Market Lab Report - The VIX Volatility Model (VVM) vs. Central Banks

Published : March 13 2016 at 19:42 ET

The massive rip tides this year caused by quantitative easing vs. the loss of confidence in central bank policies are profound. The VIX Volatility Model (VVM) which is highly sensitive to small changes in the markets is quite telling in that its self-learning nature tailors its behavior to the market environment. In a sense, it acts as a market barometer.

For example, it has never had so many signal changes over the last 10 week period unless one goes back to the colossally turbulent times of 2000-2002 or 2008, both which were highly profitable periods for the model. Such frequent signal changes can be nicely profitable as a number of intrasignal 15%+ gains have materialized since the model went beta in September 2015 if trading UVXY on buy signals. In addition, a profit taking algorithm is now in place to determine when to take profits in such favorable situations as some of these fast gains went well beyond +15% as volatility spiked. That said, it is essential no signals are overridden, especially by my trying to second guess the model in this rip-tide environment, as some signals can produce such 15%+ gains (see 

Still, frequent signal changes can also lead to drawdowns, depending on market conditions. For example, the model dislikes what could be called the rare periods of chaotic volatility as opposed to clean volatility such as in 2000-2002 and 2008. During any chaotic periods which are rare, noise levels are elevated. The model tends to have drawdowns during such periods which are characterized by strings of small losses and whipsaws, some occurring up to three times in a day which is fortunately a very rare occurrence. These drawdown periods last typically a few months or less, though can be frustrating. Further, drawdown periods are also contained to typically within -20% (using 1x XIV on sell signals and 2x UVXY on buy signals). Meanwhile, the upside trounces any such drawdown periods as shown in the backtests. But it can require a degree of patience to withstand a string of small losses, even though typically contained to within 1.5% if trading 1x ETFs such as VXX and XIV and often breakeven.

Potential Issues

Indeed, it would be unfortunate were the model to have such a drawdown period at this point after scoring a number of +15% intrasignal gains. When the profit taking algorithm is taken together with the signals I overrode, we would be sitting on a healthy profit at this point to easily withstand a drawdown period. 

Another issue is the model could trigger a fail-safe then not re-enter the position as the market goes on an uptrend which means it would be sidelined while the market moves higher. Fortunately, this has yet to happen in the backtests or in real--time, but that does not mean it cannot happen in the future. 

Another issue is the model could get whipsawed a number of times should the ETF go into a trading range while the model stays on its signal. That said, it has a maximum of three tries for each fail-safe then moves to cash until the next change in signal. 

Finally, there is gap up/gap down risk with these volatility ETFs. Should the ETF gap lower the next day triggering a fail-safe, the loss could be greater than the typical -1.5% using a 1x ETF. Backtests have shown the worst case scenario was an overnight gap of 8.2% which made the total loss for that particular signal -11.2% due to two fail-safe triggers which also occurred during the signal (-8.2% - 1.5% - 1.5% = -11.2%). The flipside of this are gaps in one's favor which have far exceeded any losses caused by gaps lower through fail-safes.

Incidentally, while there is no 2x inverse volatility ETF, the reason we use only 1x XIV on sell signals is that volatility can spike overnight without warning on some catastrophic news event which would send XIV gapping lower. Meanwhile, markets generally never "melt-up" by gapping higher overnight by the same order of magnitude. And 2x UVXY would greatly benefit on spikes in volatility as it did on 8/24/15 when it gapped higher overnight by +61.9%. Incidentally, the model has never been caught in XIV on such a huge gap down as the worst has been -8.2% as noted above.

My intention in sharing this with you is to make everyone aware of certain negative factors which can occur even if such occurrences are very low probability. 

At present, it is easy to feel the markets are behaving in an absurd manner as the rip tides created by central banks and the global economy play out. But it goes to show that governments rule the day in excessive fashion on this 39th day as brilliantly described in legendary futures trader Ed Seykota's book Govopoly on the 39th Day and

Seykota uses the 39th day as an analogy to pond ecosystems where the amount of duckweed doubles each day. It goes unnoticed until after the 30th day where the presence of duckweed in the pond becomes pronounced. But by this time, it is too late to reverse its exponential growth. We are in the 39th day which means the world sits at a major tipping point as duckweed, ie, size of government, is doubling once more only to suffocate all life in the pond. Indeed, 8,000 years of history bears this out and is why fiat currencies have a limited lifetime as any leading government eventually topples.

Indeed, this 39th day is a time of major transformation. Seykota says that while various crises are emerging to accelerate this transformation, his advice has been to keep a clear head and watch for the inevitable tsunamis that emerge as massive change brings massive volatility thus huge potential for profit. Indeed, the massively volatile period of 2000-2002 and 2008 were the VIX Volatility Model's most profitable periods and in backtests, all 12-month rolling periods have been profitable regardless of the year. For example, the model trounced the major averages even in a trendless year such as 2015.

A number of transformative tsunamis will occur over the next few years if history is any guide, and over the last few months, it appears one may have just begun.

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