Hedge Funds Archives - QUANTITATIVE RESEARCH AND TRADING

December 1, 2020December 1, 2020

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).

August 11, 2020

Career Opportunity for Quant Traders

Career Opportunity for Quant Traders as Strategy Managers

We are looking for 3-4 traders (or trading teams) to showcase as Strategy Managers on our Algorithmic Trading Platform. Ideally these would be systematic quant traders, since that is the focus of our fund (although they don’t have to be). So far the platform offers a total of 10 strategies in equities, options, futures and f/x. Five of these are run by external Strategy Managers and five are run internally.

The goal is to help Strategy Managers build a track record and gain traction with a potential audience of over 100,000 members. After a period of 6-12 months we will offer successful managers a position as a PM at Systematic Strategies and offer their strategies in our quantitative hedge fund. Alternatively, we will assist the manager is raising external capital in order to establish their own fund.

If you are interested in the possibility (or know a talented rising star who might be), details are given below.

March 10, 2020

Capitalizing on the Coming Market Crash

Long-Only Equity Investors

Recently I have been discussing possible areas of collaboration with an RIA contact on LinkedIn, who also happens to be very familiar with the hedge fund world. He outlined the case of a high net worth investor in equities (long only), who wanted to remain invested, but was becoming increasingly concerned about the prospects for a significant market downturn, or even a market crash, similar to those of 2000 or 2008.

I am guessing he is not alone: hardly a day goes by without the publication of yet another article sounding a warning about stretched equity valuations and the dangerously elevated level of the market.

The question put to me was, what could be done to reduce the risk in the investor’s portfolio?

Typically, conservative investors would have simply moved more of their investment portfolio into fixed income securities, but with yields at such low levels this is hardly an attractive option today. Besides, many see the bond market as representing an even more extreme bubble than equities currently.

Hedging Strategies

The problem with traditional hedging mechanisms such as put options, for example, is that they are relatively expensive and can easily reduce annual returns from the overall portfolio by several hundred basis points. Even at current low level of volatility the performance drag is noticeable, since the potential upside in the equity portfolio is also lower than it has been for some time. A further consideration is that many investors are not mandated – or are simply reluctant – to move beyond traditional equity investing into complex ETF products or derivatives.

An equity long/short hedge fund product is one possible solution, but many equity investors are reluctant to consider shorting stocks under any circumstances, even for hedging purposes. And while a short hedge may provide some downside protection it is unlikely to fully safeguard the investor in a crash scenario. Furthermore, the cost of a hedge fund investment is typically greater than for a long-only product, entailing the payment of a performance fee in addition to management fees that are often higher than for standard investment products.

The Ideal Investment Strategy

Given this background, we can say that the ideal investment strategy is one that:

Invests long-only in equities
Is inexpensive to implement (reasonable management fees; no performance fees)
Does not require shorting stocks, or expensive hedging mechanisms such as options
Makes acceptable returns during both bull and bear markets
Is likely to produce positive returns in a market crash scenario

A typical buy-and-hold approach is unlikely to meet only the first three requirements, although an argument could be made that a judicious choice of defensive stocks might enable the investment portfolio to generate returns at an “acceptable” level during a downturn (without being prescriptive as to the precise meaning of that term may be). But no buy-and-hold strategy could ever be expected to prosper during times of severe market stress. A more sophisticated approach is required.

Market Timing

Market timing is regarded as a “holy grail” by some quantitative strategists. The idea, simply, is to increase or reduce risk exposure according to the prospects for the overall market. For a very long time the concept has been dismissed as impossible, by definition, given that markets are mostly efficient. But analysts have persisted in the attempt to develop market timing techniques, motivated by the enormous benefits that a viable market timing strategy would bring. And gradually, over time, evidence has accumulated that the market can be timed successfully and profitably. The rate of progress has accelerated in the last decade by the considerable advances in computing power and the development of machine learning algorithms and application of artificial intelligence to investment finance.

I have written several articles on the subject of market timing that the reader might be interested to review (see below). In this article, however, I want to focus firstly on the work on another investment strategist, Blair Hull.

http://jonathankinlay.com/2014/07/how-to-bulletproof-your-portfolio/

http://jonathankinlay.com/2014/07/enhancing-mutual-fund-returns-with-market-timing/

The Hull Tactical Fund

Blair Hull rose to prominence in the 1980’s and 1990’s as the founder of the highly successful quantitative option market making firm, the Hull Trading Company which at one time moved nearly a quarter of the entire daily market volume on some markets, and executed over 7% of the index options traded in the US. The firm was sold to Goldman Sachs at the peak of the equity market in 1999, for a staggering $531 million.

