## Summary

- Our Hedged Convexity Capture Indices are at an all time high.
- Our Non-Correlated Hedged Convexity Capture Index is at an all time high.
- Our Structural Arbitrage Index is at an all time high.
- Our Integrated Index is at an all time high.
- Correlations to the S&P 500 for all benchmarks remain moderate.

Making money with math has been too easy for too long, and it makes me incredibly nervous. It is time to head to the beach, literally, figuratively, and financially. To that end, I have spent almost a week with dear friends in Newport Beach, and I am headed to Malibu today.

I should take deep satisfaction that our strategy-based indices have crushed the performance of the S&P 500, but instead, I am worried. As a quantitative researcher and trader, that's my job--to be worried about potential risks, even when things are going well. It's always the events that have not yet occurred in any backtest, but are yet to come, that kill participants in the financial markets.

As our long time readers know, Hedged Convexity Capture and Structural Arbitrage are two strategies that my firm has invented which take advantage of purely quantitative inefficiencies in financial markets. The strategy-based indices we have created illustrate the potential performance of harvesting these inefficiencies, but are extremely simplistic by design. We are trying to zero in on the inefficiencies, so that we can truly explicate their potential size, persistence, and stability over time.

Our safer, more complex, strategy-based indices are for clients. Think of our elegantly simple Structural Arbitrage and Hedged Convexity Capture indices as proofs of concept, like test bed aircraft, rather than planes designed for the rigors of combat.

Each index is designed to explicate one inefficiency--not to withstand every potential future obstacle lurking in the financial markets. It's important that readers understand that.

The metaphor of a test plane is quite accurate. Imagine that we are testing a new stealth coating on an experimental aircraft. We're hitting the plane with radar from different angles, and we are purely focusing on testing the performance of that coating.

Now imagine if an Airforce general said that combat aircraft should come with bombs, missiles, and electronic countermeasures. I totally agree. A combat-ready plane should. And when we deliver it to the Airforce for combat, after all R&D is complete, it will. But the purpose of that experimental aircraft is purely to test one thing--the stealth coating!

Similarly, I constantly get messages and comments from readers (which I do appreciate) saying that an investment-ready index should come with additional bells and whistles, such as trailing stops, put options for hedges, etc. I am totally with you one thousand percent. But the purpose of the index, like the experimental aircraft, is solely to explicate one or two phenomenon for R&D. The purpose is not to send the testbed plane into combat, or to send investors into the financial markets with simplistic indices.

Therefore, think of our indices as explicating one of two factors which are rarely harvested in the financial markets. Then consider how much more profitable it has been to harvest even one of these inefficiencies compared to more common factors/phenomenon such as momentum. And think about how you would integrate strategies which take advantage of these phenomenon into a larger investment-ready (or combat-ready!) portfolio.

Once the indices are viewed in this light, readers can stop expecting them to be an investment panacea. Rather, they should be viewed as potentially fruitful starting points for further investigation in the financial markets.

Shrewd researchers will realize that one can actually take advantage of these phenomenon using totally different instruments than the ones we use to construct our indices. Indeed, what separates a shrewd macro investor from an average macro investor is not their views, but the instruments they use to express those views.

Also, readers should stop relegating quantitative phenomenon to the realm of the mysterious or the magical. Indeed, when readers think deeply about issues such as Structural Arbitrage and Hedged Convexity Capture, they will realize that these are really statistically based value strategies, which take advantage of a mispricing, on average, between two different instruments or markets.

The mispricing between disparate instruments is statistical, or persists on average, rather than always occurring. When the mispricing is not occurring, that's what leads to drawdowns. Therefore, one can think of these strategies as statistically value-based. The nature of the instruments and the markets means that, on average, a probabilistic value based mispricing exists between the instruments relative to each other. The mispricing does not always occur. It simply occurs more often then not, on average. This is either due to the nature of the instrument, or due to the nature of market structure and inter-asset class relationships

When one thinks of these strategies as statistically or probabilistically value-based, they become imminently understandable and familiar in substance. Only the form is new. On that note, let's examine the performance of each index in its entirety and year-to-date.

