The Hedged Volatility Strategy

Being short regular Volatility ETFs or long Inverse Volatility ETFs are winning strategies…most of the time. The challenge is that when the VIX spikes or when the VIX futures curve is downward sloping instead of upward sloping, very significant losses can occur. Many people have built and back-tested models that attempt to move from long to short to neutral positions in the various Volatility ETFs, but almost all of them have one or both of these very significant flaws: 1) Failure to use “out of sample” back-testing and 2) Failure to protect against “black swan” events.

In this strategy a position and weighting in the appropriate Volatility ETFs are established based on a multi-factor model which always uses out of sample back-testing to determine effectiveness. Volatility Options are always used to protect against significant short-term moves which left unchecked could result in the total loss of one’s portfolio value; these options will usually lose money, but that is a small price to pay for the protection they provide. (Strategies should be scaled at a minimum of 20% to ensure options protection.)

This is a good strategy for IRA accounts in which short selling is not allowed. Long positions in Inverse Volatility ETFs are typically held. Suggested minimum capital: $26,000 (using 20% scaling).

Volatility Trading Styles

The VIX Surge of Feb 2018

Volatility trading has become a popular niche in investing circles over the last several years.  It is easy to understand why:  with yields at record lows it has been challenging to find an alternative to equities that offers a respectable return.  Volatility, however, continues to be volatile (which is a good thing in this context) and the steepness of the volatility curve has offered investors attractive returns by means of the volatility carry trade.  In this type of volatility trading the long end of the vol curve is sold, often using longer dated futures in the CBOE VIX Index, for example.  The idea is that profits are generated as the contract moves towards expiration, “riding down” the volatility curve as it does so.  This is a variant of the ever-popular “riding down the yield curve” strategy, a staple of fixed income traders for many decades.  The only question here is what to use to hedge the short volatility exposure – highly correlated S&P500 futures are a popular choice, but the resulting portfolio is exposed to significant basis risk.  Besides, when the volatility curve flatten and inverts, as it did in spectacular fashion in February, the transition tends to happen very quickly, producing a substantial losses on the portfolio.  These may be temporary, if the volatility spike is small or short-lived, but as traders and investors discovered in the February drama, neither of these two desirable outcomes is guaranteed.  Indeed as I pointed out in an earlier post this turned out to be the largest ever two-day volatility surge in history.  The results for many hedge funds, especially in the quant sector were devastating, with several showing high single digit or double-digit losses for the month.

VIX_Spike_1

 

Over time, investors have become more familiar with the volatility space and have learned to be wary of strategies like volatility carry or option selling, where the returns look superficially attractive, until a market event occurs.  So what alternative approaches are available?

An Aggressive Approach to Volatility Trading

In my blog post Riders on the Storm  I described one such approach:  the Option Trader strategy on our Algo Trading Platform made a massive gain of 27% for the month of February and as a result strategy performance is now running at over 55% for 2018 YTD, while maintaining a Sharpe Ratio of 2.23.

Option Trader

 

The challenge with this style of volatility trading is that it requires a trader (or trading system) with a very strong stomach and an investor astute enough to realize that sizable drawdowns are in a sense “baked in” for this trading strategy and should be expected from time to time.  But traders are often temperamentally unsuited to this style of trading – many react by heading for the hills and liquidating positions at the first sign of trouble; and the great majority of investors are likewise unable to withstand substantial drawdowns, even if the eventual outcome is beneficial.

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The Market Timing Approach

So what alternatives are there?  One way of dealing with the problem of volatility spikes is simply to try to avoid them.  That means developing a strategy logic that step aside altogether when there is a serious risk of an impending volatility surge.  Market timing is easy to describe, but very hard to implement successfully in practice.  The VIX Swing Trader strategy on the Systematic Algotrading platform attempts to do just that, only trading when it judges it safe to do so. So, for example, it completely side-stepped the volatility debacle in August 2015, ending the month up +0.74%.  The strategy managed to do the same in February this year, finishing ahead +1.90%, a pretty creditable performance given how volatility funds performed in general.  One helpful characteristic of the strategy is that it trades the less-volatile mid-section of the volatility curve, in the form of the VelocityShares Daily Inverse VIX MT ETN (ZIV).  This ensures that the P&L swings are much less dramatic than for strategies exposed to the front end of the curve, as most volatility strategies are.

VIX Swing Trader1 VIX Swing Trader2

A potential weakness of the strategy is that it will often miss great profit opportunities altogether, since its primary focus is to keep investors out of trouble. Allied to this, the system may trade only a handful of times each month.  Indeed, if you look at the track record above you find find months in which the strategy made no trades at all. From experience, investors are almost as bad at sitting on their hands as they are at taking losses:  patience is not a highly regarded virtue in the investing community these days.  But if you are a cautious, patient investor looking for a source of uncorrelated alpha, this strategy may be a good choice. On the other hand, if you are looking for high returns and are willing to take the associated risks, there are choices better suited to your goals.

The Hedging Approach to Volatility Trading

A “middle ground” is taken in our Hedged Volatility strategy. Like the VIX Swing Trader this strategy trades VIX ETFs/ETNs, but it does so across the maturity table. What distinguishes this strategy from the others is its use of long call options in volatility products like the iPath S&P 500 VIX ST Futures ETN (VXX) to hedge the short volatility exposure in other ETFs in the portfolio.  This enables the strategy to trade much more frequently, across a wider range of ETF products and maturities, with the security of knowing that the tail risk in the portfolio is protected.  Consequently, since live trading began in 2016, the strategy has chalked up returns of over 53% per year, with a Sharpe Ratio of 2 and Sortino Ratio above 3.  Don’t be confused by the low % of trades that are profitable:  the great majority of these loss-making “trades” are in fact hedges, which one would expect to be losers, as most long options trades are.  What matters is the overall performance of the strategy.

Hedged Volatility

All of these strategies are available on our Systematic Algotrading Platform, which offers investors the opportunity to trade the strategies in their own brokerage account for a monthly subscription fee.

