Guest Post: Short “Sweet Spot” Approaching?  —by Frank Roellinger

Updated: Apr 24th, 2016 | Vance Harwood

Probably the most difficult thing to do in stock market investing is to identify a good time to sell.  Many technical indicators have been devised to identify lows around the time they occur or soon thereafter with a moderate degree of success, but to my knowledge that has not happened for tops with comparable success.

My own modified Davis method does not do a very good job at this. It does profit on the short side, but only about 2% per year on average.  Were it not for the occasional severe bear market where my method will be short or at most 50% long—enabling significant funds to be invested near the beginning of the next bull market, my method probably would not do any better than buy and hold over the long term.

However, I have discovered a tendency which I think is nice to know, based on the length of the breadth divergence period preceding the short signal.  My test for breadth divergence uses a measure similar to the classic cumulative advance-decline line.  As a bull market matures there will be a point when prices make new highs, but that high is not accompanied by a new high in the cumulative advance-decline line.  This is the point of breadth divergence, and this phenomenon has occurred near the end of virtually all bull markets.  Stan Weinstein described this metric in “Secrets For Profiting in Bull and Bear Markets” almost 30 years ago, and he probably wasn’t the first to do so.

When developing my buy/sell algorithms, I avoid the trap of fitting my approach to the historical data by using only an older subset of the historical data to determine my thresholds.  I then run my method forward test on the newer, out of sample data, to determine how it would have performed.

Here is a list of all of the short trades in my forward test, arranged by the number of consecutive weeks of divergence in effect at the time of the signal.  The chart below summarizes the results.

Frank chart2


It appears that divergence must be in place for at least 13 weeks to have a good shot at a successful short.  In particular, it also appears that there is a “sweet spot” from 41 to 79 weeks.  There, the probability has been 0.80 of a profitable short.  Over the entire period there were 5 shorts that resulted in double-digit profits, and 2 of them occurred in this interval.  Beyond 79 weeks the probability of success declines significantly.  (Bear in mind that the method shorts at the 50% level, so these figures would be doubled if it shorted at the 100% level.)

Note that something similar to a normal distribution is formed in the above chart.  I am no statistician, but it may be that these results are indicative of some degree of natural order in the workings of the stock market.

However, there does not seem to be anything here that could be incorporated into my modified Ned Davis algorithm, and even if it could, that obviously could not be considered as part of the forward test.  The only thing to do is to take every short as it comes, as one never knows for certain when “the big one” will begin.

At the last short (12/11/15) of my method, divergence had been in effect for 31 weeks.  At this writing, the figure is 34 weeks, and it will take a very strong market to end this condition.  The current short may not end profitably, but the next short may have a good chance of being in the 41 to 79-week sweet spot.

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A Very Simple Model for Pricing VIX Futures

Updated: Dec 27th, 2015 | Vance Harwood

Serious volatility watchers are always observing a three-ring circus. The left ring holds the general market. Center ring has options on the S&P 500 and the various CBOE VIX® style indexes and to the right are VIX futures, Volatility Exchange Traded Products like VXX, UVXY, TVIX, and XIV plus associated options.

Activities in the three rings usually follow a familiar choreographed pattern. The VIX moves in opposition to the market while VIX futures and their kin trail the VIX unenthusiastically. VIX futures converge to the VIX’s value at expiration but prior to that they following their own path—usually charging a premium to the VIX, but sometimes offering steep discounts. Meanwhile, in the background, the VIX maintains its reversion to mean behavior, a macro cycle the short term moves modulate.

One of my ongoing interests is monitoring the Volatility Circus’ rings two and three—the family ensembles of VIX and VIX Futures.  I note unusual movements and try to determine which one of them is “right” more often—perhaps foreshadowing market moves. Recently I’ve developed a model that helps describe this relationship. It is presented later in this post.