Long-biased Hedged Convexity Captures is a strategy which seeks to capture the negative convexity associated with leveraged ETPs. The idea behind Hedged Convexity Capture is to capture the potential returns from shorting leveraged inverse equity ETPs with lower drawdowns and far higher Sharpe ratios than by just shorting them outright. The strategy seeks to accomplish this by shorting leveraged inverse equity ETPs like TZA (NYSEARCA:TZA) or SPXU (NYSEARCA:SPXU), and pairing that short with a short position in TMV (NYSEARCA:TMV), an inverse-leveraged long bond ETP.

Not only does shorting TMV often provide a hedge for the equity portion of the strategy, but TMV itself suffers from negative convexity, further increasing the effectiveness of the long bond hedge.

We shall efficiently capture negative convexity in a hedged manner with the following rules:

I. Short TZA with 50% of the dollar value of the portfolio.

II. Short TMV with 50% of the dollar value of the portfolio.

III. Rebalance weekly to maintain the 50%/50% dollar value weighting between the two instruments.

Here is a graph of the results in a log scale:

What's fascinating about the performance of the strategy is that not only does that Sharpe and MAR vastly exceed that of the SPY, but also the strategy outperforms the SPY (NYSEARCA:SPY) in every year of the test period, with a moderate 0.61 correlation. The negative convexity inefficiency exhibits excellent factor persistence and stability.

For the SPXU version of the strategy, we shall efficiently capture negative convexity in a hedged manner with the following rules:

I. Short SPXU with 50% of the dollar value of the portfolio.

II. Short TMV with 50% of the dollar value of the portfolio.

III. Rebalance weekly to maintain the 50%/50% dollar value weighting between the two instruments.

Here is a graph of the results in a log scale:

Even better performance YTD, and again, the negative convexity inefficiency exhibits excellent factor persistence and stability.

This time, will focus on Non-Correlated Hedged Convexity Capture. We shall efficiently capture this negative convexity in a hedged manner with the following rules:

I. Short SPXU with 40% of the dollar value of the portfolio.

II. Short TMV with 60% of the dollar value of the portfolio.

III. Rebalance weekly to maintain the 40%/60% dollar value weighting between the two instruments.

Here is a graph of the results in a log scale:

Even with miniscule 0.13 correlation to the SPY, the negative convexity inefficiency exhibits excellent factor persistence and stability. It is not simply levered SPY exposure.

But why does Hedged Convexity Capture outperform so consistently? Hedged Convexity Capture is a perfect example of an actuarial approach to trading. It makes the same shrewd bet on convexity again and again, by focusing on a repeatable phenomenon (remember that concept from science?) called negative convexity. We eschew helter-skelter predictions. And when the phenomenon observed is especially strong, the results of that phenomenon become proportionally more predictable. And the negative convexity associated with leveraged inverse ETPs is an especially strong phenomenon.

While would-be hedgers are attempting to lay off market risk by buying inverse leveraged ETPs, they are actually taking on negative convexity risk. And that decision is grossly inefficient, because the negative convexity risk turns out to be, on average (remember, on average!), a far larger risk than the market risk they were seeking to avoid. Therefore, the strategy takes the other side of those trades and captures that negative convexity in a hedged manner.

Moving on, Structural Arbitrage is based upon the phenomenon that profits are possible by acting as a synthetic insurance company which sells expensive insurance in the volatility market, and then synthetically reinsures that market risk with long duration government bonds.

Structural Arbitrage is metaphorically like running a virtual insurance company for S&P 500 market risk. But we will not have the overhead or competition of a conventional insurance company. Imagine an insurer that sells insurance, then reinsures against catastrophe. Similarly, we are selling expensive insurance by synthetically shorting VIX ETPs through their inverse analogues, then reinsuring cheaply by purchasing ETPs which hold Long Bonds. Our theory is that we can (somewhat often, but not always fully) reinsure the risk we have undertaken by selling insurance which is usually too expensive, and by buying reinsurance which is usually too cheap.

This statistical structural arbitrage has persisted thus far, because while market participants recognize that volatility and long bonds are both linked to equity prices, they have not made the logical/syllogistic leap that, therefore, volatility and long bond markets should also be linked to each other. The arbitrage is structural in nature, because it exploits differences in how two different markets price equity risk. However, the arbitrage is statistical, rather than absolute, because these two markets misprice equity risk relative to each to each other, not always, but on average.