The Multi-Strategy Approach

The approach taken by the Systematic Volatility Strategy in our Systematic Strategies hedge fund again seeks to steer a middle course between risk and return.  It does so by using a meta-strategy approach that dynamically adjusts the style of strategy deployed as market conditions change.  Rather than using options (the strategy’s mandate includes only ETFs) the strategy uses leveraged ETFs to provide tail risk protection in the portfolio. The strategy has produced an average annual compound return of 38.54% since live trading began in 2015, with a Sharpe Ratio of 3.15:

Systematic Volatility Strategy 1 Page Tear Sheet June 2018

 

A more detailed explanation of how leveraged ETFs can be used in volatility trading strategies is given in an earlier post:

http://jonathankinlay.com/2015/05/investing-leveraged-etfs-theory-practice/

 

Conclusion:  Choosing the Investment Style that’s Right for You

There are different styles of volatility trading and the investor should consider carefully which best suits his own investment temperament.  For the “high risk” investor seeking the greatest profit the Option Trader strategy in an excellent choice, producing returns of +176% per year since live trading began in 2016.   At the other end of the spectrum, the VIX Swing trader is suitable for an investor with a cautious trading style, who is willing to wait for the right opportunities, i.e. ones that are most likely to be profitable.  For investors seeking to capitalize on opportunities in the volatility space, but who are concerned about the tail risk arising from major market corrections, the Hedge Volatility strategy offers a better choice.  Finally, for investors able to invest $250,000 or more, a hedge fund investment in our Systematic Volatility strategy offers the highest risk-adjusted rate of return.

Volatility ETF Trader – June 2017: +15.3%

The Volatility ETF Trader product is an algorithmic strategy that trades several VIX ETFs using statistical and machine learning algorithms.

We offer a version of the strategy on the Collective 2 site (see here for details) that the user can subscribe to for a very modest fee of only $149 per month.

The risk-adjusted performance of the Collective 2 version of the strategy is unlikely to prove as good as the product we offer in our Systematic Strategies Fund, which trades a much wider range of algorithmic strategies.  There are other important differences too:  the Fund’s Systematic Volatility Strategy makes no use of leverage and only trades intra-day, exiting all positions by market close.  So it has a more conservative risk profile, suitable for longer term investment.

The Volatility ETF Trader on Collective 2, on the other hand, is a highly leveraged, tactical strategy that trades positions overnight and holds them for periods of several days .  As a consequence, the Collective 2 strategy is far more risky and is likely to experience significant drawdowns.    Those caveats aside, the strategy returns have been outstanding:  +48.9% for 2017 YTD and a total of +107.8% from inception in July 2016.

You can find full details of the strategy, including a listing of all of the trades, on the Collective 2 site.

Subscribers can sign up for a free, seven day trial and thereafter they can choose to trade the strategy automatically in their own brokerage account.

 

VIX ETF Strategy June 2017

Algorithmic Trading on Collective 2


Regular readers will recall my mentioning out VIX Futures scalping strategy which we ran on the Collective2 site for a while:

 

VIX HFT Scalper

 

The strategy, while performing very well, proved difficult for subscribers to implement, given the latencies involved in routing orders via the Collective 2 web site.  So we began thinking about slower strategies that investors could follow more easily, placing less reliance on the fill rate for limit orders.

Our VIX ETF Trader strategy has been running on Collective 2 for several months now and is being traded successfully by several subscribers.  The performance so far has been quite good, with net returns of 58.9% from July 2016 and a Sharpe ratio over 2, which is not at all bad for a low frequency strategy.  The strategy enters and exits using a mix of  limit and stop orders, so although some slippage is incurred the trade entries and exits work much more smoothly overall.

Having let the strategy settle for several months trading only the ProShares Short VIX Short-Term Futures ETF (SVXY)we are now ready to ramp things up.  From today the strategy will also trade several other VIX ETF products including the VelocityShares Daily Inverse VIX ST ETN (XIV), ProShares Ultra VIX Short-Term Futures (UVXY) and VelocityShares Daily 2x VIX ST ETN (TVIX).  All of the trades in these products are entered and exited using market or stop orders, and so will be easy for subscribers to follow.  For now we are keeping the required account size pegged at $25,000 although we will review that going forward.  My guess is that a capital allocation should be more than sufficient to trade the product in the kind of size we use on the Collective 2 versions of the strategies, especially if the account uses portfolio margin rather than standard Reg-T.

With the addition of the new products to the portfolio mix, we anticipate the strategy Sharpe ratio with rise to over 3 in the year ahead.

 

 

VIX ETF Strategy

 

The advantage of using a site like Collective 2 from the investor’s viewpoint is that, firstly, you get to see a lot of different trading styles and investment strategies.  You can select the strategies in a wide range of asset classes that fit your own investment preferences and trade several of them live in your own brokerage account.  (Setting up your account for live trading is straightforward, as described on the C2 site).  A major advantage of investing this way is that it doesn’t entail the commitment of capital that is typically required for a hedge fund or managed account investment:  you can trade the strategies in much smaller size, to fit your budget.

From our perspective, we find it a useful way to showcase some of the strategies we trade in our hedge fund, so that if investors want to they can move up to more advanced, but similar investment products.  We plan to launch new strategies on Collective 2 in the near futures , including an equity portfolio strategy and a CTA futures strategy.

If you would like more information, contact us for further details.

 

Ethical Strategy Design

It isn’t often that you see an equity curve like the one shown below, which was produced by a systematic strategy built on 1-minute bars in the ProShares Ultra VIX Short-Term Futures ETF (UVXY):
Fig3

As the chart indicates, the strategy is very profitable, has a very high overall profit factor and a trade win rate in excess of 94%:

Fig4

 

FIG5

 

So, what’s not to like?  Well, arguably, one would like to see a strategy with a more balanced P&L, capable of producing profitable trades on the long as well as the short side. That would give some comfort that the strategy will continue to perform well regardless of whether the market tone is bullish or bearish. That said, it is understandable that the negative drift from carry in volatility futures, amplified by the leverage in the leveraged ETF product, makes it is much easier to make money by selling short.  This is  analogous to the long bias in the great majority of equity strategies, which relies on the positive drift in stocks.  My view would be that the short bias in the UVXY strategy is hardly a sufficient reason to overlook its many other very attractive features, any more than long bias is a reason to eschew equity strategies.

