Confidence runs high; I’m Vix’ed out

Maybe I’m a one-trick pony.  I just happen to like playing the Vix – in particular, the Vix-related derivatives.  There are 2 main ways I like to do this:

  1. Just short it: I take advantage of a few related characteristics to short the Vix via UVXY.  In particular, I like that UVXY is the prototype of a bad long-term buy, so I short the product using medium-dated options.  After biting my nails during the past couple months’ market volatility, this trade finally hit pay dirt.  I’m out of my latest iteration as of today, after holding about 2.5 months.
  2. Spread it: when the markets get really scared, the Vix futures curve looks like the red line below (from previous post):
    Mmm...backwardation.  Source: thinkorswim by TDAmeritrade

    Mmm…backwardation. Source: thinkorswim by TDAmeritrade

    Today that red curve looks much more normal, as we’re back to daily all-time highs:

    All back to normal.  Source: thinkorswim by TDAmeritrade.

    All back to normal. Source: thinkorswim by TDAmeritrade.

    I took off the latest iteration of this trade yesterday.  About 3 weeks of holding time.

What have I learned?  First, it’s great to learn with successful trades.  My theses were researched and executed when the time was right.  Most importantly – in my mind – is patience: at one point I was looking at some pretty solid losses on the #1 trade as the Vix kept climbing higher.  Nevertheless, that’s why I chose medium-term options to express the trade, and ensured I had a manageable maximum loss at initiation of the trade.


VIX futures calendar spread strategy: a little data mining

Has anyone noticed a bit more volatility in markets these days?  That SNB shocker was something, indeed – it seems some FX retail brokerage houses have already declared insolvency.  Anyway, the increased volatility has had quite an impact on VIX in the year so far: after starting the year at an elevated, yet respectable 17-ish, the index has climbed to a panic-like 22.5.  For those stats-minded, that latter figure implies a daily move of around 1.4% for the SPX – not unusual recently, but pretty darn high compared to post-2008.

OK, let’s get to work.  The VIX is very elevated, but it can be a mug’s game to short (i.e. the VIX can get smashed a bit like the SNB smashed the Euro/CHF exchange rate yesterday).  Too risky for an outright short.  What to do?? A VIX calendar spread.

Mmm...backwardation.  Source: thinkorswim by TDAmeritrade

Mmm…spot the backwardation. Source: thinkorswim by TDAmeritrade

  • Hypothesis: times of high volatility causes the VIX futures curve to go from contango (e.g. further months more expensive than near months) to backwardation (the opposite).  When the market returns to more normal conditions, the contango will return.
  • Method: when the futures curve goes to backwardation, or very near it, go short a near-month future and hedge by going long a further-dated future.  Take off the trade when contango returns.
  • Which contracts?  Note the curves in the picture.  The red line is today’s VIX futures curve – e.g. flat to backwardated.  The other lines are the month-end VIX futures curves for the past 6 months.  A couple observations:
    • In normal markets, there is a pretty smooth contango.  So the max return for any 1-month calendar spread is about the same going out 6 months.  You could choose, say, months 2/3, 3/4, etc.
    • However: notice how much extra movement occurs in months 1 & 2, say, relative to further months.  So, it’s a risk/return situation: if you want higher risk/return, go for earlier months.  I, being a chicken, will stick with a bit less risk – months 3/4, perhaps.  That means I choose to be short Apr 2015, hedged by long May 2015 futures.
  • A bit of data mining to convince me: I downloaded the month 3 and 4 continuous contracts from Quandl, then did the following rough analysis:
    • Time range: 1 Jan 2008 through yesterday, daily data.
    • Metric: gross profit from Month 3 and 4 calendar spread, assuming a 1-month hold (i.e. mechanically holding the position 1 month).
    • Brief, dirty stats:
      • Unconditional (e.g. all daily observations)
        • Observations = 1740
        • Mean gross return = $0.006/spread
        • Expected return, using uniform probability distribution and decile returns (including min/max) = $0.033/spread
        • Z-test for mean different than 0 = 36.8%.  In sum, I can’t assume the expected return is positive.
      • Conditional (e.g. only enter trade when spread is $0.05 or less)
        • Observations = 454
        • Mean gross return = $0.341/spread
        • Expected return, same method = $0.320/spread
        • Z-test for mean different than 0 = less than 0.01%.  In sum, I can assume a positive return.
  • Summary: I think this strategy will work in the current environment, so I’ve put on the trade in small size to test the waters.  Wish me luck!