Interpreting the values of VIX futures has been especially challenging. The price relationship of the next to expire VIX future and the VIX tends to be very dynamic in the last few weeks before its expiration.  With only a single data point, the one active future with less a month to expiration, there hasn’t been much data to work with.

Of course, there are mind-bending mathematical models available for VIX Future pricing—but unless you have a Ph.D. in quantitative finance they are probably too complex to be helpful.

Enter the CBOE’s Weekly Futures

By introducing VIX futures with weekly expiration dates the CBOE boosted the number of close-in data points from one to five—a dramatic improvement. One day while looking at Eli Mintz’s chart on these new futures a light bulb lit up in my head


The green dots are the newly introduced futures. Taken together the leftmost part of this curve looked logarithmic to me.

Sure enough, when plotted in Excel the logarithmic trendline match to the first two months of the futures was very good.

ln match to VIX Futures


However around month 4 the trendline starts seriously understating VIX futures prices.

7mo Trend Projection

Apparently there’s an additional mechanism that boosts the futures’ value over time.

The Model

Using VIX Futures data from 2004 on I developed the following equation which does a surprisingly good job of estimating VIX futures’ prices given its simplicity.  The only inputs are the current VIX value, the number of days (X) until the future expires, and the historical median value of the VIX.

VS-VX_FUT version B equation

The VIX closing median value from January 1990 through October 2015 is 18.01.

Example calculation: if the VIX is at 16 and a VIX future has 10 days before expiration this model predicts a price of 16.93.

16+ (1-16/18.01)* Ln (10+1) +3.1623*0.23 = 16+ 0.1116* 2.3979 + 3.1623*.21 = 16.93

A near real-time chart of the VIX Futures values predicted by the equation is posted here.

A Few Notes on the Equation

  • At VIX Future expiration (X = 0), the equation sets the VIX futures price equal to the VIX. The convergence term in the middle is forced to zero because Ln (0+1) equals zero and the carry cost term on the right is forced to zero by X being zero.
  • If the VIX matches its historical median price the convergence term is canceled out by the expression in front of the natural log, and the only difference in prices from the VIX will be the square root of time scaled factor on the right.
  • If the VIX is relatively low (below the historic median) the equation predicts the typical premium prices of the VIX futures relative to the VIX. The market is in this state 75% to 85% of the time.
  • Conversely, if the VIX is high, the equation predicts the VIX futures will be cheap relative to the VIX levels.
  • Since volatility increases with the square root of time, the term on the right side of the equation suggests a time scaled volatility component.  The 0.21 factor was determined empirically by adjusting its value until the average errors for the 3rd, 4th, and 5th-month futures from March 2004 through September 2015 were less than a percent.  The resulting 0.21 factor is quite close to the historical VIX median volatility of 0.18, so it’s possible that it is an implied volatility factor that rests a few percentage points above the historical value.

Why A Model?

You might reasonably ask why bother with a model when you can just look up the current VIX futures prices on the web. This model is interesting to me because:

  • It helps me understand the underlying mechanisms behind VIX Futures pricing
  • I can determine how current VIX futures prices are behaving compared to their predicted behavior—useful for evaluating situations where event risk is distorting prices or the market is especially panicky
  • I can predict future VIX futures prices for various VIX scenarios

Model Errors

The model is very inaccurate at times, with errors on historic data sometimes exceeding +30%/-15% percent. The chart below shows the model’s error terms for the next to expire futures since 2004.

sqrt-time-VX-FT model pt21

The model tends to overestimate futures prices while in sustained periods of low VIX and underestimate the prices in bear markets. During big volatility spikes (Oct 08, Aug 11, Aug 15) the model predicts VIX futures values that far exceed the actual prices.

It isn’t surprising that the model doesn’t adroitly handle the impact of big jumps or drops in market volatility since it doesn’t incorporate any historical information at all—other than the long-term median VIX value.

The error spike on the far right of the error chart is a whopper, nearly 50%. On August 24th, 2015 the VIX closed up 45% at 40.74, but the front month future (September) only climbed 26% to 25.13. The model predicted a value of 37.16, up 39%.