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This example is similar to one we use in our training program for proprietary and hedge fund traders, to illustrate some of the pitfalls of strategy development.  We point out that the strategy performance has held up well out of sample – indeed, it matches the in-sample performance characteristics very closely.  When we ask trainees how they could test the strategy further, the suggestion is often made that we use Monte-Carlo simulation to evaluate the performance across a wider range of market scenarios than seen in the historical data.  We do this by introducing random fluctuations into the ETF prices, as well as in the strategy parameters, and by randomizing the start date of the test period.  The results are shown below. As you can see, while there is some variation in the strategy performance, even the worst simulated outcome appears very benign.

 

Fig2

Around this point trainees, at least those inexperienced in trading system development, tend to run out of ideas about what else could be done to evaluate the strategy.  One or two will mention drawdown risk, but the straight-line equity curve indicates that this has not been a problem for the strategy in the past, while the results of simulation testing suggest that drawdowns are unlikely to be a significant concern, across a broad spectrum of market conditions.  Most trainees simply want to start trading the strategy as soon as possible (although the more cautious of them will suggest trading in simulation mode for a while).

As this point I sometimes offer to let trainees see the strategy code, on condition that they agree to trade the strategy with their own capital.   Being smart people, they realize something must be wrong, even if they are unable to pinpoint what the problem may be.  So the discussion moves on to focus in more detail the question of strategy risk.

A Deeper Dive into Strategy Risk

At this stage I point out to trainees that the equity curve shows the result from realized gains and losses. What it does not show are the fluctuations in equity that occurred before each trade was closed.

That information is revealed by the following report on the maximum adverse excursion (MAE), which plots the maximum drawdown in each trade vs. the final trade profit or loss.  Once trainees understand the report, the lights begin to come on.  We can see immediately that there were several trades which were underwater to the tune of $30,000, $50,000, or even $70,000 , or more, before eventually recovering to produce a profit.  In the most extreme case the trade was almost $80,000 underwater, before producing a profit of only a few hundred dollars. Furthermore, the drawdown period lasted for several weeks, which represents almost geological time for a strategy operating on 1-minute bars. It’s not hard to grasp the concept that risking $80,000 of your own money in order to make $250 is hardly an efficient use of capital, or an acceptable level of risk-reward.


FIG6 FIG7

 

FIG8

 

Next, I ask for suggestions for how to tackle the problem of drawdown risk in the strategy.   Most trainees will suggest implementing a stop-loss strategy, similar to those employed by thousands of  trading firms.  Looking at the MAE chart, it appears that we can avert the worst outcomes with a stop loss limit of, say, $25,000.  However, when we implement a stop loss strategy at this level, here’s the outcome it produces:

 

FIG9

Now we see the difficulty.  Firstly, what a stop-loss strategy does is simply crystallize the previously unrealized drawdown losses.  Consequently, the equity curve looks a great deal less attractive than it did before.  The second problem is more subtle: the conditions that produced the loss-making trades tend to continue for some time, perhaps as long as several days, or weeks.  So, a strategy that has a stop loss risk overlay will tend to exit the existing position, only to reinstate a similar position more or less immediately.  In other words, a stop loss achieves very little, other than to force the trader to accept losses that the strategy would have made up if it had been allowed to continue.  This outcome is a difficult one to accept, even in the face of the argument that a stop loss serves the purpose of protecting the trader (and his firm) from an even more catastrophic loss.  Because if the strategy tends to re-enter exactly the same position shortly after being stopped out, very little has been gained in terms of catastrophic risk management.

Luck and the Ethics of Strategy Design

What are the learning points from this exercise in trading system development?  Firstly, one should resist being beguiled by stellar-looking equity curves: they may disguise the true risk characteristics of the strategy, which can only be understood by a close study of strategy drawdowns and  trade MAE.  Secondly, a lesson that many risk managers could usefully take away is that a stop loss is often counter-productive, serving only to cement losses that the strategy would otherwise have recovered from.

A more subtle point is that a Geometric Brownian Motion process has a long-term probability of reaching any price level with certainty.  Accordingly, in theory one has only to wait long enough to recover from any loss, no matter how severe.   Of course, in the meantime, the accumulated losses might be enough to decimate the trading account, or even bring down the entire firm (e.g. Barings).  The point is,  it is not hard to design a system with a very seductive-looking backtest performance record.

If the solution is not a stop loss, how do we avoid scenarios like this one?  Firstly, if you are trading someone else’s money, one answer is: be lucky!  If you happened to start trading this strategy some time in 2016, you would probably be collecting a large bonus.  On the other hand, if you were unlucky enough to start trading in early 2017, you might be collecting a pink slip very soon.  Although unethical, when you are gambling with other people’s money, it makes economic sense to take such risks, because the potential upside gain is so much greater than the downside risk (for you). When you are risking with your own capital, however, the calculus is entirely different.  That is why we always trade strategies with our own capital before opening them to external investors (and why we insist that our prop traders do the same).

As a strategy designer, you know better, and should act accordingly.  Investors, who are relying on your skills and knowledge, can all too easily be seduced by the appearance of a strategy’s outstanding performance, overlooking the latent risks it hides.  We see this over and over again in option-selling strategies, which investors continue to pile into despite repeated demonstrations of their capital-destroying potential.  Incidentally, this is not a point about backtest vs. live trading performance:  the strategy illustrated here, as well as many option-selling strategies, are perfectly capable of producing live track records similar to those seen in backtest.  All you need is some luck and an uneventful period in which major drawdowns don’t arise.  At Systematic Strategies, our view is that the strategy designer is under an obligation to shield his investors from such latent risks, even if they may be unaware of them.  If you know that a strategy has such risk characteristics, you should avoid it, and design a better one.  The risk controls, including limitations on unrealized drawdowns (MAE) need to be baked into the strategy design from the outset, not fitted retrospectively (and often counter-productively, as we have seen here).