Round 3: winning the mental battle against volatility using managed futures

In the previous posts (here and here) I have illustrated a couple approaches to one of the most dangerous parts of personal investing: the mental anguish caused by volatile (equity) markets.  It’s one of the main reasons we as retail investors tend to buy high and sell low: it’s tough to watch the big oscillations that an equity portfolio experiences.

So far I’ve shown a very easy-to-implement solution (the ostrich method) and a somewhat easy approach (a leveraged investment in bonds).  Today I’ll talk about another method, which is adding an uncorrelated strategy to the equity portfolio: managed futures.

I’ve written about managed futures before.  In a nutshell, the strategies employed by these funds tend to be momentum-related, and therefore exhibit near-zero correlation with equities.  That’s about as good as diversification gets.  Let’s take a look at the managed futures index (the Barclay BTOP50) versus the S&P500 since 1997:

Better diversification = better portfolio.  Source: Quandl and BarclayHedge.

Better diversification = better portfolio. Source: Quandl and BarclayHedge.

A few observations:

  • The performance of managed futures does indeed occur at different times than stocks.  There seems to be a toss-up whether managed futures will make money when stocks are gaining value
  • A 60/40 mix of stocks and managed futures looks like better value portfolio diversification than 60/40 stocks and (unlevered) bonds.  This is due to managed futures having 3 beneficial characteristics versus bonds in the period:
    • Better returns (5% p.a. versus 1% p.a.)
    • Correlation to stocks closer to zero (-0.17 versus -0.27)
    • Volatility – and thus ‘diversification bang for the buck’ – higher (8% p.a. versus 5% p.a.)
  • The 60/40 mix of stocks and managed futures seems a lot less ‘equity-like’ than the 60/40 stocks and bonds mix.  That’s the impact of the strategy diversification

So we can see that, when a material part of the portfolio is moved from equities to managed futures, the result is a better risk and return tradeoff.

Extension: let’s consider the same approach as last post, which used futures or similar to create a Triple 5-Year Note.  It turns out that a common practice among managed futures providers is targeted volatility, so we can in effect choose the amount of volatility we would like our investment in managed futures to have.  Some managers keep low volatility (e.g. 5-10% p.a.), whereas others have quite high volatility (e.g. 25-30% p.a.).  Let’s look at the effect of this on the equity/managed futures portfolio:

Higher volatility of diversifier = better diversification.  Source: Quandl and BarclayHedge

Higher volatility of diversifier = better diversification. Source: Quandl and BarclayHedge

We can see:

  • The blue line remains the S&P500
  • The purple line is the 60/40 mix of stocks and managed futures
  • The green and red lines are different mixes of stocks and a theoretical managed futures manager achieving 3x the risk and return of the index (I am aware of managers who have achieved this, but I don’t think their track records are public)
  • In order to make the 60/40 mix of equity and 3x managed futures seem sensible, I’ve switched to a log scale on the chart
  • Two conclusions to note:
    • With the higher volatility, the 60/40 mix has pretty amazing performance, which doesn’t resemble equity very much.  So stocks have essentially become the diversifier, even though they’re a larger proportion of capital.
    • A higher volatility managed futures program means you need less capital invested to get the same diversification benefits.  In this case, switching from 60/40 to, say, 85/15 gives about the same diversification as the old managed futures case with better performance.

In sum: adding a diversifying strategy to the equity portfolio improves risk-adjusted performance quite a bit.  If you’re looking for ‘bang for the buck’, a high-volatility managed futures program should allow for a smaller capital allocation while maintaining diversification benefits.  And that should allow for less portfolio volatility overall, which helps win the mental battle.

Round 2: winning the mental battle against volatility using….volatility.

Last post addressed one way to deal with the mental anguish provided by market volatility: the ostrich method.  It’s a simple method, albeit not particularly sophisticated.  Also, the ostrich method doesn’t really help improve portfolio performance, aside from keeping the investor away from rash trading decisions.

So let’s try another way; let’s consider using market volatility as a weapon of choice against market volatility.  Let’s see how more volatility can help our portfolio weather the markets.

To conquer volatility, you must become...oh never mind.  Source: Google Images.

To conquer volatility, you must become…oh never mind. Source: Google Images.

Again, let’s focus on a concrete example:

And the data, with monthly views.  I’ve included a 100% equity portfolio for comparison:

Cutting volatility through diversification.  Source: Quandl.

Cutting volatility through diversification. Source: Quandl.

A couple observations:

  1. Wow, what a run from stocks since 2008
  2. Even with 60/40 portfolio balance, the portfolio looks a lot like the 100% equity portfolio.  How much diversification am I really getting?