We shall implement Structural Arbitrage with the following rules:

I. Buy XIV (NASDAQ:XIV) with 40% of the dollar value of the portfolio.

II. Buy TMF (NYSEARCA:TMF) with 60% of the dollar value of the portfolio.

III. Rebalance weekly to maintain the 40%/ 60% dollar value split between the positions.

Here is a graph of the results in a log scale:

Again, we happily have a low correlation to the SPY combined with a higher Sharpe and MAR. The strategy, like Hedged Convexity Capture, seems to massively outperform in weak years for the S&P 500, and underperform in strong up years for the S&P 500. And Structural Arbitrage has a 42.8% performance YTD to boot with long-only instruments that do not have the massive tail risk inherent in shorting.

Moving on, combining the benefits of Hedged Convexity Capture with Structural Arbitrage is really combining the advantages of exploiting two different major market inefficiencies. Notice that not only are we employing Structural Arbitrage, but we are doing so using the 2X leveraged instrument UVXY, which suffers from negative convexity due to its leverage, as well as using TMV as the hedge, which further exhibits negative convexity.

Here are the integrated strategy's rules:

I. Short UVXY (NYSEARCA:UVXY) with 25% of the dollar value of the portfolio.

II. Short TMV with 75% of the dollar value of the portfolio.

III. Rebalance weekly to maintain the 25%/ 75% dollar value split between the positions.

Here is a graph of the results in a log scale:

We have a very nice bump in the Sharpe ratio and MAR, which still persists if we use the imperfect TVIX (NASDAQ:TVIX) in place of UVXY for testing in order to see data which goes back to late 2010. However, I do not like to use the TVIX for trading purposes due to its well-documented past problems. For testing, it confirms the point of integrating the two inefficiencies.

Moving on, we will examine two more never-before-published methods of implementing an integrated Structural Arbitrage/Hedged Convexity strategy.

Here are the second integrated strategy's rules:

I. Short VXX (NYSEARCA:VXX) with 20% of the dollar value of the portfolio.

II. Short SPXU with 30% of the dollar value of the portfolio.

II. Short TMV with 50% of the dollar value of the portfolio.

III. Rebalance weekly to maintain the 20%/30%/50% dollar value split between the positions.

Here is a graph of the results in a log scale:

Notice that the Sharpe is now 2, and that the MAR is approaching 3. I am quite proud of this. This may be the first time that a strategy with a MAR approaching 3 has ever been publicly revealed.

And now, given what has preceded, here is an obvious extension that further integrates Structural Arbitrage and Hedged Convexity capture.

Here are the third integrated strategy's rules:

I. Short UVXY with 10% of the dollar value of the portfolio.

II. Short SPXU with 30% of the dollar value of the portfolio.

II. Short TMV with 60% of the dollar value of the portfolio.

III. Rebalance weekly to maintain the 10%/30%/60% dollar value split between the positions.

Here is a graph of the results in a log scale:

Notice that we have managed to lower the correlation significantly while maintaining a strong Sharpe.

My firm has proven the point beyond a shred of a doubt--Hedged Convexity Capture and Structural Arbitrage take advantage of massive, systematic, and persistent inefficiencies. Structural and Convexity inefficiencies can and should be exploited.

However, as I have said many times before, there are risks such as hyperinflation or other events in which both volatility could skyrocket and government bonds could simultaneously tank. This or other risks could cause these benchmarks to drop uncontrollably or for the investors to lose more than their original investment, if they use shorting. These benchmarks merely illustrate factor performance. The public versions explicated here are not "combat ready." These benchmarks should spur further research on the part of readers who are interested in market inefficiencies and should serve as a starting point for serious research--not the end of it. We have far better long-only benchmarks available for clients.

What worries me is that even the simple benchmarks explicated above are wildly crushing the S&P 500 recently--and have been for years. Making money with simple math has been too easy for too long. If you have been using only these simple public benchmarks and have been heedless of my warnings that they should only serve as test-beds, close out your positions and join me on the beach--literally, figuratively, and financially. It's time to enjoy the profits.

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