The acid test is this:  if you would not be prepared to risk your own capital in a strategy, don’t ask your investors to take the risk either.

The ethical principle of “do unto others as you would have them do unto you” applies no less in investment finance than it does in life.

Strategy Code

Code for UVXY Strategy

 

Crash-Proof Investing

As markets continue to make new highs against a backdrop of ever diminishing participation and trading volume, investors have legitimate reasons for being concerned about prospects for the remainder of 2016 and beyond, even without consideration to the myriad of economic and geopolitical risks that now confront the US and global economies. Against that backdrop, remaining fully invested is a test of nerves for those whose instinct is that they may be picking up pennies in front an oncoming steamroller.  On the other hand, there is a sense of frustration in cashing out, only to watch markets surge another several hundred points to new highs.

In this article I am going to outline some steps investors can take to match their investment portfolios to suit current market conditions in a way that allows them to remain fully invested, while safeguarding against downside risk.  In what follows I will be using our own Strategic Volatility Strategy, which invests in volatility ETFs such as the iPath S&P 500 VIX ST Futures ETN (NYSEArca:VXX) and the VelocityShares Daily Inverse VIX ST ETN (NYSEArca:XIV), as an illustrative example, although the principles are no less valid for portfolios comprising other ETFs or equities.

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Risk and Volatility

Risk may be defined as the uncertainty of outcome and the most common way of assessing it in the context of investment theory is by means of the standard deviation of returns.  One difficulty here is that one may never ascertain the true rate of volatility – the second moment – of a returns process; one can only estimate it.  Hence, while one can be certain what the closing price of a stock was at yesterday’s market close, one cannot say what the volatility of the stock was over the preceding week – it cannot be observed the way that a stock price can, only estimated.  The most common estimator of asset volatility is, of course, the sample standard deviation.  But there are many others that are arguably superior:  Log-Range, Parkinson, Garman-Klass to name but a few (a starting point for those interested in such theoretical matters is a research paper entitled Estimating Historical Volatility, Brandt & Kinlay, 2005).

Leaving questions of estimation to one side, one issue with using standard deviation as a measure of risk is that it treats upside and downside risk equally – the “risk” that you might double your money in an investment is regarded no differently than the risk that you might see your investment capital cut in half.  This is not, of course, how investors tend to look at things: they typically allocate a far higher cost to downside risk, compared to upside risk.

One way to address the issue is by using a measure of risk known as the semi-deviation.  This is estimated in exactly the same way as the standard deviation, except that it is applied only to negative returns.  In other words, it seeks to isolate the downside risk alone.

This leads directly to a measure of performance known as the Sortino Ratio.  Like the more traditional Sharpe Ratio, the Sortino Ratio is a measure of risk-adjusted performance – the average return produced by an investment per unit of risk.  But, whereas the Sharpe Ratio uses the standard deviation as the measure of risk, for the Sortino Ratio we use the semi-deviation. In other words, we are measuring the expected return per unit of downside risk.

There may be a great deal of variation in the upside returns of a strategy that would penalize the risk-adjusted returns, as measured by its Sharpe Ratio. But using the Sortino Ratio, we ignore the upside volatility entirely and focus exclusively on the volatility of negative returns (technically, the returns falling below a given threshold, such as the risk-free rate.  Here we are using zero as our benchmark).  This is, arguably, closer to the way most investors tend to think about their investment risk and return preferences.

In a scenario where, as an investor, you are particularly concerned about downside risk, it makes sense to focus on downside risk.  It follows that, rather than aiming to maximize the Sharpe Ratio of your investment portfolio, you might do better to focus on the Sortino Ratio.

 

Factor Risk and Correlation Risk

Another type of market risk that is often present in an investment portfolio is correlation risk.  This is the risk that your investment portfolio correlates to some other asset or investment index.  Such risks are often occluded – hidden from view – only to emerge when least wanted.  For example, it might be supposed that a “dollar-neutral” portfolio, i.e. a portfolio comprising equity long and short positions of equal dollar value, might be uncorrelated with the broad equity market indices.  It might well be.  On the other hand, the portfolio might become correlated with such indices during times of market turbulence; or it might correlate positively with some sector indices and negatively with others; or with market volatility, as measured by the CBOE VIX index, for instance.

Where such dependencies are included by design, they are not a problem;  but when they are unintended and latent in the investment portfolio, they often create difficulties.  The key here is to test for such dependencies against a variety of risk factors that are likely to be of concern.  These might include currency and interest rate risk factors, for example;  sector indices; or commodity risk factors such as oil or gold (in a situation where, for example, you are investing a a portfolio of mining stocks).  Once an unwanted correlation is identified, the next step is to adjust the portfolio holdings to try to eliminate it.  Typically, this can often only be done in the average, meaning that, while there is no correlation bias over the long term, there may be periods of positive, negative, or alternating correlation over shorter time horizons.  Either way, it’s important to know.

Using the Strategic Volatility Strategy as an example, we target to maximize the Sortino Ratio, subject also to maintaining very lows levels of correlation to the principal risk factors of concern to us, the S&P 500 and VIX indices. Our aim is to create a portfolio that is broadly impervious to changes in the level of the overall market, or in the level of market volatility.

 

One method of quantifying such dependencies is with linear regression analysis.  By way of illustration, in the table below are shown the results of regressing the daily returns from the Strategic Volatility Strategy against the returns in the VIX and S&P 500 indices.  Both factor coefficients are statistically indistinguishable from zero, i.e. there is significant no (linear) dependency.  However, the constant coefficient, referred to as the strategy alpha, is both positive and statistically significant.  In simple terms, the strategy produces a return that is consistently positive, on average, and which is not dependent on changes in the level of the broad market, or its volatility.  By contrast, for example, a commonplace volatility strategy that entails capturing the VIX futures roll would show a negative correlation to the VIX index and a positive dependency on the S&P500 index.

