Volatility Related Indexes and Tickers

Sunday, December 7th, 2014 | Vance Harwood

Unless you have access to a Bloomberg terminal or something similar finding quotes and historical data for volatility indexes can be an adventure.  Below I’ve assembled links to the online resources that I’ve been able to find.  Links marked with a “$SFI” are historical data sets that I offer for sale—they don’t match the official indexes exactly, but they are very close.

In many cases data is available from multiple sources.  I did not attempt to list all of them.

If you are looking for symbols/tickers for volatility exchange traded products then you should go to this post where I list information on all 23 USA traded volatility style funds.  Simulated histories for some of these funds back to 2004 are available here.

Historical data from different sources can differ—often because they use different closing times.  Sites like Yahoo and Google Finance use standard NYSE hours, but the CBOE’s hours are different (close is 4:15 ET) and open times vary.  For example with the advent of near 24 hour trading on VIX futures the open time for VIX futures for Tuesday through Friday is 4:30PM the previous day and Sunday at 5PM is the opening time for Monday.  When used for computing other indexes (e.g., when VIX is used in computing the index used by VQT), the CBOE data should be used.

If you have an account with Fidelity’s Active Trader Pro  you can get historical intra-day data for many volatility tickers by exporting data from their charts.  Schwab’s StreetSmart Edge allows export of watch list information, including option Greeks.


Standard long volatility indexes

Index Quotes/Charts Used by:(not exhaustive) Historical Data Resources Description
SPVXSTR ?? VXX Barcays3 (since Jan 09)
$SFI (Mar 04-Feb12)
Methodology Short Term Volatility Total Returns1
(includes OHLC)
Market Watch
$SFI (Mar 04-Feb12)
Methodology Short Term Volatility Excess Returns2
SPVXMTR ?? VXZ Barclays3 (since Jan 09)
$SFI (since Mar 04)
Methodology Mid Term Volatility Total Returns1
SPVXMP MarketWatch
(single dates)
$SFI (since Mar 04)
Methodology Mid Term Volatility Excess Returns2


  1. The term “Total Returns” (TR) denotes that dividends/interest is included in the index. For example for SPTR it would be the dividends from the underlying 500 stocks in the S&P 500. In the case of SPVXSTR it would be interest from 13 week treasury bills.
  2. “Excess Returns” (ER) in this context indicates that the calculation does not include dividends or interest.
  3. Barclays data includes some non-USA trading days, the values are carried over from the previous trading day

Related Posts


Hedged style volatility fund indexes 

Index Quotes/Charts Used by Historical Data Resources Description
$SFI (since Mar 04)
Barclays (VQT IV only)
Methodology S&P 500® Dynamic VEQTOR Index TR
Used within VQT/PHDG calculations FT.com Methodology S&P 500® Dynamic VEQTOR Index ER
SPVQSER Yahoo Finance
VQTS FT.com Methodology S&P 500® VEQTOR Switch Index ER
SPVXVSP Google Finance
SPXH FT.com Methodology
33% 2X ST
66% -1X ST
White Paper
Volatility Hedged Large Cap Index
SPVXTRSP Google Finance
TRSK FT.com Methodology
45% 2X ST
55% -1X ST
White Paper
Tail Risk Hedged Large Cap Index
SPDVIXTR ?? XVZ Barclays3
$SFI (since Mar 04)
Methodology S&P 500 Dynamic VIXFutures Index
VXTH CBOE VIXH Methodology
White Paper
CBOE VIX Tail Hedge Index