Blair used the capital to establish the Hull family office, Hull Investments, and in 2013 founded an RIA, Hull Tactical Asset Allocation LLC. The firm’s investment thesis is firmly grounded in the theory of market timing, as described in the paper “A Practitioner’s Defense of Return Predictability”, authored by Blair Hull and Xiao Qiao, in which the issues and opportunities of market timing and return predictability are explored.

In 2015 the firm launched The Hull Tactical Fund (NYSE Arca: HTUS), an actively managed ETF that uses quantitative trading model to take long and short positions in ETFs that seek to track the performance of the S&P 500, as well as leveraged ETFs or inverse ETFs that seek to deliver multiples, or the inverse, of the performance of the S&P 500. The goal to achieve long-term growth from investments in the U.S. equity and Treasury markets, independent of market direction.

How well has the Hull Tactical strategy performed? Since the fund takes the form of an ETF its performance is a matter in the public domain and is published on the firm’s web site. I reproduce the results here, which compare the performance of the HTUS ETF relative to the SPDR S&P 500 ETF (NYSE Arca: SPY):

Although the HTUS ETF has underperformed the benchmark SPY ETF since launching in 2015, it has produced a higher rate of return on a risk-adjusted basis, with a Sharpe ratio of 1.17 vs only 0.77 for SPY, as well as a lower drawdown (-3.94% vs. -13.01%). This means that for the same “risk budget” as required to buy and hold SPY, (i.e. an annual volatility of 13.23%), the investor could have achieved a total return of around 36% by using margin funds to leverage his investment in HTUS by a factor of 2.8x.

How does the Hull Tactical team achieve these results? While the detailed specifics are proprietary, we know from the background description that market timing (and machine learning concepts) are central to the strategy and this is confirmed by the dynamic level of the fund’s equity exposure over time:

A Long-Only, Crash-Resistant Equity Strategy

A couple of years ago I and my colleagues carried out an investigation of long-only equity strategies as part of a research project. Our primary focus was on index replication, but in the course of our research we came up with a methodology for developing long-only strategies that are highly crash-resistant.

The performance of our Long-Only Market Timing strategy is summarized below and compared with the performance of the HTUS ETF and benchmark SPY ETF (all results are net of fees). Over the period from inception of the HTUS ETF, our LOMT strategy produced a higher total return than HTUS (22.43% vs. 13.17%), higher CAGR (10.07% vs. 6.04%), higher risk adjusted returns (Sharpe Ratio 1.34 vs 1.21) and larger annual alpha (6.20% vs 4.25%). In broad terms, over this period the LOMT strategy produced approximately the same overall return as the benchmark SPY ETF, but with a little over half the annual volatility.

Application of Artificial Intelligence to Market Timing

Like the HTUS ETF, our LOMT strategy operates with very low fees, comparable to an ETF product rather than a hedge fund (1% management fee, no performance fees). Again, like the HTUS ETF our LOMT products makes no use of leverage. However, unlike HTUS it avoids complicated (and expensive) inverse or leveraged ETF products and instead invests only in two assets – the SPY ETF and 91-day US Treasury Bills. In other words, the LOMT strategy is a pure market timing strategy, moving capital between the SPY ETF and Treasury Bills depending on its forecast of future market performance. These forecasts are derived from machine learning algorithms that are specifically tuned to minimize the downside risk in the investment portfolio. This not only makes strategy returns less volatile, but also ensures that the strategy is very robust to market downturns.

In fact, even better than that, not only does the LOMT strategy tend to avoid large losses during periods of market stress, it is capable of capitalizing on the opportunities that more volatile market conditions offer. Looking at the compounded returns (net of fees) over the period from 1994 (the inception of the SPY ETF) we see that the LOMT strategy produces almost double the total profit of the SPY ETF, despite several years in which it underperforms the benchmark. The reason is clear from the charts: during the periods 2000-2002 and again in 2008, when the market crashed and returns in the SPY ETF were substantially negative, the LOMT strategy managed to produce positive returns. In fact, the banking crisis of 2008 provided an exceptional opportunity for the LOMT strategy, which in that year managed to produce a return nearing +40% at a time when the SPY ETF fell by almost the same amount!