Regression

 

Tail Risk

Ever since the publication of Nassim Taleb’s “The Black Swan”, investors have taken a much greater interest in the risk of extreme events.  If the bursting of the tech bubble in 2000 was not painful enough, investors surely appear to have learned the lesson thoroughly after the financial crisis of 2008.  But even if investors understand the concept, the question remains: what can one do about it?

The place to start is by looking at the fundamental characteristics of the portfolio returns.  Here we are not such much concerned with risk, as measured by the second moment, the standard deviation. Instead, we now want to consider the third and forth moments of the distribution, the skewness and kurtosis.

Comparing the two distributions below, we can see that the distribution on the left, with negative skew, has nonzero probability associated with events in the extreme left of the distribution, which in this context, we would associate with negative returns.  The distribution on the right, with positive skew, is likewise “heavy-tailed”; but in this case the tail “risk” is associated with large, positive returns.  That’s the kind of risk most investors can live with.

 

skewness

 

Source: Wikipedia

 

 

A more direct measure of tail risk is kurtosis, literally, “heavy tailed-ness”, indicating a propensity for extreme events to occur.  Again, the shape of the distribution matters:  a heavy tail in the right hand portion of the distribution is fine;  a heavy tail on the left (indicating the likelihood of large, negative returns) is a no-no.

Let’s take a look at the distribution of returns for the Strategic Volatility Strategy.  As you can see, the distribution is very positively skewed, with a very heavy right hand tail.  In other words, the strategy has a tendency to produce extremely positive returns. That’s the kind of tail risk investors prefer.

SVS

 

Another way to evaluate tail risk is to examine directly the performance of the strategy during extreme market conditions, when the market makes a major move up or down. Since we are using a volatility strategy as an example, let’s take a look at how it performs on days when the VIX index moves up or down by more than 5%.  As you can see from the chart below, by and large the strategy returns on such days tend to be positive and, furthermore, occasionally the strategy produces exceptionally high returns.

 

Convexity

 

The property of producing higher returns to the upside and lower losses to the downside (or, in this case, a tendency to produce positive returns in major market moves in either direction) is known as positive convexity.

 

Positive convexity, more typically found in fixed income portfolios, is a highly desirable feature, of course.  How can it be achieved?    Those familiar with options will recognize the convexity feature as being similar to the concept of option Gamma and indeed, one way to produce such a payoff is buy adding options to the investment mix:  put options to give positive convexity to the downside, call options to provide positive convexity to the upside (or using a combination of both, i.e. a straddle).

 

In this case we achieve positive convexity, not by incorporating options, but through a judicious choice of leveraged ETFs, both equity and volatility, for example, the ProShares UltraPro S&P500 ETF (NYSEArca:UPRO) and the ProShares Ultra VIX Short-Term Futures ETN (NYSEArca:UVXY).

 

Putting It All Together

While we have talked through the various concepts in creating a risk-protected portfolio one-at-a-time, in practice we use nonlinear optimization techniques to construct a portfolio that incorporates all of the desired characteristics simultaneously. This can be a lengthy and tedious procedure, involving lots of trial and error.  And it cannot be emphasized enough how important the choice of the investment universe is from the outset.  In this case, for instance, it would likely be pointless to target an overall positively convex portfolio without including one or more leveraged ETFs in the investment mix.

Let’s see how it turned out in the case of the Strategic Volatility Strategy.

 

SVS Perf

 

 

Note that, while the portfolio Information Ratio is moderate (just above 3), the Sortino Ratio is consistently very high, averaging in excess of 7.  In large part that is due to the exceptionally low downside risk, which at 1.36% is less than half the standard deviation (which is itself quite low at 3.3%).  It is no surprise that the maximum drawdown over the period from 2012 amounts to less than 1%.

A critic might argue that a CAGR of only 10% is rather modest, especially since market conditions have generally been so benign.  I would answer that criticism in two ways.  Firstly, this is an investment that has the risk characteristics of a low-duration government bond; and yet it produces a yield many times that of a typical bond in the current low interest rate environment.

Secondly, I would point out that these results are based on use of standard 2:1 Reg-T leverage. In practice it is entirely feasible to increase the leverage up to 4:1, which would produce a CAGR of around 20%.  Investors can choose where on the spectrum of risk-return they wish to locate the portfolio and the strategy leverage can be adjusted accordingly.

 

Conclusion

The current investment environment, characterized by low yields and growing downside risk, poses difficult challenges for investors.  A way to address these concerns is to focus on metrics of downside risk in the construction of the investment portfolio, aiming for high Sortino Ratios, low correlation with market risk factors, and positive skewness and convexity in the portfolio returns process.

Such desirable characteristics can be achieved with modern portfolio construction techniques providing the investment universe is chosen carefully and need not include anything more exotic than a collection of commonplace ETF products.

Developing A Volatility Carry Strategy

By way of introduction we begin by reviewing a well known characteristic of the  iPath S&P 500 VIX ST Futures ETN (NYSEArca:VXX).  In common with other long-volatility ETF /ETNs, VXX has a tendency to decline in value due to the upward sloping shape of the forward volatility curve.  The chart below which illustrates the fall in value of the VXX, together with the front-month VIX futures contract, over the period from 2009.


VXXvsVX

 

 

This phenomenon gives rise to opportunities for “carry” strategies, wherein a long volatility product such as VXX is sold in expectation that it will decline in value over time.  Such strategies work well during periods when volatility futures are in contango, i.e. when the longer dated futures contracts have higher prices than shorter dated futures contracts and the spot VIX Index, which is typically the case around 70% of the time.  An analogous strategy in the fixed income world is known as “riding down the yield curve”.  When yield curves are upward sloping, a fixed income investor can buy a higher-yielding bill or bond in the expectation that the yield will decline, and the price rise, as the security approaches maturity.  Quantitative easing put paid to that widely utilized technique, but analogous strategies in currency and volatility markets continue to perform well.

