1. DIGGING INTO VOLATILITY

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    OK, let’s face it.  The human race is really, really good at procrastinating and not facing the music until forced to. Financial markets are no exception.  One way this effect can be seen is in volatility spikes, like the one below.  It turns out that such extremes are commonplace and can be exploited for an edge.

    Likely the best known volatility measure is the S&P 500 Volatility Index (VIX), aka The Fear Index.  Why is it called that?  Well, when everyone all together rides the spank train, guess what?  Insurance premiums go up… a lot and quickly. And that is largely associated with panic and fear. 

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    [above: examples of spikes in implied volatility] 

    Now it’s important to note that this is not observed/historical volatility, rather implied volatility.  It’s feet betting on how volatile the market might be 30 days into the future.  On display is the immediate perception of how good or bad it will be going forward, through the lens of what just happened.  And, perception drives behavior.

    Below is the chart of the S&P 500 Index (top pane) with the ratio of 30-day implied volatility to 90-day implied volatility (near-term expectation for volatility versus the longer-term).  This tunes into crowd sentiment by answering the question, “Is it more important to buy insurance now, or later?” 

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    [above: on top the S&P 500 with the ratio of 30-day to 90-day implied volatility below]

    As this ratio moves higher, the fear of a correction being just around the corner is growing.  As market prices move lower, confirming fears, demand for near-term insurance is pushed to extremes shown by the shaded red zones.  These are our proprietary measures of extremes. 

    By tracking when the ratio first crosses a zone, we can look for extremes in real-time.  Purple arrows indicate lower extremes while green ones indicate greater extremes.  These are likely real-time peaks. 

    So how is this helpful?  For starters, what about all of those cliches like buy low sell high, buy when there’s blood in the streets, don’t follow the crowd, etc.?  This model provides that context.  

    Second, peaks in sentiment occur near market bottoms roughly 68% of the time. This is extremely helpful, pun intended, by helping filter out the noise from the media, giving a sense of whether or not the odds are favorable for buying a particular dip.  Recently, several lower extremes in sentiment have been detected.  This is bullish. 

    How about if another pull-back occurs in the near-term?  When would I look for another opportunity?  A reading of 0.97 will indicate a lower extreme while a reading above 1.06 would indicate a higher extreme. Either one would give me a clue that sentiment is being pushed to an extreme, creating another likely opportunity.