Click here to download all the SVXY, front month VIX futures and contango and backwardation data since 2004. Note that the data was updated on April 24 2017 06:02:35 (California Time).
The calculated SVXY price is modeled based on VIX futures data. This allows to obtain the price of the SVXY since the VIX futures started trading (2004/march) until now. The price model can also be used to forecast future SVXY data based on VIX futures prices.
This is the model used to generate the data:
 For any given day n, calculate R(n) as the return that you would get by holding a combination of shorted 1st and 2nd month VIX futures from day (n-1) to day n.
Note that SVXY is calculated almost exactly the same as the VXX as explained here. In the VXX case 2nd month VIX futures contracts are bought with the proceeds of selling 1st month ones. In the SVXY case, starting with only shorted contracts of 1st month vix futures, 2nd month futures are shorted and 1st month futures are bought with the proceeds (reducing the 1st month vix futures short amount). This means that at all times a combination of 1st and 2nd month vix futures is short, obtaining the exact inverse of the VXX.
 Apply SVXY’(n+1) = SVXY’(n) + SVXY’(n) x R(n+1)
Take as initial value the market value adjusted for splits of the SVXY on its first trading day: SVXY’(1)=10.52
 Calculate the daily tracking error, F, by solving:
SVXY(n+1) = SVXY’(n+1) x F
With border conditions:
SVXY’(N)= Market price of the SVXY on the last trading day (N = day of the last close).
The estimated SVXY’ model, based only on  gives higher values than the market prices. That difference with respect to the market data is used to calculate the daily tracking error. The tracking error in the final model  is close to 1% per year and would be smaller if no management fees existed. That tracking error would only represent management fees if the SVXY accomplished perfectly its supposed goal of replicating the daily inverse of the VXX.
This is how the model and the data looks like. Note that the graph was made with info up to the 5th May 2014 but the downloadable spreadsheet has updated data :
Looking at the data you can see that in low volatility periods, when the VXX is falling the SVXY goes up and vice versa. You can also see that while the VXX has fallen around 99.8% the SVXY has multiplied by 17. Thats equivalent to more than a 45% annual VXX fall and around a 30% SVXY annual increase. That’s maybe why some prefer to short the VXX instead of going long SVXY.
The SVXY has an almost 100% correlation with the XIV, for which I also made a model. The only difference might be that the SVXY tracks it’s index slightly better and that it offers options. For the reason just explained the following discussion applies also to the XIV.
The SVXY had a very long and big run up from below 4 until over 30. This happened in the low volatility years of 2004-2006 until when it peaked in February 2007. The extreme low volatility caused the VXX price to fall dramatically and correspondingly the SVXY to increase. When volatility increased in 2007 the SVXY fell violently. Notice how after bouncing under 15 in August 2007 the SVXY falls back much more to under 4, lower than in 2004 ! That was in the 2008/2009 period of extreme high volatility which simultaneously caused the VXX price to more than triple for a while. After the 2008/2009 period it made a big come back, temporarily interrupted by occasional falls, to reach 65 these last days. Where will it go next !? With such extreme volatility but nonetheless growing in such an irregular fashion it is understandable why many traders like the SVXY.
If you want to play conservative the goal is to buy it at the moment when you want to sell short the VXX. So at moments of extreme volatility, when it is highly probable that things are so bad that volatility will fall or not increase anymore. The product can have huge spikes if you manage to buy it in periods of very high volatility like in the end 2008 / begin 2009 or in August-Sept 2011).
I like the strategy of permanently shorting the VXX. Adding to it at extreme volatile periods. But having seen this historical behavior I think that I can have less risk if I short the VXX and also go long the SVXY in periods of extreme volatility. Note that it’s not a hedge. It’s in both cases betting on a volatility fall, on a reverse to the mean. But like this half of the amount (the SVXY part) has limited loss (can not lose more than 100%). While the other half has unlimited potential loss (if the VXX more than duplicates I could have more than 100% of paper losses). That strategy seems safer than going only short on the VXX since at least like that I reduce the unlimited potential loss to half of the amount. Actually the good thing about dividing the trade with both instruments (SVXY and VXX) is that I could use much less than half for the SVXY long because if it explodes, due to volatility falling from high levels, the SVXY could multiply itself, while the short VXX can theoretically at most go down to 0. The total amount at risk could be reduced since I would need a smaller amount to go long on the SVXY than the amount used to short the VXX to obtain the same profit. That’s a good argument for the SVXY traders. Not only do they limit the theoretical loss to just what they invested but they also invest less. So if you plan to short volatility it is worth considering to short the VXX and at the same time to go long on the SVXY. Both have advantages, VXX has in the long run an almost guaranteed fall while the SVXY offers a decent gain and limits the paper loss or margin call risk better and requires smaller amounts.
If you want to calculate new SVXY values you will have to implement the formulas guided by what I explained here or by using the model definition above. It is good that you implement how to calculate the data in order to have a better understanding of the SVXY dynamics. The SVXY historical prices are available from yahoo finance. The VIX future prices are available at the CBOE website. You can use them to keep on updating the model since I may not update it regularly. The VXX data back to 2004 is here.