The challenge for any carry strategy is what happens when the curve inverts, as futures move into backwardation, often giving rise to precipitous losses.  A variety of hedging schemes have been devised that are designed to mitigate the risk.  For example, one well-known carry strategy in VIX futures entails selling the front month contract and hedging with a short position in an appropriate number of E-Mini S&P 500 futures contracts. In this case the hedge is imperfect, leaving the investor the task of managing a significant basis risk.

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The chart of the compounded value of the VXX and VIX futures contract suggests another approach.  While both securities decline in value over time, the fall in the value of the VXX ETN is substantially greater than that of the front month futures contract.  The basic idea, therefore, is a relative value trade, in which we purchase VIX futures, the better performing of the pair, while selling the underperforming VXX.  Since the value of the VXX is determined by the value of the front two months VIX futures contracts, the hedge, while imperfect, is likely to entail less basis risk than is the case for the VIX-ES futures strategy.

Another way to think about the trade is this:  by combining a short position in VXX with a long position in the front-month futures, we are in effect creating a residual exposure in the value of the second month VIX futures contract relative to the first. So this is a strategy in which we are looking to capture volatility carry, not at the front of the curve, but between the first and second month futures maturities.  We are, in effect, riding down the belly of volatility curve.

 

The Relationship between VXX and VIX Futures

Let’s take a look at the relationship between the VXX and front month futures contract, which I will hereafter refer to simply as VX.  A simple linear regression analysis of VXX against VX is summarized in the tables below, and confirms two features of their relationship.

Firstly there is a strong, statistically significant relationship between the two (with an R-square of 75% ) – indeed, given that the value of the VXX is in part determined by VX, how could there not be?

Secondly, the intercept of the regression is negative and statistically significant.  We can therefore conclude that the underperformance of the VXX relative to the VX is not just a matter of optics, but is a statistically reliable phenomenon.  So the basic idea of selling the VXX against VX is sound, at least in the statistical sense.

Regression

 

 

Constructing the Initial Portfolio

In constructing our theoretical portfolio, I am going to gloss over some important technical issues about how to construct the optimal hedge and simply assert that the best one can do is apply a beta of around 1.2, to produce the following outcome:

Table1

VXX-VX Strategy

 

While broadly positive, with an information ratio of 1.32, the strategy performance is a little discouraging, on several levels.  Firstly, the annual volatility, at over 48%, is uncomfortably high. Secondly, the strategy experiences very substantial drawdowns at times when the volatility curve inverts, such as in August 2015 and January 2016.  Finally, the strategy is very highly correlated with the S&P500 index, which may be an important consideration for investors looking for ways to diversity their stock portfolio risk.

 

Exploiting Calendar Effects

We will address these issues in short order.  Firstly, however, I want to draw attention to an interesting calendar effect in the strategy (using a simple pivot table analysis).

Calendar

As you can see from the table above, the strategy returns in the last few days of the calendar month tend to be significantly below zero.

The cause of the phenomenon has to do with the way the VXX is constructed, but the important point here is that, in principle, we can utilize this effect to our advantage, by reversing the portfolio holdings around the end of the month.  This simple technique produces a significant improvement in strategy returns, while lowering the correlation:

Table2

 

Reducing Portfolio Risk and Correlation

We can now address the issue of the residual high level of strategy volatility, while simultaneously reducing the strategy correlation to a much lower level.  We can do this in a straightforward way by adding a third asset, the SPDR S&P 500 ETF Trust (NYSEArca:SPY), in which we will hold a short position, to exploit the negative correlation of the original portfolio.

We then adjust the portfolio weights to maximize the risk-adjusted returns, subject to limits on the maximum portfolio volatility and correlation.  For example, setting a limit of 10% for both volatility and correlation, we achieve the following result (with weights -0.37 0.27 -0.65 for VXX, VX and SPY respectively):

 

Table3

 

 

VXX-VX-SPY

 

Compared to the original portfolio, the new portfolio’s performance is much more benign during the critical period from Q2-2015 to Q1-2016 and while there remain several significant drawdown periods, notably in 2011, overall the strategy is now approaching an investable proposition, with an information ratio of 1.6 and annual volatility of 9.96% and correlation of 0.1.

Other configurations are possible, of course, and the risk-adjusted performance can be improved, depending on the investor’s risk preferences.

 

Portfolio Rebalancing

There is an element of curve-fitting in the research process as described so far, in as much as we are using all of the available data to July 2016 to construct a portfolio with the desired characteristics. In practice, of course, we will be required to rebalance the portfolio on a periodic basis, re-estimating the optimal portfolio weights as new data comes in.  By way of illustration, the portfolio was re-estimated using in-sample data to the end of Feb, 2016, producing out-of-sample results during the period from March to July 2016, as follows:

Table4

 

A detailed examination of the generic problem of how frequently to rebalance the portfolio is beyond the scope of this article and I leave it to interested analysts to perform the research for themselves.

 

Practical Considerations

In order to implement the theoretical strategy described above there are several important practical steps that need to be considered.

 

  • It is not immediately apparent how the weights should be applied to a portfolio comprising both ETNs and futures. In practice the best approach is to re-estimate the portfolio using a regression relationship expressed in $-value terms, rather than in percentages, in order to establish the quantity of VXX and SPY stock to be sold per single VX futures contract.
  • Reversing the portfolio holdings in the last few days of the month will add significantly to transaction costs, especially for the position in VX futures, for which the minimum tick size is $50. It is important to factor realistic estimates of transaction costs into the assessment of the strategy performance overall and specifically with respect to month-end reversals.
  • The strategy assumed  the availability of VXX and SPY to short, which occasionally can be a problem. It’s not such a big deal if you are maintaining a long-term short position, but flipping the position around over a few ays at the end of the month might be problematic, from time to time.
  • Also, we should take account of stock loan financing costs, which run to around 2.9% and 0.42% annually for VXX and SPY, respectively. These rates can vary with market conditions and stock availability, of course.
  • It is highly likely that other ETFs/ETNs could profitably be added to the mix in order to further reduce strategy volatility and improve risk-adjusted returns. Likely candidates could include, for example, the Direxion Daily 20+ Yr Trsy Bull 3X ETF (NYSEArca:TMF).
  • We have already mentioned the important issue of portfolio rebalancing. There is an argument for rebalancing more frequently to take advantage of the latest market data; on the other hand, too-frequent changes in the portfolio composition can undermine portfolio robustness, increase volatility and incur higher transaction costs. The question of how frequently to rebalance the portfolio is an important one that requires further testing to determine the optimal rebalancing frequency.