OK, so we can show that diversification generally works to lower portfolio volatility (from about 16% p.a. with all stocks, to about 10% p.a. with 60/40 blend).  Returns are a bit lower, but risk-adjusted return is a bit higher.  What I’m not so happy with is the following:

  1. My portfolio still looks very equity-like, even with 60/40
  2. This diversification is expensive: I’m sacrificing about 1.2% returns p.a. for the peace of mind coming with diversification

How to solve this?  Here’s one idea: suppose I created a new portfolio holding called Triple 5-year Note.  This holding is exactly the same as the 5-year note in the example, but has 3 times the return stream.  That means 3x the risk and 3x the return.  For those familiar with futures markets, this is absolutely trivial to create (provided account size is large enough to overcome lot size issues, but I digress).

Let’s now add a 3rd portfolio to the arsenal, which is 60% stocks and 40% Triple 5-year Note:

More vol = better portfolio.  Source: Quandl.

More vol = better portfolio. Source: Quandl.

A few observations:

  1. The new portfolio is a bit less equity-like.  In particular, I note smoother performance around the tech bubble, and better performance around 2008
  2. Diversification is cheaper.  I only sacrificed 0.8% returns p.a. for my diversification
  3. The new portfolio looks less volatile than the others.  And it is: portfolio volatility is 9%, rather than 10% or 16% in the older case

So what have we done?  We’ve lowered portfolio volatility and increased returns over the old 60/40 by increasing volatility of the diversifying asset.  The 5-year note future’s volatility is about 5% p.a., which is roughly 1/3 equity volatility.  By increasing the 5-year volatility to equal that of equities, we get more diversification.  That leads to better portfolio returns, and lower portfolio risk.

Extension: some may recognise this idea as a basic concept behind products such as Risk Parity or Managed Futures strategies.  In a nutshell, the strategies equalise each asset’s volatility in the portfolio, then weight each (volatility-scaled) asset such that diversification is maximised.

How to implement: I can think of a few ways to fight portfolio volatility through this method:

  1. Use futures/options – if your account is large enough (e.g. 1 E-mini S&P Future has a notional value of around $100,000, and 1 5-year note future has a notional value of about $120,000), use futures as a replacement for long stocks/bonds.  You can easily create the Triple 5-year through buying 3x the note futures.  Smaller accounts can use option strategies, such as buying deep ITM calls or vertical spreads.  NB: you’ll need to roll these derivatives each month to maintain exposure.
  2. Use leveraged ETFs – for example, TYD is a 3x-levered ETF on 7-10 year US Treasuries.  It’s very illiquid, so be careful.  Vol drag might be an issue, but probably not very (volatility is low, and carry is positive).
  3. Buy a mutual fund – for example, AQR has a well-known Risk Parity mutual fund which uses similar techniques as noted above.  It would mean having only this fund in the portfolio, rather than in addition to other holdings.  But it’s an easy solution.

Next time, I’ll talk about managing portfolio volatility through adding managed futures.  In particular, what adding a small allocation to the strategy, and the effect of lower/higher volatility programmes, do for portfolio performance.

Winning the mental battle against volatility – the ostrich method

I’m planning to write a few entries on a topic close to my heart (insert jokes about how I need a real life here): portfolio volatility.  To begin, let me propose an old-fashioned technique for fighting the mental anguish associated with market volatility: the ostrich method.

A keen user of the ostrich method.  Source: Google Images.

A keen user of the ostrich method. Source: Google Images.

Let me explain with a concrete example:

  • A somewhat-typical portfolio: 
    • 50% long equities.  Proxied by the S&P 500 index
    • 30% long bonds.  Proxied by the performance of a US Treasury Bond ETF
    • 20% long commodities.  Proxied by the performance of DB Commodities ETF
    • Portfolio start = Feb 2006 (start date of commodities ETF)
    • No rebalancing.  Just set and forget

Let’s look at the data.  All return series are rescaled to begin at 100:

Portfolio performance view Feb 2006 - Nov 2014.  Source: Quandl

Portfolio performance view Feb 2006 – Nov 2014. Source: Quandl

A couple observations:

  1. Nice performance.  What we all know by this point: buy and hold (if you kept through the crash of 2008) worked just fine.
  2. Lots of squiggles.  That’s volatility for you.  Not just the big swings (e.g. portfolio value going from 120 -> 80 in the crash), but the regular gyrations in the market.  Look at the recent sell-off in equities at the right edge: that was indeed cringe-worthy.
  3. (aside) Long commodities don’t look very good.  I’ve talked about this at length in previous posts.