Related Posts


VIX style Indexes and Settlement quotes 

Index Quotes/Charts Used by Historical Data Resources Description
Google Finance
VXST options & futures CBOE (since Jan-2011)
White Paper Measure of 9 day IV of (SPX) Index options.
VIX® Yahoo ^VIX
VIX options & futures VQT
CBOE (since Jan1990) White Paper
Near real time calc graph
Measure of 30 day IV of (SPX) Index options
VIXMO Google Finance
indexcboe: VIXMO
Calc the VIXMO—the easy Part VIX index previous to Oct 6, 2014
Measure of 30 day IV of (SPX) Index options using SPX monthly options
VXV Yahoo ^VXV XVZ CBOE (since Dec 2007)
Measure of 3-month IV of (SPX) Index options.
VXMTSM Google Finance
CBOE (since Jan 2008)
Measure of 6-month IV of (SPX) Index options.
SVRO Yahoo ^SVRO VXST options & futures CBOE Settlement process Exercise-settlement value for VXST options & futures
VRO Yahoo ^VRO VIX options /&futures CBOE Settlement process Exercise-settlement value for VIX options  & futures
VVIXSM Yahoo ^VVIX CBOE (since March 06)
White Paper VIX methodology applied to VIX options (VIX of VIX)

Related Posts


VIX Style Calculation Indexes  (used by CBOE to compute VXST, VIX, VIXMO, VXV, VXMT)

Index Quotes/Charts Used by Historical Data Resources Description
VSTN Google VXST Google White Paper CBOE Near-term VXST Index
VSTF Google VXST Google White Paper CBOE Far-term VXST Index
VIN Google4 VIX Google4 White Paper CBOE Near-term VIX Index
VIF Google4 VIX Google4 White Paper CBOE Far-term VIX Index
VINMO Google VIXMO Google CBOE Near-term VIX monthly only
VIFMO Google VIXMO Google CBOE Far-term VIX monthly only
VIX(#)  CBOE VIX CBOE (since 2010) CBOE per month VIX calculations


  1. The CBOE changed its VIX calculation on 21-Oct-14 to use weekly options bracketing the 30 day VIX target expectation. Before that the VIN/VIF values reflect the old calculation that only used monthly SPX option series.

Related Posts


Support Indexes

Index Quotes/Charts Used by Historical Data Resources Description
13Wk T Bills ?? VXXVXZ US Treas
Conversion 13wk US Treasury bills
SPX  Yahoo (^GSPC) Yahoo SPX total returns (dividends applied but not re-invested)
SPTR ?? VQTPHDG CBOE(since 1988)
Eoddate (reg required)
SPX total returns with dividends reinvested



VIX Futures

Ticker Quotes/Charts Used by Historical Data Resources Description
VXST CBOE (e.g, 2VSW/Z4)  select VSW on Futures VXST options CBOE VSW VXST Futures, Options, Index Futures on VXST index
VIX/month-code/last digit of year CBOE (e.g. vix/z4, VX series)
Google (e.g.  indexcboe:VIXJan)
VIX optionsVolatility Funds CBOE


Month Codes
Expiration Calendars
Futures on VIX index

Related posts



Some other interesting indexes currently not used by volatility funds

Index Quotes/Charts Ratio Historical Data Resources Description
SPVXTRMP / SPVXTRMT Google Finance 60% 2X MT long, 40% -1X ST short FT.com Methodology
ETF.com article
S&P 500  VIX futures Tail Risk ER Mid-Term
SPVXVMP/ SPVXVMT Google Finance 45% 2X MT long, 55% -1X ST short FT.com Methodology
ETF.com article
S&P 500  VIX futures variable long / short mid-term
SPVXVHSP / SPVXVHST Google Finance 10% 2X ST long, 90% -1X ST short FT.com Methodology
ETF.com article
S&P 500  VIX futures volatility hedged Short term
SPVXVHMP / SPVXVHMT Google Finance 30% 2X MT long, 70% -1X ST short FT.com Methodology
ETF.com article
S&P 500  VIX futures Tail Risk Midterm


