributions? I think it would, and I’m not sure how Empiritrage took that into account. I have sent them an email with a link to this post in case I have erred or in case they want to provide some clarity.

We can assume, since I will be using price indices (ETFs, actually) for testing the system, that my results will not be as good as their results since dividends will not be included. Other considerations are that ETFs do not perfectly track their underlying indices and can be subject to bid/ask and liquidity issues. I am also including a return on cash via $SHY (iShares 1-3 Treasury Bond ETF), but I have not calculated the return the same as Empiritrage as they used T-Bills. My goal is to test how VBA would work in real-life for a real person who chooses to manage his or her long term accounts. I will test the strategy over the data and securities that such a person is likely to have available.

Backtested Results

I will present my results much the same way Empiritrage did, for the sake of easy comparison. All results are frictionless, meaning commissions and slippage have not been included. Trades are made and portfolios are rebalanced once a month, on the first trading day of the month.

It has been a rough decade for these asset classes. While $EEM returned 15.30%, its maximum system % drawdown was a killer. The Core 5 EW, which is simply all five classes held in equal weights, also had a killer maximum drawdown. What Empiritrage is seeking to accomplish is to replicate the returns without the risk.

Let’s see if their volatility-based allocation strategy is able to do that.

The volatility-based allocation (VBA $SPY) was able to come close to the $SPY benchmark return while significantly lowering risk metrics.

  • Exposure was reduced by roughly 60%
  • Maximum System % Drawdown was reduced by 80%
  • Sharpe Ratio more than doubled compared to the other strategies.

If the goal is to beat the S&P 500 and include some downside protection, the ROC(5,252) and MA(2,12) have accomplished it. But with increased CAGR comes increased risk. I do not think it is possible to separate the good from the bad, but the VBA strategy shows on $SPY that it is possible to keep most of the good and throw out most of the bad.

The Volatility-Based Allocation Equity Curve

Click on the charts to make them bigger…

Upon seeing the equity curve, I started thinking that it would be hard to stick with this system from 2003 – 2006 when the market was steadily trending up and the system was losing money. And therein lies the system trader’s dilemma.

The next post will take a look at how this strategy has worked with the other 4 asset classes, and will then run the strategy over all 5 classes equally weighted. If there are any questions, please let me know in the comments. I have glossed over quite a few of the specifics in order to make this post manageable.

Exit question: Is the market making a huge triple top?

 

$VIX Explodes: Bullish or Bearish for $SPY?

Over the last 5 days, $VIX has gained more than 25%. Is a large gain in the volatility index bullish or bearish for the S&P 500 over the next 50 days?

The Rules:

  • Buy $SPY at close when $VIX has gained more than 25% over the last 5 days.
  • Sell $SPY at the close X days later.
  • No commission or slippage included.
  • All available $SPY history used.

The Results:

Some Additional Stats:

Next Day Winning Percentage: 58.93%
5 Day Winning Percentage: 69.62%
Median Trade After 50 Days: 2.74%
Average Trade After 50 Day: 2.26%
Number of Setups: 112
Number of Trades Held 50 Days: 44

While my breadth indicators are not quite signaling that a bounce is imminent, this study has yielded bullish results.

$VIX closed at 19.48 and hasn’t closed above 20 since July, 2012. Let’s see what happens over the next couple of days. Another $VIX spike and an extreme breadth reading will make for a great short-term bottom.

 

 

What Does a Low $VIX Mean Going Forward? Expect $SPY Outperformance.

On Friday, August 17th, $VIX made a new 1000 day low. Financial journals broadcasted this event, and headlines such as this and this are making the rounds. What can we take away from these articles?

…the VIX has spent over half of its time over the past two decades (from 1992 through Tuesday) between 10-20. So the level it’s at today is very, very normal.

“Be careful if you think the VIX has nowhere to go but higher”….the VIX has a history of remaining depressed during long periods of time — like they did between 2004 and 2007 when stocks slowly drifted higher.