Despite the chaos prevalent on August 24th the futures market did an impressive job of predicting the eventual (23 days later) expiration value of the September 2015 futures.  Expiration was at 22.38, only 2.75 points away from the August 24th closing value.

A more accurate model would need to incorporate the effects of VIX jumps and slumps. It’s not a trivial problem. In general, the VIX futures seriously lag big jumps in the VIX, but then stay higher than you’d expect after the volatility drops.  Part of this post is an enhancement to the model that  does take VIX’s gyrations into account, but it still leaves a lot to be desired.

Now instead of resembling Fellini’s circus the VIX futures moves in the Volatility Circus feel more rational to me. Their movements are often mysterious and complex—but a simple theme unites.

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VIX Futures Prices vs. Predictions from a “Simple” Model

Updated: Dec 9th, 2015 | Vance Harwood

It’s been said that we learn more from failures than success. Hopefully the chart below will be an illustration of that. It displays the near real-time prices of VIX futures vs. the predictions of a “simple” model I’ve created.  My intent with the model is not to achieve high accuracy (it won’t) but rather to distinguish between when VIX futures prices are truly unusual, and when they are displaying typical behavior.


The yellow dots show the percentage error between my estimate and the actual quotes.

I put simple in quotes above because I recognize that most people would not consider the formulas below simple.  But compared to the typical academic treatment of VIX Futures pricing this is a simple model.

SM-VIX futures pricing


The difference between this model and the one I described in the very simple model displayed below is the addition of functionality that addresses VIX jumps and slumps. The VIX itself is quite volatile with daily double-digit percentage changes being relatively common. Typically VIX futures lag these moves by about half (e.g, if VIX moves +10% the VIX futures term structure might shift up 5%).

I modeled this behavior with an exponential moving average of the VIX, using a coefficient of 0.5. This enhancement works well for the longer-dated futures, but shorter-term VIX futures have progressively tighter tracking to the VIX. To address this, I tweaked the jump/slump term, using a natural log function to extinguish the jump/slump lag as the time to expiration approaches zero.

A Very Simple Model

The graphs and descriptions below belong to my very simple model, which does not incorporate the historical patterns of the VIX at all.


The percentage errors are shown in yellow organized by expiration date.

The current VIX futures quotes are from Yahoo Finance (e.g., ^VIXnov).  Unfortunately as far as I know they aren’t providing quotes on the new Weekly VIX Futures yet. The chart below shows the model’s predictions for their values.


that I’ve created

You can check over at if you want to get the current values for both traditional monthly and the new Weeklys VIX futures (click on the “VIX Term All” tab).

My estimates are produced using this equation:

VS-VX_FUT version B equation

Where VIX is the current VIX index value, VIX Median is the historic VIX Median value (18.01 for March 2004 through September 2015), and X = days until VIX Future expiration.

For more information on this equation see, “A Very Simple Model for Pricing VIX Futures.”

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How Does TVIX Work?

Updated: Oct 8th, 2015 | Vance Harwood

Exchange Trade Note TVIX and its Exchange Traded Fund cousin UVXY are 2X leveraged funds that track short term volatility.  To have a good understanding of TVIX (full name: VelocityShares Daily 2x VIX Short-Term ETN you need to know how it trades, how its value is established, what it tracks, and how VelocityShares makes money on it.


How does TVIX trade? 

  • For the most part TVIX trades like a stock. It can be bought, sold, or sold short anytime the market is open, including pre-market and after-market time periods.  With an average daily volume of 21 million shares its liquidity is excellent and its bid/ask spread is penny.
  • Like a stock, TVIX’s shares can be split or reverse split. If fact, TVIX reverse split 3 times in its first four years of existence. The last reverse split was 10:1 and I’m predicting the next one will be around September 2016 with a 10:1 ratio also.  See this post for more details.
  • Unlike UVXY there are no options available on TVIX.
  • TVIX can be traded in most IRAs / Roth IRAs, although your broker will likely require you to electronically sign a waiver that documents the various risks with this security. Shorting of any security is not allowed in an IRA.