Graphical VIX & VIXMO calculations

Wednesday, November 12th, 2014 | Vance Harwood

The chart below graphically represents the calculation for the VIX® and the legacy VIX (ticker VIXMO) which was used from September 22, 2003 through October 5th, 2014.  My apologies for the small size / non-expandable format, but this was the best near real time (20 minute delayed) solution I could figure out using Google Sheets. The actual VIX is located on the black dotted line in the left center of the graph. Click here for a larger snapshot for 12-Nov-2014. The VIX now uses an interpolation between two VIX style calculations (VIN and VIF) on SPX options series that are a week apart—bracketing the 30 day target horizon of the VIX.  The legacy calculation uses SPX monthly options (now published as VINMO and VIFMO) which requires significantly longer interpolation/extrapolation periods.

Since its inception on October 6th, 2014 the new VIX has often differed significantly from the older calculation, often running 5% or more lower than the legacy number.   This is disconcerting and I initially wondered if the reduced volume/open interest of the SPX weekly options used in the new calculation or some other factor was distorting things, but as I look at the data I’m becoming comfortable with the new calculation as a significant improvement in the accuracy of the index.

The dynamically updated chart above uses delayed quotes from Yahoo Finance.  For more information on these VIX calculations see Calculating the VIX and Calculating the VIXMO.

The VXST is the CBOE’s 9 day version of the VIX, and  VXV is the CBOE’s 93 day version.

There are two somewhat parallel markets associated with general USA market volatility: the S&P 500 (SPX) options market and the VIX Futures market.  SPX option prices are used to calculate the CBOE’s family of volatility indexes, with the VIX® being the flagship.  VIX futures are priced directly in expected volatility for contracts expiring up to 9 months out.  The nearest VIX Future synchronizes with the VIX once a month—on its expiration date.

Additional resources:

Related Posts

Calculating the New VIX—The Easy Part

Tuesday, October 21st, 2014 | Vance Harwood

The movements of the CBOE’s VIX® are often confusing.  It usually moves the opposite direction of the S&P 500 but not always.  On Fridays the VIX tends to sag and on Mondays it often climbs because S&P 500 (SPX) option traders are adjusting prices to mitigate value distortions caused by the weekend.

In addition to these market driven eccentricities the actual calculation of the VIX has some quirks too.  The VIX is calculated using SPX options that have a “use by” date.   Every week a series of SPX options expire.  This schedule of expirations forces a weekly shift in the VIX calculation to longer dated options.  For many years the CBOE’s VIX calculations only used monthly SPX options, but starting October 6th, 2014 it switched to using SPX weekly options when appropriate.  See “Why the Switch” section towards the bottom of this post for more information.

The VIX provides a 30 day expectation of volatility, but the volatility estimate from SPX options changes in duration every day.  For example, on October 13, 2014 the SPX options expiring on the 7th of November provide a 25 day estimate of volatility, while the November 14th options provide a 32 day estimate.  In this case to get a 30 day expectation the VIX calculation uses a weighted average of the volatility estimates from these two sets of November options.

The newly updated S&P 500 VIX calculation is documented in this white paper.  It computes a composite volatility of each series of SPX options by combining the prices of a large number of puts and calls.  The CBOE updates these intermediate calculations using the ticker VIN for the nearer month of SPX options and VIF for the further away options.  The “N” in VIN stands for “Near” and the “F” in VIF stands for “Far”.  These indexes are available online under the following tickers:

  • Yahoo Finance as ^VIN, ^VIF
  • Schwab $VIN, $VIF; historical data available
  • Google Finance INDEXCBOE:VIN, INDEXCBOE:VIN; historical data available
  • Fidelity:  .VIN, .VIF;  limited historical data

The final VIX value is determined using the VIN and VIF values in a 30 day weighted average calculation.  Graphically this calculation looks like the chart below most of the time:



As shown above the VIX value for October 13th is determined by averaging between the November 7th SPX options (VIN) and the November 14th SPX options (VIF) to give the projected 30 day value.  If you look closely you can see that the interpolation algorithm used between VIN and VIF does not give a straight line result; I provide calculation details later in “The Weighted Average Calculation” section

The chart below shows the special case when the VIX is very close, or identical to the VIF value.