Long Volatility Strategies

I recall having a conversation with Nassim Taleb, of Black Swan fame, about his Empirica fund around the time of its launch in the early 2000’s. He explained that his analysis had shown that volatility was often underpriced due to an under-estimation of tail risk, which the fund would seek to exploit by purchasing cheap out-of-the-money options. My response was that this struck me a great idea for an insurance product, but not a hedge fund – his investors, I explained, were going to hate seeing month after month of negative returns and would flee the fund. By the time the big event occurred there wouldn’t be sufficient AUM remaining to make up the shortfall. And so it proved.

A similar problem arises from most long-volatility strategies, whether constructed using options, futures or volatility ETFs: the combination of premium decay and/or negative carry typically produces continuing losses that are very difficult for the investor to endure.

Conclusion

What investors have been seeking is a strategy that can yield positive returns during normal market conditions while at the same time offering protection against the kind of market gyrations that typically decimate several years of returns from investment portfolios, such as we saw after the market crashes in 2000 and 2008. With the new breed of long-only strategies now being developed using machine learning algorithms, it appears that investors finally have an opportunity to get what they always wanted, at a reasonable price.

And just in time, if the prognostications of the doom-mongers turn out to be correct.

Contact Hull Tactical

Contact Systematic Strategies

February 25, 2020

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.

January 7, 2020January 7, 2020

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:

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.

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.

December 24, 2019December 24, 2019

The New Long/Short Equity

High Frequency Trading Strategies

One of the benefits of high frequency trading strategies lies in their ability to produce risk-adjusted rates of return that are unmatched by anything that the hedge fund or CTA community is capable of producing. With such performance comes another attractive feature of HFT firms – their ability to make money (almost) every day. Of course, HFT firms are typically not required to manage billions of dollars, which is just as well given the limited capacity of most HFT strategies. But, then again, with a Sharpe ratio of 10, who needs outside capital? This explains why most investors have a difficult time believing the level of performance achievable in the high frequency world – they never come across such performance, because HFT firms generally have little incentive to show their results to external investors.

By and large, HFT strategies remain the province of proprietary trading firms that can afford to make an investment in low-latency trading infrastructure that far exceeds what is typically required for a regular trading or investment management firm. However, while the highest levels of investment performance lie beyond the reach of most investors and money managers, it is still possible to replicate some of the desirable characteristics of high frequency strategies.

Quantitative Equity Strategy

I am going to use an example our Quantitative Equity strategy, which forms part of the Systematic Strategies hedge fund. The tables and charts below give a broad impression of the performance characteristics of the strategy, which include a CAGR of 14.85% (net of fees) since live trading began in 2013.

This is a strategy that is designed to produce returns on a par with the S&P 500 index, but with considerably lower risk: at just over 4%, the annual volatility of the strategy is only around 1/3 that of the index, while the maximum drawdown has been a little over 2% since inception. This level of portfolio risk is much lower than can typically be achieved in an equity long/short strategy (equity market neutral is another story, of course). Furthermore, the realized information ratio of 3.4 is in the upper 1%-tile of risk-adjusted performance amongst equity long/short strategies. So something rather more interesting must be going on that is very different from the typical approach to long/short equity.

One plausible explanation is that the strategy is exploiting some minor market anomaly that works fine for small amounts of capital, but which cannot be scaled. But this is not the case here: the investment universe comprises more than a hundred of the most liquid stocks in US markets, across a broad spectrum of sectors. And while single-name investment is capped at 10% of average daily volume, this nonetheless provides investment capacity of several hundreds of millions of dollars.

Nor does the reason for the exceptional performance lie in some new portfolio construction technique: rather, we rely on a straightforward 1/n allocation. Again, neither is factor exposure the driver of strategy alpha: as the factor loading table illustrates, strategy performance is largely uncorrelated with most market indices. It loads significantly on only large cap value, chiefly because the investment universe is defined as comprising the stocks with greatest liquidity (which tend to be large cap value), and on the CBOE VIX index. The positive correlation with market volatility is a common feature of many types of trading strategy that tend to do better in volatile markets, when short-term investment opportunities are plentiful.