How is TVIX’s value established?

  • Unlike stocks, owning TVIX does not give you a share of a corporation.  There are no sales, no quarterly reports, no profit/loss, no PE ratio, and no prospect of ever getting dividends.  Forget about doing fundamental style analysis on TVIX. While you’re at it forget about technical style analysis too, the price of TVIX is not driven by supply and demand—it’s a small tail on the medium sized VIX futures dog, which itself is dominated by SPX options (notional value > $100 billion).
  • According to its prospectus the value of TVIX is closely tied to twice the daily return of the S&P VIX Short-Term Futurestm .
  • The index is maintained by S&P Dow Jones Indices. The theoretical value of TVIX if it were perfectly tracking 2X the daily returns of the short term index is published every 15 seconds as the “intraday indicative” (IV) value.  Yahoo Finance publishes this quote using the ^TVIX-IV ticker.
  • Wholesalers called “Authorized Participants” (APs) will at times intervene in the market if the trading value of TVIX diverges too much from the IV value.  If TVIX is trading enough below the IV value they start buying large blocks of TVIX—which tends to drive the price up, and if it’s trading above they will short TVIX.  The APs have an agreement with Credit Suisse that allows them to do these restorative maneuvers at a profit, so they are highly motivated to keep TVIX’s tracking in good shape.


What does TVIX track?

  • Ideally TVIX would exactly track the CBOE’s VIX® index—the market’s de facto volatility indicator.  However since there are no investments available that directly track the VIX VelocityShares chose to track the next best choice: VIX futures.
  • VIX Futures are not as volatile as the VIX itself; solutions (e.g., like Barclays’ VXX) that hold unleveraged positions in VIX futures typically only move about 55% as much as the VIX. This shortfall leaves volatility junkies clamoring for more—hence the 2X leveraged TVIX and UVXY.
  • TVIX attempts to track twice the daily percentage moves of the S&P VIX Short-Term Futurestm  index (minus investor fees).  This index manages a hypothetical portfolio of the two nearest to expiration VIX futures contracts.  Every day the index specifies a new mix of VIX futures in that portfolio.  For more information on how the index itself works see this post or the VXX prospectus.
  • TVIX’s tracking to its target index is not as good as UVXY.  I’ll get into the details of why later in the post, but on average you pay a premium of almost 2% for TVIX shares relative to the index it tracks, compared to a premium of 0.25% for UVXY.   For a security as volatile as TVIX this is not an especially big deal, but worth knowing.
  • If you want to understand how 2X leveraged funds work in detail you should read this post, but in brief you should know that the 2X leverage only applies to daily percentage returns, not longer term returns. With a leveraged fund longer term results depend on the volatility of the market and general trends.  In TVIX’s case these factors usually (but not always) conspire to dramatically drag down its price when held for more than a few days.
  • The leverage process isn’t the only drag on TVIX’s price. The VIX futures used as the underlying carry their own set of problems. The worst being horrific value decay over time.  Most days both sets of VIX futures that TVIX tracks drift lower relative to the VIX—dragging down TVIX’s underling non-leveraged index at the average rate of 7.5% per month (60% per year).  This drag is called roll or contango loss.
  • The combination of losses due to the 2X structure and contango add up to typical TVIX losses of 15% per month (85% per year). This is not a buy and hold investment.
  • On the other hand, TVIX does a decent job of matching the short term percentage moves of the VIX. The chart below shows historical correlations with the linear best-fit approximation showing TVIX’s moves to be about 93% of the VIX’s.    The data from before TVIX’s inception on October 3, 2011 comes from my simulation of TVIX based on the underlying VIX futures.