Wednesdays are important days for the VIX calculation:

  • The VIX calculation is dominated by the VIF values.
  • The SPX options used switch such that the old VIF becomes VIN and the options with 36 days to expiration become VIF.
  • Once a month on a Wednesday VIX futures and options expire (expiration calendar).  Soon after market open a special opening quotation of VIX called SOQ is generated.  Its ticker is VRO and it’s used as the settlement value for the futures and options.  Unlike the VIX’s normal calculation, the SOQ uses actual trade values of the underlying SPX options not the mid-price between the bid and ask.  Only one series of options, the ones with exactly 30 days to expiration are used.

Although SPX weekly options are available for 5 weeks in the future, the VIX calculation uses the SPX monthly options (expiring the 3rd Friday of the month) instead of the weeklies when they fit into the 24 to 36 day window used by the calculation.   The SPX weeklies expire at market close on Friday but the monthly options expire at market open on Friday.  By using these monthly options the CBOE keeps the VIX futures / options settlement process identical with the previous month based VIX calculation.

Why the Switch?

The chart below illustrates how the CBOE changed the VIX calculation methodology.


This particular snapshot  shows the old VIX calculation (ticker: VIXMO) doing an extrapolation using SPX monthly options expiring November 22nd and December 20th (11 and 39 days away from the 30 day target)—a hefty distance.  If you would like more details about the old VIX calculation see “Computing the VIXMO—the easy part“.  The new VIX calculation on the other hand always does an interpolation over a much shorter period of timenever using options with expirations more than +-7 days from the 30 day target.  This CBOE article gives a good overview of the advantages of the new approach.

If you look closely at the chart, you can see that in this case the VIX calculation using the two methods arrives at slightly different answers (black line).  The new method gives a result of 21.16, 1.5% higher than the old method’s 20.85.  While I’m confident that the new calculation will be better in the long run because of the tighter VIN / VIF brackets I do have some concerns about the current volumes and low open interest in the SPX weekly options that are 4 to 5 weeks out.   I have seen the VIX / VIXMO differ by up to 5%—so for the time being I’m keeping both indexes on my watch lists. 

The Weighted Average Calculation

If you want to compute the VIX yourself using the VIN and VIF values you can’t just do a linear interpolation / extrapolation because volatility does not vary linearly with time.  Instead you have to convert the volatility into variance, which does scale linearly with time, do the averaging, and then convert back to volatility.  The equation below accomplishes this process.


Related Posts

The Volatility Landscape—October 2014

Friday, October 10th, 2014 | Vance Harwood

VIX + VIX Future Term Structure May 2011- March 2012


  • CBOE
    • On October 6th, the CBOE introduced an updated methodology for computing the VIX index.  Previously the index was computed using SPX options with 3rd Friday of the month expirations, but now SPX weekly options are used when they bracket the 30 day expectation maturity of the VIX.  For example the VIX for 9-October-2014 computes an implied volatility value for 8-November—since options don’t exist that expire on that date an interpolation is used between the 7-Nov-2014 and the 14-Nov-2014 SPX weekly options.  Calculations via the old methodology are reported under the VIXMO ticker.    While technically I think this is a sound move, the currently much lower volumes on SPX weekly options that far out may cause the new VIX to significantly diverge from the old VIX  (so far I’ve seen variations as high as +5%).   The CBOE’s new methodology does not impact the expiration process for VIX futures and options; they will continue to use the 3rd Friday options for that calculation.
    • The CBOE’s expansion to near 24 hour trading for VIX futures has gone well and they are planning to expand the trading hours of VIX and SPX options starting October 21st to the same Sunday Afternoon 6pm ET to Friday Afternoon at 4:15 ET span.  For more information see this note from the CBOE.
    • VXST futures have been trading since February 13th, 2014.  So far their reception has been lukewarm with volumes running around 50 per day, but their prices do seem to track the VIX index pretty well, significantly better than VIX futures.   For more see VXST FuturesNot a Bad Proxy for the VIX.
  • PHDG gains momentum
    • This PowerShares fund uses the same VEQTOR methodology as VQT, but it’s an ETF rather than an ETN.  Its assets under management have climbed to $431 million, gaining momentum in its quest to top Barclay’s $640 million VQT (source ETFdb.com).
    • PHDG distributes a dividend (currently around 1.6% per year).  Of the 22 USA volatility based funds only PHDG and VelocityShares’ SPXH and TRSK distribute dividends.
    • Recently options became available (30-July-2014) on PHDG.  I’ve wanted options on the VEQTOR based funds (VQT and PHDG) for a long time because they raise the possibility of a Covered Call Strategy That’s Long Volatility.   Unfortunately the market maker isn’t showing much enthusiasm, with huge spreads and no bid prices on ATM options.