While the detail of the strategy must necessarily remain proprietary, I can at least offer some insight that will, I hope, provide food for thought.

We can begin by comparing the returns for two of the stocks in the portfolio, Home Depot and Pfizer. The charts demonstrate one of important strategy characteristic: not every stock is traded at the same frequency. Some stocks might be traded once or twice a month; others possibly ten times a day, or more. In other words, the overall strategy is diversified significantly, not only across assets, but also across investment horizons. This has a considerable impact on volatility and downside risk in the portfolio.

Home Depot vs. Pfizer Inc.

Overall, the strategy trades an average of 40-60 times a day, or more. This is, admittedly, towards the low end of the frequency spectrum of HFT strategies – we might describe it as mid-frequency rather than high frequency trading. Nonetheless, compared to traditional long/short equity strategies this constitutes a high level of trading activity which, in aggregate, replicates some of the time-diversification benefits of HFT strategies, producing lower strategy volatility.

There is another way in which the strategy mimics, at least partially, the characteristics of a HFT strategy. The profitability of many (although by no means all) HFT strategies lies in their ability to capture (or, at least, not pay) the bid-offer spread. That is why latency is so crucial to most HFT strategies – if your aim is to to earn rebates, and/or capture the spread, you must enter and exit, passively, often using microstructure models to determine when to lean on the bid or offer price. That in turn depends on achieving a high priority for your orders in the limit order book, which is a function of latency – you need to be near the top of the queue at all times in order the achieve the required fill rate.

How does that apply here? While we are not looking to capture the spread, the strategy does seek to avoid taking liquidity and paying the spread. Where it can do so, it will offset the bid-offer spread by earning rebates. In many cases we are able to mitigate the spread cost altogether. So, while it cannot accomplish what a HFT market-making system can achieve, it can mimic enough of its characteristics – even at low frequency – to produce substantial gains in terms of cost-reduction and return enhancement. This is important since the transaction volume and portfolio turnover in this approach are significantly greater than for a typical equity long/short strategy.

Portfolio of Strategies vs. Portfolio of Equities

But this feature, while important, is not really the heart of the matter. Rather, the central point is this: that the overall strategy is an assembly of individual, independent strategies for each component stock. And it turns out that the diversification benefit of a portfolio of strategies is generally far greater than for an equal number of stocks, because the equity processes themselves will typically be correlated to a far greater degree than will corresponding trading strategies. To take the example of the pair of stocks discussed earlier, we find that the correlation between HD and PFE over the period from 2013 to 2017 is around 0.39, based on daily returns. By comparison, the correlation between the strategies for the two stocks over the same period is only 0.01.

This is generally the case, so that a portfolio of, say, 30 equity strategies, might reasonably be expected to enjoy a level of risk that is perhaps as much as one half that of a portfolio of the underlying stocks, no matter how constructed. This may be due to diversification in the time dimension, coupled with differences in the alpha generation mechanisms of the underlying strategies – mean reversion vs. momentum, for example

Strategy Robustness Testing

There are, of course, many different aspects to our approach to strategy risk management. Some of these are generally applicable to strategies of all varieties, but there are others that are specific to this particular type of strategy.

A good example of the latter is how we address the issue of strategy robustness. One of the principal concerns that investors have about quantitive strategies is that they may under-perform during adverse market conditions, or even simply stop working altogether. Our approach is to stress test each of the sub-strategy models using Monte Carlo simulation and examine their performance under a wide range of different scenarios, many of which have never been seen in the historical data used to construct the models.

For instance, we typically allow prices to fluctuate randomly by +/- 30% from historical values. But we also randomize the start date of each strategy by up to a year, which reduces the likelihood of a strategy being selected simply on the strength of a lucky start. Finally, we are interested in ensuring that the performance of each sub-strategy is not overly sensitive to the specific parameter values chosen for each model. Again, we test this using Monte Carlo, assessing the performance of each sub-strategy if the parameter values of the model are varied randomly by up to 30%.

The output of all these simulation tests is compiled into a histogram of performance results, from which we select the worst 5%-tile. Only if the worst outcomes – the 1-in-20 results in the left tail of the performance distribution – meet our performance criteria will the sub-strategy advance to the next stage of evaluation, simulated trading. This gives us – and investors – a level of confidence in the ability of the strategy to continue to perform well regardless of how market conditions evolve over time.