  • Most people buy TVIX as a contrarian investment, expecting it to go up when the equities market goes down.  It does a respectable job of this with the median TVIX’s percentage move being -4.8 times the S&P 500’s percentage move. However 18% of the time TVIX has moved in the same direction as the S&P 500.  So please don’t say that TVIX is broken when it doesn’t happen to move the way you expect.
  • The distribution of TVIX % moves relative to the S&P 500 is shown below:

TVIX ratio histo

  • With erratic S&P 500 tracking and heavy price erosion over time, owning TVIX is usually a poor investment. In fact, even the provider’s marketers who you’d expect to figure out a positive spin, state that “The long term expected value of your ETNs is zero.”  Unless your timing is especially good you will lose money.


How do Credit Suisse and VelocityShares make money on TVIX?

  • Credit Suisse, TVIX’s issuer, collects a daily investor fee on TVIX’s assets—on an annualized basis it’s 1.65% per year.  With current assets of around $250 million this fee generates approximately $4 million per year.  That should be enough to cover TVIX costs and be profitable, however I suspect their business model includes revenue from more than just the investor fee.
  • VelocityShares (now owned by Janis Capital Group) – gets a portion of the investor fee for its marketing and branding efforts.
  • Unlike an ETF, TVIX’s Exchange Traded Note structure does not require Credit Suisse to specify what they are doing with the cash it receives for creating shares.  The note is carried as senior debt on their balance sheet but they don’t pay out any interest on this debt.  Instead they promise to redeem shares that the APs return to them based on the value of its index—an index that’s headed for zero.
  • To fully hedge their liabilities Credit could hold the appropriate number of VIX futures contracts, but they almost certainly don’t because there are cheaper ways (e.g., swaps) to minimize their risks.  Given TVIX’s inexorable journey towards zero it would be tempting to assume some risk and not fully hedge their TVIX position, but I doubt Credit Suisse has a corporate culture that would support that. Instead I suspect they put fund assets not needed for hedging to work earning interest on relatively safe investments like collateralized repurchase agreements.  Earning even an extra percent or two annually on $250 million is real money.

February through March 2012 —When TVIX was not working

  • In February 2012 TVIX’s assets were growing rapidly, climbing several hundred million in a few days to reach $691 million. Normally this would be viewed as a very good thing by an ETN’s issuer, but Credit Suisse was not happy.  With a daily resetting fund like TVIX positions need to be rebalanced daily, and with a 2X leveraged fund the positions adjustments needed are equal to the day’s percentage move times the asset value of the fund.  So if TVIX was to move +30% in a day, not unprecedented, and the assets were at $690 million, then an additional $207 million in hedging securities would be need to be purchased that day.  We don’t know the reason, but likely because of the costs of doing that hedging or the risks of a swap’s counterparty defaulting Credit Suisse decided to stop creating new TVIX shares on February 22, 2012.  This prevented the assets of the fund from growing any larger.
  • You might think that limiting the number of shares in an ETN would be a good thing for the shareholders—if shares are scarce they might become more valuable. But for exchange traded products this is a very bad thing.   The share creation mechanism is essential to the process that keeps the fund closely tracking its underlying index.  Specifically if TVIX’s value gets too high relative to its index the authorized participants will normally short TVIX and hedge that position with VIX futures related securities to lock in a risk free profit.  They will continue to short sell, driving down TVIX’s price until the gap between TVIX and the index is too small for this arbitrage transaction to be profitable.
  • Short selling requires that there be shares available to be borrowed, and with Credit Suisse no longer willing to create new shares the supply of borrowable shares dried up completely. As a result TVIX’s share value became untethered from its index and by the end of March was trading at a 90% premium to the index.  In market cap terms there was around $277 million of bogus value in TVIX.
  • In late March 2012 Credit Suisse resumed share creation and the TVIX premium evaporated instantaneously—leaving a lot of stunned and poorer shareholders. Credit Suisse’s solution to their problem was to lay more of the risk on the authorized participants, requiring them to provide the necessary securities before they would create shares.  The extra cost of doing this is reflected in the premium (often around 1% or 2%) in TVIX’s price over its index.