White Papers

  • Volatility: A New Return Driver?
    • A good non-mathematical overview of volatility, volatility products including futures and a couple example trading strategies using volatility Exchange Traded Products
  • The VIX-VIX Futures Puzzle?”
    • A technical paper testing the forecast accuracy of VIX futures that includes a comprehensive technical overview of the VIX, VIX Futures, and volatility term structures.  It skips the calculus but provides a clear description and comprehensive formulas.
  • Variance and Convexity: A Practitioner’s Approach
    •  My favorite paper from the CBOE’s 2013 Risk Management Conference.  Sparse and very technical it addresses some of the differences between variance and volatility with regards to VIX futures.  Most of the other papers from the conference are posted here with links associated to the agenda items.
  • VIX White Paper
    • Complete details on the VIX calculation, recently updated (8-Oct-2014) to reflect the new methodology that utilizes SPX weekly options



Wish List

The Myth of Option Weekend Decay

Sunday, October 5th, 2014 | Vance Harwood

While doing simulations on volatility and the square root of time I started thinking about how options experience time—is it calendar time, market time, or something in-between?  The CBOE’s VIX® calculations use calendar time, a 365 day year, but most option gurus recommend using a 252 day year for volatility calculations—the typical number of trading days per year in the USA markets.

When it comes to option decay most people, including the gurus, believe that option values decay when the markets are closed—a position I believe conflicts with the 252 day approach to annualizing volatility.

The experimental discovery that led to the current theory of option decay occurred in 1825 when the botanist Robert Brown looked through his microscope at pollen grains suspended in water and noticed they were moving in an irregular pattern.  He couldn’t explain the motion but later physicists including Albert Einstein showed it was the result of water molecules randomly colliding with the pollen. This effect was named “Brownian Motion” in honor of Mr. Brown.

If you effectively stop time in Mr. Brown’s experiment (e.g., freeze the sample), the pollen will stop moving.  Or if you close a casino for a day (probably a better model for the market) the net worth of the associated gamblers stops dropping.

Defenders of the calendar time approach point out there are many activities / events with broadband impact that can move the value of the underliers while the market is closed.  Things like extended trading hours, activity in foreign markets, corporate announcements, geopolitical events, and natural disasters.

However it occurs to me that most noteworthy events that happen outside of market hours tend to be bad news.  For example, I’m not expecting to see headlines any time soon stating, “ISIS disbands, ‘We realized it was all a terrible misunderstanding’”, or “Harmless landslide reveals huge cache of gold”.  This tendency towards negative moves is reflected in the average annual growth rate of off market hours for the last 20 years, -0.37% vs +9.59% for market hours.   And bad news tends to make option prices go up…

If option time is still running when the markets are closed I would expect the market’s opening value to be different from the closing value.  Below is a quick look at the last 20 years of data:

S&P 500 Returns 1-Jan-1994 through 22-Aug-2014 (5197 market days)

Market Time: Open to Close (occurrences) Market Time: Close to Open (occurrences)
No change 0.1%  (5) 58% (3046)
Change less than 0.05% 5.2%  (270) 81% (4249)
Changes >= 1% 27% (1396) 0.04%  (3)

I was surprised how often the market opened at no-change from the previous close (3046 times) and how seldom it has gapped overnight more than +-1% (3 times).