 

Conclusion

We have described the process of constructing a volatility carry strategy based on the relative value of the VXX ETN vs the front-month contract in VIX futures.  By combining a portfolio comprising short positions in VXX and SPY with a long position in VIX futures, the investor can, in principle achieve risk-adjusted returns corresponding to an information ratio of around 1.6, or more. It is thought likely that further improvements in portfolio performance can be achieved by adding other ETFs to the portfolio mix.

 

ETFs vs. Hedge Funds – Why Not Combine Both?

Grace Kim, Brand Director at DarcMatter, does a good job of setting out the pros and cons of ETFs vs hedge funds for the family office investor in her LinkedIn post.

She points out that ETFs now offer as much liquidity as hedge funds, both now having around $2.96 trillion in assets.  So, too, are her points well made about the low cost, diversification and ease of investing in ETFs compared to hedge funds.

But, of course, the point of ETF investing is to mimic the return in some underlying market – to gain beta exposure, in the jargon – whereas hedge fund investing is all about alpha – the incremental return that is achieved over and above the return attributable to market risk factors.

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But should an investor be forced to choose between the advantages of diversification and liquidity of ETFs on the one hand and the (supposedly) higher risk-adjusted returns of hedge funds, on the other?  Why not both?

Diversified Long/Short ETF Strategies

In fact, there is nothing whatever to prevent an investment strategist from constructing a hedge fund strategy using ETFs.  Just as one can enjoy the hedging advantages of a long/short equity hedge fund portfolio, so, too, can one employ the same techniques to construct long/short ETF portfolios.  Compared to a standard equity L/S portfolio, an ETF L/S strategy can offer the added benefit of exposure to (or hedge against) additional risk factors, including currency, commodity or interest rate.

For an example of this approach ETF long/short portfolio construction, see my post on Developing Long/Short ETF Strategies.  As I wrote in that article:

My preference for ETFs is due primarily to the fact that  it is easier to achieve a wide diversification in the portfolio with a more limited number of securities: trading just a handful of ETFs one can easily gain exposure, not only to the US equity market, but also international equity markets, currencies, real estate, metals and commodities.

More Exotic Hedge Fund Strategies with ETFs

But why stop at vanilla long/short strategies?  ETFs are so varied in terms of the underlying index, leverage and directional bias that one can easily construct much more sophisticated strategies capable of tapping the most obscure sources of alpha.

Take our very own Volatility ETF strategy for example.  The strategy constructs hedged positions, not by being long/short, but by being short/short or long/long volatility and inverse volatility products, like SVXY and UVXY, or VXX and XIV.  The strategy combines not only strategic sources of alpha that arise from factors such as convexity in the levered ETF products, but also short term alpha signals arising from temporary misalignments in the relative value of comparable ETF products.  These can be exploited by tactical, daytrading algorithms of a kind more commonly applied in the context of high frequency trading.

For more on this see for example Investing in Levered ETFs – Theory and Practice.

Does the approach work?  On the basis that a picture is worth a thousand words, let me answer that question as follows:

Systematic Strategies Volatility ETF Strategy

Perf Summary Dec 2015

Conclusion

There is no reason why, in considering the menu of ETF and hedge fund strategies, it should be a case of either-or.  Investors can combine the liquidity, cost and diversification advantages of ETFs with the alpha generation capabilities of well-constructed hedge fund strategies.

How to Make Money in a Down Market

The popular VIX blog Vix and More evaluates the performance of the VIX ETFs (actually ETNs) and concludes that all of them lost money in 2015.  Yes, both long volatility and short volatility products lost money!

VIX ETP performance in 2015

Source:  Vix and More

By contrast, our Volatility ETF strategy had an exceptional year in 2015, making money in every month but one:

Monthly Pct Returns

How to Profit in a Down Market

How do you make money when every product you are trading loses money?  Obviously you have to short one or more of them.  But that can be a very dangerous thing to do, especially in a product like the VIX ETNs.  Volatility itself is very volatile – it has an annual volatility (the volatility of volatility, or VVIX) that averages around 100% and which reached a record high of 212% in August 2015.

VVIX

The CBOE VVIX Index

Selling products based on such a volatile instrument can be extremely hazardous – even in a downtrend: the counter-trends are often extremely violent, making a short position challenging to maintain.

Relative value trading is a more conservative approach to the problem.  Here, rather than trading a single product you trade a pair, or basket of them.  Your bet is that the ETFs (or stocks) you are long will outperform the ETFs you are short.  Even if your favored ETFs declines, you can still make money if the ETFs you short declines even more.

This is the basis for the original concept of hedge funds, as envisaged by Alfred Jones in the 1940’s, and underpins the most popular hedge fund strategy, equity long-short.  But what works successfully in equities can equally be applied to other markets, including volatility.  In fact, I have argued elsewhere that the relative value (long/short) concept works even better in volatility markets, chiefly because the correlations between volatility processes tend to be higher than the correlations between the underlying asset processes (see The Case for Volatility as an Asset Class).

 

Portfolio Improvement for the Equity Investor

Portfolio

Equity investors and long-only portfolio managers are constantly on the lookout for ways to improve their portfolios, either by yield enhancement, or risk reduction.  In the case of yield enhancement, the principal focus is on adding alpha to the portfolio through stock selection and active management, while risk reduction tends to be accomplished through diversification.