Okay, easy enough. What we are witnessing with $VIX is not abnormal, but that isn’t information isn’t specific enough to help traders and investors. Let’s break the $VIX history apart so that we have information that is actionable.

We are going to start by looking at what happens when $VIX crosses beneath various moving averages. It is currently trading beneath its 10, 20, 50, and 200 day moving averages. We would expect this with it making a new 1000 day low.

The rules are simple: Buy $SPY at Close when $VIX Closes Beneath its X Day Average.

$SPY Buy-n-Hold is calculated by taking all $SPY history, breaking it into 50 day segments, and averaging the segments.

When $VIX is beneath the shorter (10, 20) moving averages, $SPY tends to track or slightly underperform its historical average performance.  This is likely due to short-term  mean reversion: as $VIX dips and stretches farther beneath the shorter moving average it will reverse for a brief period of time.

When $VIX is beneath the longer (50, 200) moving averages, $SPY tends to outperform its historical average performance. As $VIX stretches farther and farther beneath these longer-term averages, $SPY has tended to trend higher in a low volatility environment, enabling the outperformance.

Let’s dig deeper and look at the percentage of winning trades.

There have been 69 trades made¹ when $VIX crosses beneath its 50 day average. 68.12% of those were higher after 50 days.

There have been 55 trades made when $VIX crosses beneath its 200 day average. 60% of those were higher after 50 days.

The bottom line is that when $VIX  is beneath its longer-term moving averages, $SPY has tended to outperform its historical average performance and there is a better than average chance that $SPY will be higher 50 days later.

The next post will look at what has happened after $VIX has made a new X day low.

¹Trades held the full 50 days. There are more than 69 trades made if each is not held the full 50 days.

 

$VIX Trades for 100 Days Below Its 200 Day Average: Bullish or Bearish?

I recently published a simple study which examined how SPY has performed after $VIX closed above its 200 day average. The flip side of that study is to examine how SPY has performed after $VIX has closed for X days beneath its 200 day average and then closes above it.

On June 1st, $VIX closed above its 200 day moving average after trading beneath it for 115 days. There are only six instances in the entire $VIX history where it has traded for more than 100 days beneath its 200 day moving average. Does the number of days it has traded beneath the 200dma have any discernible effect on SPY performance if SPY is bought at the close the day $VIX closes back above its 200dma?

The Rules:

Buy SPY at the close if

  • $VIX has closed beneath its 200dma for more than X days
  • $VIX closes above its 200dma

SPY is sold Y days later. No commissions or slippage included. All SPY and $VIX history used.

The Results:

The results show that we can expect more sideways trading and volatility. While the >99 days results are promising, there were only 5 trades. The dates and results for these 5 trades are below. Perhaps 20 years from now we will have enough samples to draw some conclusions, but for now, we must only observe and try not to make major decisions on the results generated from only 5 trades.

EntryDate      % Gain/Loss

SPY   8/3/1999     -3.21%
SPY    3/10/2004  -2.63%
SPY    2/27/2007   8.36%
SPY    1/22/2010    9.00%
SPY    6/1/2012       3.71%

The >24 days below results were generated from 30 trades. 25 trades were held the full 50 days. While this is not a large sample, I believe it is large enough for us to expect more volatility and sideways trading.

$VIX Climbs Above its 200 Day Average. Bullish or Bearish?

On Friday, $VIX closed above its 200 day moving average. The last day it traded above this average was December 14, 2011. With volatility seemingly entering a bullish phase, is this a bullish or bearish setup for buying SPY?

The Rules:

Buy SPY at the close if $VIX closes above its 200 day moving average, and $VIX was not above the 200MA the previous day.

The Results:

Using all SPY history, there were 60 occurrences of this setup. After 50 days, 60.38% of trades were winners.

The win rate combined with the higher average winning trade has resulted in SPY averaging just over 2% after 50 days.

The market has a bullish bias. While this test reflects that, it also demonstrates that a climbing $VIX does not necessarily signify a death knell for the markets.

Previous Posts by Woodshedder