An obvious question to ask at this point is: if this is such a great idea, why don’t more firms use this approach? The answer is simple: it involves too much research. In a typical portfolio strategy there is a single investment idea that is applied cross-sectionally to a universe of stocks (factor models, momentum models, etc). In the strategy portfolio approach, separate strategies must be developed for each stock individually, which takes far more time and effort. Consequently such strategies must necessarily scale more slowly.

Another downside to the strategy portfolio approach is that it is less able to control the portfolio characteristics. For instance, the overall portfolio may, on average, have a beta close to zero; but there are likely to be times when a majority of the individual stock strategies align, producing a significantly higher, or lower, beta. The key here is to ask the question: what matters more – the semblance of risk control, or the actual risk characteristics of the strategy? In reality, the risk controls of traditional long/short equity strategies often turn out to be more theoretical than real. Time and again investors have seen strategies that turn out to be downside-correlated with the market, regardless of the purported “market-neutral” characteristics of the portfolio. I would argue that what matters far more is how the strategy actually performs under conditions of market stress, regardless of how “market neutral” or “sector neutral” it may purport to be. And while I agree that this is hardly a widely-held view, my argument would be that one cannot expect to achieve above-average performance simply by employing standard approaches at every turn.

Parallels with Fund of Funds Investment

So, is this really a “new approach” to equity long/short? Actually, no. It is certainly unusual. But it follows quite closely the model of a proprietary trading firm, or a Fund of Funds. There, as here, the task is to create a combined portfolio of strategies (or managers), rather than by investing directly in the underlying assets. A Fund of Funds will seek to create a portfolio of strategies that have low correlations to one another, and may operate a meta-strategy for allocating capital to the component strategies, or managers. But the overall investment portfolio cannot be as easily constrained as an individual equity portfolio can be – greater leeway must be allowed for the beta, or the dollar imbalance in the longs and shorts, to vary from time to time, even if over the long term the fluctuations average out. With human managers one always has to be concerned about the risk of “style drift” – i.e. when managers move away from their stated investment mandate, methodologies or objectives, resulting in a different investment outcomes. This can result in changes in the correlation between a strategy and its peers, or with the overall market. Quantitative strategies are necessarily more consistent in their investment approach – machines generally don’t alter their own source code – making a drift in style less likely. So an argument can be made that the risk inherent in this form of equity long/short strategy is on a par with – certainly not greater than – that of a typical fund of funds.

Conclusions

An investment approach that seeks to create a portfolio of strategies, rather than of underlying assets, offers a significant advantage in terms of risk reduction and diversification, due to the relatively low levels of correlation between the component strategies. The trading costs associated with higher frequency trading can be mitigated using passive entry/exit rules designed to avoid taking liquidity and generating exchange rebates. The downside is that it is much harder to manage the risk attributes of the portfolio, such as the portfolio beta, sector risk, or even the overall net long/short exposure. But these are indicators of strategy risk, rather than actual risk itself and they often fail to predict the actual risk characteristics of the strategy, especially during conditions of market stress. Investors may be better served by an approach to long/short equity that seeks to maximize diversification on the temporal axis as well as in terms of the factors driving strategy alpha.

Disclaimer: past performance does not guarantee future results. You should not rely on any past performance as a guarantee of future investment performance. Investment returns will fluctuate. Investment monies are at risk and you may suffer losses on any investment.

November 19, 2019

Strategy Portfolio Construction

May 14, 2019May 14, 2019

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.

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

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.

March 26, 2019

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!

Source: Vix and More

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

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.

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).

December 11, 2018

The Case for Volatility as an Asset Class

Volatility as an asset class has grown up over the fifteen years since I started my first volatility arbitrage fund in 2000. Caissa Capital grew to about $400m in assets before I moved on, while several of its rivals have gone on to manage assets in the multiple billions of dollars. Back then volatility was seen as a niche, esoteric asset class and quite rightly so. Nonetheless, investors who braved the unknown and stayed the course have been well rewarded: in recent years volatility strategies as an asset class have handily outperformed the indices for global macro, equity market neutral and diversified funds of funds, for example.

The Fundamentals of Volatility

It’s worth rehearsing a few of the fundamental features of volatility for those unfamiliar with the territory.