You may not have time or do not wish to implement the pricing formulas or want future SVXY forecasts based on VIX futures values. I sell for 40 US$, or its equivalent on any currency accepted by paypal, the same spreadsheet with a data model for historical and future forecasted data. It includes:
1) Pricing formulas.
2) Forecast future values based on VIX futures values, with random market noise to see different outcomes.
3) Latest data update, and future updates if you don’t manage to do them.
4) Front month VIX futures, contango and backwardation data up to 2004.
5) Parameterizable model to generate buy signals.
6) Support on forecasting, modelling and updates.
Besides the historical and modelling data it has the advantage that you can understand how the VIX futures determine the SVXY price. You may also use it to make forecasts of how the SVXY will be affected depending on future VIX futures prices. It includes SVXY price forecasts based on VIX futures values, with which you can play with, to estimate what the SVXY price will be depending on different scenarios.
You can make the payment via paypal to my email [email protected] or with the button below. Via the paypal payment button, besides allowing you to use paypal, you may also chose cards such as visa, master, american express, discover or maestro. You may pay in dollars or the equivalent amount in the supported currency of your choice. Once paid I will be notified by email and I’ll send you the excel spreadsheet with the latest data and the formulas. I’ll gladly give you e-mail support if you need it. You may request an invoice by email if you specify it before or during the payment process.
Here below is the button to buy in US dollars:
And here is the paypal button in Euros for those who prefer to pay in that currency:
Alternatively if you are in Europe you can send a transfer, free among EU members, for the equivalent amount to my European bank account (ask me if you want it).
Note that if you trade both the SVXY and the VXX and are interested in the VXX pricing model I sell both for 60 US dollars, or the equivalent on any currency accepted by paypal. That would be cheaper than buying them separately (the SVXY model alone for 40 US$ and the VXX model alone for 35 US$). I have also a model for the XIV for sale, as well as a UVXY and a TVIX model. The more models you buy the less you pay, for example if you buy all the five models (SVXY+VXX+TVIX+UVXY+XIV) you pay 100 US$, less that the 40+35+40+40+25 = 180 US$ that you would pay by buying them separately.
Originally I made a VXX pricing model after I shorted the VXX in August 2011 and panicked when I realised I hardly knew how it was priced. Acquiring the knowledge gave me the strength to hold and turn a badly losing trade into a handsome profit. I still trade the VXX up until today. Having done it initially for myself I was surprised to see many people adopting it. Some among them who trade with other volatility funds asked me about their price models or how the funds they traded would have behaved in the past. I thought about it sometimes a bit but never gave it time to develop them because initially I preferred shorting the VXX. I finally did it when someone offered to pay for them and when I decided to start trading them myself.
It’s basically a tool you can use to guide and feel more confident and under control with your trading !
Anyone else interested in sharing modelling or trading ideas feel free to contact.
Hope this data is useful and if you find any interesting patterns by analyzing it please do not doubt to let me know !
PD1 -> Why doesn’t the SVXY return to the same level when the VXX goes up and down ? If the VXX goes from Price A to B and then back to A then the SVXY should also come back to its starting price. Unfortunately that does not happen. No product has been created that can reach close to that level. If it had then the SVXY would maybe be more than twice its value by now. It would be possible if a perfect inverse could be obtained, like the name of the product often (mis)leads to believe. But what the product does is to go up or down the opposite percentage of the VXX on a daily basis, but opposite in sign is not the same as an inverse. Many traders think that the SVXY should behave on the long run like a mathematical inverse and get disappointed and poorer when they realized it does not.
PD2 -> How is Contango/Backwardation calculated and why does it affect the VXX and therefore the SVXY price ? That is answered in the VXX blog post where it says PD4.
PD3 -> Why does the calculated data change as more data becomes available ?
That’s due to the way that the model is designed to learn from available market data and extrapolate. All calculated data changes depending on the amount of market data that the model learns from. Not only does the first 2004 calculated value change but all of them do.
I could fix an initial 2004 value arbitrarily choosing it. But that would be probably an error since you can at most estimate it with ever growing and changing available data. That makes that initial value a variable too. At most you can calculate it by learning from the available market values.
Differences in calculated values happen every day. They are normal because the data is calculated all the way backwards to 2004 by taking into account all the available market data since the SVXY started trading up until the last available market value. The model fixes the 1st and last available SVXY market values and uses all the VIX futures market values in between to calculate the SVXY and project it back to 2004 (see the SVXY model definition above).
For example from 2009 to 2012 there is one less year of data than from 2009 to 2013. So its normal that the calculated values, including the first calculated value, on both cases differ.
The changes may look big and concerning but they are actually small if you consider that the annualized percentage growth of the SVXY in both cases is almost the same.
You can also graph different series of calculated data taken in different periods and you will notice slight differences. No calculated data series is better than another, that depends on the market data quality. Most probably the more market data that you feed the model the better that the calculated data will be, since in such case the model will use more information to learn from.