Another approach is to seek improvement by adding investments outside the chosen universe of stocks, while remaining within the scope of the investment mandate (which, for instance, may include equity-related products, but not futures or options).  The advent of volatility products in the mid-2000’s offered new opportunities for risk reduction; but this benefit was typically achieved at the cost of several hundred basis points in yield.  Over the last decade, however, a significant evolution has taken place in volatility strategies, such that they can now not only provide insurance for the equity portfolio, but, in addition, serve as an orthogonal source of alpha to enhance portfolio yields.

An example of one such product is our volatility strategy, a quantitative approach to trading VIX-related ETF products traded on ARCA. A summary of the performance of the strategy is given below.

Vol Strategy perf Sept 2015

The mechanics of the strategy are unlikely to be of great interest to the typical equity investor and so need not detain us here.  Rather, I want to focus on how an investor can use such products to enhance their equity portfolio.

Performance of the Equity Market and Individual Sectors

The last five years have been extremely benign for the equity market, not only for the broad market, as evidenced by the performance of the SPDR S&P 500 Trust ETF (SPY), and also by almost every individual sector, with the notable exception of energy.

Sector ETF Performance 2012-2015

The risk-adjusted returns have been exceptional over this period, with information ratios reaching 1.4 or higher for several of the sectors, including Financials, Consumer Staples, Healthcare and Consumer Discretionary.  If the equity investor has been in a position to diversify his portfolio as fully as the SPY ETF, it might reasonably been assumed that he has accomplished the maximum possible level of risk reduction; at the same time, no-one is going to argue with a CAGR of 16.35%.  Yet, even here, portfolio improvement is possible.

Yield Enhancement

The key to improving the portfolio yield lies in the superior risk-adjusted performance of the volatility portfolio compared to the equity portfolio and also due the fact that, while the correlation between the two is significant (at 0.44), it is considerably lower than 1.  Hence there is potential for generating higher rates of return on a risk-adjusted basis by combining the pair of portfolios in some proportion.

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To illustrate this we assume, firstly, that the investor is comfortable with the currently level of risk in his broadly diversified equity portfolio, as measured by the annual standard deviation of returns, currently 10.65%.   Holding this level of risk constant, we now introduce an overlay strategy, namely the volatility portfolio, to which we seek to allocate some proportion of the available investment capital.  With this constraint it turns out that we can achieve a substantial improvement in the overall yield by reducing our holding in the equity portfolio to just over 2/3 of the current level (67.2%) and allocating 32.8% of the capital to the volatility portfolio.  Over the period from 2012, the combined equity and volatility portfolio produced a CAGR of 26.83%, but with the same annual standard deviation – a yield enhancement of 10.48% annually.  The portfolio Information Ratio improves from 1.53 to a 2.52, reflecting the much higher returns produced by the combined portfolio, for the same level of risk as before.

Chart

Risk Reduction

The given example may appear impressive, but it isn’t really a practical proposition.  Firstly, no equity investor or portfolio manager is likely to want to allocate 1/3 of their total capital to a strategy operated by a third party, no matter how impressive the returns. Secondly, the capacity in the volatility strategy is, realistically, of the order of $100 million.  A 32.8% allocation of capital from a sizeable equity portfolio would absorb a large proportion of the available capacity in the volatility ETF strategy, or even all of it.

A much more realistic approach would be to cap the allocation to the volatility component at a reasonable level – say, 5%.  Then the allocation from a $100M capital budget would be $5M, well within the capacity constraints of the volatility product.  In fact, operating at this capped allocation percentage, the volatility strategy provides capacity for equity portfolios of up to $2Bn in total capital.

Let’s look at an example of what can be achieved under a 5% allocation constraint.  In this scenario I am going to move along the second axis of portfolio improvement – risk reduction.  Here, we assume that we wish to maintain the current level of performance of the equity portfolio (CAGR 16.35%), while reducing the risk as much as possible.

A legitimate question at this stage would be to ask how it might be possible to reduce risk by introducing a new investment that has a higher annual standard deviation than the existing portfolio?  The answer is simply that we move some of our existing investment into cash (or, rather, Treasury securities).  In fact, by allocating the maximum allowed to the volatility portfolio (5%) and reducing our holding in the equity portfolio to 85.8% of the original level (with the remaining 9.2% in cash), we are able to create a portfolio with the same CAGR but with an annual volatility in single digits: 9.53%, a reduction in risk of  112 basis points annually.  At the same time, the risk adjusted performance of the portfolio improves from 1.53 to 1.71 over the period from 2012.

Of course, the level of portfolio improvement is highly dependent on the performance characteristics of both the equity portfolio and overlay strategy, as well as the correlation between them. To take a further example, if we consider an equity portfolio mirroring the characteristics of the Materials Select Sector SPDR ETF (XLB), we can achieve a reduction of as much as 3.31% in the annual standard deviation, without any loss in expected yield, through an allocation of 5% to the volatility overlay strategy and a much higher allocation of 18% to cash.

Other Considerations

Investors and money managers being what they are, it goes against the grain to consider allocating money to a third party – after all, a professional money manager earns his living from his own investment expertise, rather than relying on others.  Yet no investor can reasonably expect to achieve the same level of success in every field of investment.  If you have built your reputation on your abilities as a fundamental analyst and stock picker, it is unreasonable to expect that you will be able accomplish as much in the arena of quantitative investment strategies.  Secondly, by capping the allocation to an external manager at the level of 5% to 10%, your primary investment approach remains unaltered –  you are maintaining the fidelity of your principal investment thesis and investment mandate.  Thirdly, there is no reason why overlay strategies such as the one discussed here should not provide easy liquidity terms – after all, the underlying investments are liquid, exchange traded products. Finally, if you allocate capital in the form of a managed account you can maintain control over the allocated capital and make adjustments rapidly, as your investment needs change.

Conclusion

Quantitative strategies have a useful role to play for equity investors and portfolio managers as a means to improve existing portfolios, whether by yield enhancement, risk reduction, or a combination of the two.  While the level of improvement is highly dependent on the performance characteristics of the equity portfolio and the overlay strategy, the indications are that yield enhancement, or risk reduction, of the order of hundreds of basis points may be achievable even through very modest allocations of capital.