Volatility is Unobservable

Volatility is the ultimate derivative, one whose fair price can never be known, even after the event, since it is intrinsically unobservable. You can estimate what the volatility of an asset has been over some historical period using, for example, the standard deviation of returns. But this is only an estimate, one of several possibilities, all of which have shortcomings. We now know that volatility can be measured with almost arbitrary precision using an integrated volatility estimator (essentially a metric based on high frequency data), but that does not change the essential fact: our knowledge of volatility is always subject to uncertainty, unlike a stock price, for example.

Volatility Trends

Huge effort is expended in identifying trends in commodity markets and many billions of dollars are invested in trend following CTA strategies (and, equivalently, momentum strategies in equities). Trend following undoubtedly works, according to academic research, but is also subject to prolonged drawdowns during periods when a trend moderates or reverses. By contrast, volatility always trends. You can see this from the charts below, which express the relationship between volatility in the S&P 500 index in consecutive months. The r-square of the regression relationship is one of the largest to be found in economics. And this is a feature of volatility not just in one asset class, such as equities, nor even for all classes of financial assets, but in every time series process for which data exists, including weather and other natural phenomena. So an investment strategy than seeks to exploit volatility trends is relying upon one of the most consistent features of any asset process we know of (more on this topic in Long Memory and Regime Shifts in Asset Volatility).

Volatility Mean-Reversion and Correlation

One of the central assumptions behind the ever-popular stat-arb strategies is that the basis between two or more correlated processes is stationary. Consequently, any departure from the long term relationship between such assets will eventually revert to the mean. Mean reversion is also an observed phenomenon in volatility processes. In fact, the speed of mean reversion (as estimated in, say, an Ornstein-Ulenbeck framework) is typically an order of magnitude larger than for a typical stock-pairs process. Furthermore, the correlation between one volatility process and another volatility process, or indeed between a volatility process and an asset returns process, tends to rise when markets are stressed (i.e. when volatility increases).

Another interesting feature of volatility correlations is that they are often lower than for the corresponding asset returns processes. One can therefore build a diversified volatility portfolio with far fewer assets that are required for, say, a basket of equities (see Modeling Asset Volatility for more on this topic).

Finally, more sophisticated stat-arb strategies tend to rely on cointegration rather than correlation, because cointegrated series are often driven by some common fundamental factors, rather than purely statistical ones, which may prove temporary (see Developing Statistical Arbitrage Strategies Using Cointegration for more details). Again, cointegrated relationships tend to be commonplace in the universe of volatility processes and are typically more reliable over the long term than those found in asset return processes.

Volatility Term Structure

One of the most marked characteristics of the typical asset volatility process its upward sloping term structure. An example of the typical term structure for futures on the VIX S&P 500 Index volatility index (as at the end of May, 2015), is shown in the chart below. A steeply upward-sloping curve characterizes the term structure of equity volatility around 75% of the time.

Fixed income investors can only dream of such yield in the current ZIRP environment, while f/x traders would have to plunge into the riskiest of currencies to achieve anything comparable in terms of yield differential and hope to be able to mitigate some of the devaluation risk by diversification.

The Volatility of Volatility

One feature of volatility processes that has been somewhat overlooked is the consistency of the volatility of volatility. Only on one occasion since 2007 has the VVIX index, which measures the annual volatility of the VIX index, ever fallen below 60.

What this means is that, in trading volatility, you are trading an asset whose annual volatility has hardly ever fallen below 60% and which has often exceeded 100% per year. Trading opportunities tend to abound when volatility is consistently elevated, as here (and, conversely, the performance of many hedge fund strategies tends to suffer during periods of sustained, low volatility)

Anything You Can Do, I Can Do better

The take-away from all this should be fairly obvious: almost any strategy you care to name has an equivalent in the volatility space, whether it be volatility long/short, relative value, stat-arb, trend following or carry trading. What is more, because of the inherent characteristics of volatility, all these strategies tend to produce higher levels of performance than their more traditional counterparts. Take as an example our own Volatility ETF strategy, which has produced consistent annual returns of between 30% and 40%, with a Sharpe ratio in excess of 3, since 2012.

(click to enlarge)

Where does the Alpha Come From?