Now for the ostrich method of combating market volatility.  It’s simple to explain, by devilishly difficult to enact:

  • Overall theory: returns for market risk premia generally oscillate, but are positive in the long term.  Thus, if we censor intermediate observations, we can focus more on the long-term drift than the interim fluctuations.
  • In plain speak: markets rise and fall a lot, but generally rise over time.  For long-term investors, it makes sense to generally ignore market prices until a decision needs to be made (e.g. rebalance or sell holdings).
  • Example: Using the same portfolio shown above, let’s take a look at a couple more charts.
    • Monthly looks: suppose you just look at your monthly brokerage statement, and forget about financial headlines/news.  Here’s how your portfolio growth would look.  As an aside, look how the recent equity sell off has “disappeared” from the chart…
    • Same portfolio, using monthly data.  Source: Quandl.

      Same portfolio, using monthly data. Source: Quandl.

    • Yearly looks: suppose you just look at your annual review of your account.  Here’s how the portfolio growth would look.  Notice how smooth everything looks!
    • Same portfolio, using yearly close data.  Source: Quandl.

      Same portfolio, using yearly close data. Source: Quandl.

In sum, and probably flogging a dead horse here: long-term investors shouldn’t care about the newspapers/financial news/websites/etc., at least insofar as making portfolio adjustments.  Diversification works, when given time.

Next time I’ll write how volatility is a very useful thing.  Sometimes we might want more, not less, volatility in our portfolios.

Christmas comes early….

My view of the markets these days:

Is it December yet?  Might as well be...  Source: Google Images.

Is it December yet? Might as well be… Source: Google Images.

The VIX is back to 13-ish level. That’s still considerably higher than the 10-level we saw in the summer, but BORING compared with the fun and games of October.

The main excitement remains in commodities, as well as some relatively decent volatility in currencies these days (thanks again, JCB).  Those plain-vanilla investors in stocks/bonds?  You’re doing just fine.  If volatility stays low like this, the reverse of the ‘Sell in May and Go Away’ seasonality effect should keep S&P returns for 2014 steady in the range of +10% or so.  Much rosier picture than S&P at 1850-ish just a month ago, right?

How would you like your returns skewed?

There have been several times in the past where I’m explaining ‘XYZ strategy’ to someone (hopefully they asked me beforehand), and the concept of skewness comes up.  A couple examples:

  • Several (successful) strategies lose far more frequently than they win.  It’s not always like playing the lottery…
  • Sometimes ‘the sure thing’ trade, which has made money every day, suddenly blows up.

Thus loops in the concept of skewness – how big are losses relative to gains?  On the lottery side, you’re almost certainly going to lose  USD 1 on a game with a (highly improbable) gain of USD tens of millions.  But other examples abound in financial markets:

  • Long-only (just about) anything: this is a negatively skewed strategy.  Most months/years you will have a gain, but some months will be TERRIBLE.  Don’t think about the little correction we just had…think about 2008.  It can take years to recoup the losses from long-only: for example, notice that the NASDAQ is still about 10% below its 2000 peak.
  • Venture capital: this is a positively skewed strategy, in its most basic form.  The VC fund manager selects (say) 10 companies at an early stage of development.  Financials don’t really mean much at this stage – they could do anything.  The hope is that, out of 10, there will be 1 big winner and maybe a few small winners.  The others are expected to be written off.  So, one gain outweighs the many.
  • Volatility selling: this is a classic negatively skewed strategy.  VERY negatively skewed, epitomising ‘picking up pennies in front of a steam roller’.  After premium selling funds lost about 50-70% in 2008 (or went completely bust), several actually hit high water marks in the past couple years.  So it’s a sustainable, if nerve-wracking, strategy.  By the way, insurance products and market making are roughly the same as option premium writing, in terms of performance characteristics.
  • Momentum trading: a classic positively skewed strategy.  Frequently momentum is classed as ‘long volatility’, which it is…kinda.  More long gamma…but anyway.  This is a ‘pain trade’, in that most of the time you’re losing money as markets oscillate back and forth and you’re trading with the trend.  Only occasionally do the big trends come; you can’t really forecast them, and you MUST be in the market when they come.  Otherwise this is a losing strategy.

I leave you with the following track records, harvested from Altegris’s managed futures website.  Interesting place to learn about volatility and momentum offerings.

Classic volatility selling strategy characteristics: nice, steady gains punctuated by large losses.  Source:

Classic volatility selling strategy characteristics: nice, steady gains punctuated by large losses. Source:

Screen Shot 2014-10-23 at 15.47.29

Classic example of a higher-geared momentum fund. Notice how the fund spends most of its time below high water mark; this is broken up with infrequent, large gains. Source:

Extra credit: those seeking more technical info on return skewness, and particularly how the time-variance of skewness is a function of strategy design, should look at this wonkish paper.