So what?

So far my arm-waving arguments give the edge to market time over calendar time, but really, so what?

Practically there are two things where this makes a difference: the dynamics of option decay and the accuracy of implied volatility calculations on soon to expire options.

Option Decay

Novice options traders are usually disappointed if they try to profit from Theta decay over the weekend.  If the underlying doesn’t move, options prices typically open on Monday unchanged from the Friday close.  Commentators explain this phenomena noting that market makers, not wanting to be stuck with Theta losses over the weekend, discount prices, overriding their models before the weekend to move their inventory—just like a fruit vendor would.

I think the market makers are right for the wrong reason.  Their computer models are (or at least were) based on calendar day assumptions—which assume option decay during the weekend.   By overriding their models they are pricing according to what really happens—no decay when the market is closed.

Annualizing factors  

For longer term expectations of volatility it doesn’t matter much which approach you use.  For options expiring a month from now the differences in implied volatility are only a few percent between the 365 vs 252 day models.  However for shorter expirations the differences can be dramatic.

The chart below compares per minute values between the two annualizing approaches and shows the percentage difference.  The calendar based approach is the black line and the green line is the market time.  Notice how the difference peaks at Monday open and drops to near agreement at Friday close.


This “weekend” effect is sometimes visible in the CBOE’s VIX index, and is pretty dramatic with their new shorter term VXSTSM index—not surprising since this new index is based on S&P 500 (SPX) option prices with at most 9 days until expiration.

There are good reasons to use a calendar day approach to annualization.  It isn’t sensitive to holidays, unexpected market stoppages, or differences in trading calendars between countries.  I expect that’s why it became a de facto standard in the volatility world.  But the rise of shorter term volatility products like weekly options has shifted the volatility landscape enough that I think we need to at least know what is technically correct.

 An analytic approach to a solution

Normally we take a shorter term (e.g., daily) volatility and multiply it by the appropriate annualizing factor to get the annualized volatility.  Since the annualizing factor is the thing in question I decided to take the historical annual volatility for the last 64 years of the S&P 500 and divide it by the daily volatility to solve for the actual historical annualizing factor.

First I validated this approach with a Monte Carlo simulation1 that computed the theoretical annualizing factor for a simulated 64 year market period—and then repeated that exercise 10000 times to get the statistics of the calculation.  I then applied the same calculation to the S&P 500’s returns2 over the last 64 years. The result:


The square of the annualizing factor comes is only 0.87% from the theoretical median value3 of 252 and the actual S&P 500 result of 243.5 is only 2.5% from the median value.  The S&P result of 243.5  is almost 3 sigma away from the competing answer of 365.

The S&P 500 data is consistent with a 252 day based annualizing model—which doesn’t support option decay while the market is closed.  The data also indicates that when you see suspiciously high short term volatility numbers at the beginning of the week you should chalk it up to flawed algorithms, not anything real in the market.



  1. For each day of the simulation I used the standard deviation of the previous 252 days natural log of daily returns for the short term volatility number.  For the yearly return I used the simulated market value one year hence divided by the current day’s market value.  Volatility drag is an important second order effect that needs to be included in the calculations.
  2. I offset the actual results by the average annualized growth rate to compensate for the non-zero mean of actual returns over the last 64 years
  3. My simulation results have a median value of 252.2 (0.08% error) if I use a volatility drag coefficient of 0.6 instead of the standard 0.5.  I believe my model slightly under corrects for volatility drag.