TVIX—destroyer of wealth

  • According to’s ETF Fund Flows tool, TVIX’s net inflows have been around $1.8 billion since its inception in 2010.  It’s currently worth $220 million, so VelocityShares has facilitated the destruction of over one and a half billion dollars of customer money—so far.  I’m confident this overall destruction will continue.

TVIX’s race to zero attracts a lot of short sellers.  That strategy works most of the time, but if your plan is to ride out any volatility along the way be prepare to handle a 4X or more spike in TVIX”s value.  Most people are unequipped both financially and emotionally to handle this sort of reversal.

TVIX has a proven record as a cash incinerator, but its occasional upward spikes continue to attract speculators hoping to profit from the anguish of the general market.  A few traders with impeccable timing or good luck will make good money going long on TVIX.  Most will lose money.

TVIX split adj price 2004


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The SEC’s Circuit Breakers for ETFs Short Out

Updated: Aug 27th, 2015 | Vance Harwood

In the aftermath of the 2010 Flash Crash the SEC investigated ways to prevent the widespread disruption of prices that led to trades at absurd levels.  One of the outcomes was the creation of single stock circuit breakers across the entire market (SEC document).  These breakers are designed to halt trading at specific points to allow markets to stabilize before restarting.  Bigger drops triggered longer waits between restarts.

On Monday, August 24th we saw a test of these circuit breakers for Exchange Traded Funds (ETFs). They failed miserably.

Things weren’t as bad as the 2010 Flash Crash, but that’s faint praise indeed, and it wasn’t just small, low liquidity funds that suffered.

Guggenheim’s S&P 500 Equal Weight ETF (RSP) has $9.5 billion in assets, yet look at what happened on the 24th.


A 38% drawdown from its opening value.

David Nadig from does an excellent detailed analysis of what happened with RSP in Understanding ETF Flash Crashes, including the action of the SEC mandated circuit breakers that “moderated” the crash.

The bottom line is that the essential 2nd tier of liquidity providers for this ETF, the market makers / authorized participates were on the sidelines during this flash crash.

The first tier of liquidity is provided by the people/ institutions that want to buy or sell the ETF itself.  With an ETF there is a second level of liquidity providers that steps in if the trading price of the fund starts deviating significantly from the prices of the securities that underlie the fund.  These providers earn risk free profits by buying the underlying securities when relatively underpriced while simultaneously shorting the ETF or selling the underlying short when underpriced by the ETF and buying the ETF shares.

This action by the 2nd tier participants typically keeps ETFs trading close to their intraday indicative value (IV) — which is an index provided by all ETFs that computes the value of the underlying assets of the fund every 15 seconds. The chart below shows the IV values of RSP compared to its traded values.


The value of RSP’s underlying assets, its IV price, only went down 3.1% during the crash.

So the 2nd level liquidity providers were missing in action.  In fairness the IV values were probably not an accurate estimate of the prices of the underlying securities at that point—their bid / ask quotes were much wider apart than normal.  But even taking that into account the drop in value of RSP’s assets was nowhere near 38%.

Given the chaotic nature of the market I don’t blame the market makers from stepping back until things settled down a bit.  This report from iShares adds a considerable amount of detail on what happened.

Clearly the SEC did not foresee this situation.  The circuit breakers algorithms naively assumed that the listed quotes were representative of the value of the fund’s asset during chaotic times.

Clearly the SEC’s circuit breakers are a work in progress and aren’t working acceptably.  I think the next step should be to incorporate the IV value of the fund into the circuit breaker calculations.  Trading halts should be continued when the tracking error between the IV value and the quoted price exceeds some multiple of the normal tracking error, maybe 5X.  This would protect investors from being sold out at obviously bogus values.


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