It is traditional at this stage for managers to point the finger at hedgers as the source of abnormal returns and indeed I will do the same now. Equity portfolio managers are hardly ignorant of the cost of using options and volatility derivatives to hedge their portfolios; but neither are they likely to be leading experts in the pricing of such derivatives. And, after all, in a year in which they might be showing a 20% to 30% return, saving a few basis points on the hedge is neither here nor there, compared to the benefits of locking in the performance gains (and fees!). The same applies even when the purpose of using such derivatives is primarily to produce trading returns. Maple Leaf’s George Castrounis puts it this way:

Significant supply/demand imbalances continuously appear in derivative markets. The principal users of options (i.e. pension funds, corporates, mutual funds, insurance companies, retail and hedge funds) trade these instruments to express a view on the direction of the underlying asset rather than to express a view on the volatility of that asset, thus making non-economic volatility decisions. Their decision process may be driven by factors that have nothing to do with volatility levels, such as tax treatment, lockup, voting rights, or cross ownership. This creates opportunities for strategies that trade volatility.

We might also point to another source of potential alpha: the uncertainty as to what the current level of volatility is, and how it should be priced. As I have already pointed out, volatility is intrinsically uncertain, being unobservable. This allows for a disparity of views about its true level, both currently and in future. Secondly, there is no universal agreement on how volatility should be priced. This permits at times a wide divergence of views on fair value (to give you some idea of the complexities involved, I would refer you to, for example, Range based EGARCH Option pricing Models). What this means, of course, is that there is a basis for a genuine source of competitive advantage, such as the Caissa Capital fund enjoyed in the early 2000s with its advanced option pricing models. The plethora of volatility products that have emerged over the last decade has only added to the opportunity set.

Why Hasn’t It Been Done Before?

This was an entirely legitimate question back in the early days of volatility arbitrage. The cost of trading an option book, to say nothing of the complexities of managing the associated risks, were significant disincentives for both managers and investors. Bid/ask spreads were wide enough to cause significant heads winds for strategies that required aggressive price-taking. Mangers often had to juggle two sets of risks books, one reflecting the market’s view of the portfolio Greeks, the other the model view. The task of explaining all this to investors, many of whom had never evaluated volatility strategies previously, was a daunting one. And then there were the capacity issues: back in the early 2000s a $400m long/short option portfolio would typically have to run to several hundred names in order to meet liquidity and market impact risk tolerances. Much has changed over the last fifteen years, especially with the advent of the highly popular VIX futures contract and the newer ETF products such as VXX and XIV, whose trading volumes and AUM are growing rapidly. These developments have exerted strong downward pressure on trading costs, while providing sufficient capacity for at least a dozen volatility funds managing over $1Bn in assets.

Why Hasn’t It Been Done Right Yet?

Again, this question is less apposite than it was ten years ago and since that time there have been a number of success stories in the volatility space. One of the learning points occurred in 2004-2007, when volatility hit the lows for a 20 month period, causing performance to crater in long volatility funds, as well as funds with a volatility neutral mandate. I recall meeting with Nassim Taleb to discuss his Empirica volatility fund prior to that period, at the start of the 2000s. My advice to him was that, while he had some great ideas, they were better suited to an insurance product rather than a hedge fund. A long volatility fund might lose money month after month for an entire year, and with it investors and AUM, before seeing the kind of payoff that made such investment torture worthwhile. And so it proved.

Conversely, stories about managers of short volatility funds showing superb performance, only to blow up spectacularly when volatility eventually explodes, are legion in this field. One example comes to mind of a fund in Long Beach, CA, whose prime broker I visited with sometime in 2002. He told me the fund had been producing a rock-steady 30% annual return for several years, and the enthusiasm from investors was off the charts – the fund was managing north of $1Bn by then. Somewhat crestfallen I asked him how they were producing such spectacular returns. “They just sell puts in the S&P, 100 points out of the money”, he told me. I waited, expecting him to continue with details of how the fund managers handled the enormous tail risk. I waited in vain. They were selling naked put options. I can only imagine how those guys did when the VIX blew up in 2003 and, if they made it through that, what on earth happened to them in 2008!

Conclusion

The moral is simple: one cannot afford to be either all-long, or all-short volatility. The fund must run a long/short book, buying cheap Gamma and selling expensive Theta wherever possible, and changing the net volatility exposure of the portfolio dynamically, to suit current market conditions. It can certainly be done; and with the new volatility products that have emerged in recent years, the opportunities in the volatility space have never looked more promising.