Click here to download all the UVXY, front month VIX futures and contango and backwardation data since 2004. Note that the data was updated on **February 21 2021 00:29:30** (California time).

The calculated UVXY price is modeled based on VIX futures data. This allows to obtain the price of the UVXY since the VIX futures started trading (2004/march) until now. The price model can also be used to forecast future UVXY data based on VIX futures prices.

**This is the model used to generate the data:**

[1]For any given day n, calculate R(n) as twice the return that you would get by holding a combination of 1st and 2nd month VIX futures from day (n-1) to day n.Note that UVXY is calculated almost 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 UVXY case basically the same is done by calculating twice the return of buying 2nd month VIX futures with the proceeds of selling 1st month ones. Like that the model replicates twice the return of the S&P 500 VIX Short-Term Futures index for a single day.

[2]Apply UVXY’(n+1) = UVXY’(n) + UVXY’(n) x R(n+1)

Take as initial value the market value adjusted for splits of the UVXY on its first trading day: UVXY’(1)=82978.56

[3]Calculate the daily tracking error, F, by solving:

UVXY(n+1) = UVXY’(n+1) x F

With border conditions:

UVXY’(N)= Market price of the UVXY on the last trading day (N = day of the last close).

and

UVXY(1)=UVXY’(1)

The estimated UVXY’ model, based only on [2] 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 [3] is close to 2% per year and would be smaller if no management fees existed. That tracking error would only represent management fees if the UVXY accomplished perfectly its supposed goal of replicating twice the return of the S&P 500 VIX Short-Term Futures index.

This is how the model and the data looks like. Note that the screenshot was taken 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 UVXY goes down much more and vice versa. You can also see that the UVXY values have fallen from around 650 millions (yes millions) to 52.15, which works out to be an 80% compounded annual fall. Even though the fall has been enormous at certain periods of explosive volatility the UVXY has multiplied itself by 15, like from the period of less then 3 months from end August 2008 until the 20th November 2008. That means that even if you were to expect an annual 80% fall, it could be that in a couple of months the UVXY could have a 1500% gain ! In other words if you short just before a period of explosive volatility your short position could go 15 times against you (or even more). On the other hand if you think you can predict the next volatility explosion or if you want to hedge against it a UVXY long position could be helpful.

If you want to calculate new UVXY values you will have to implement the formulas guided by what I explained here or by the model definition explained at the beginning of this post. It is good that you implement how to calculate the data in order to have a better understanding of the UVXY dynamics. The UVXY historical prices are available from yahoo finance. The VIX future prices are available at the CBOE website. You can use that data to keep on updating the model since I may not update it regularly.

I also made models and generated data for other volatility funds (VXX, XIV, SVXY and TVIX). The VXX data back to 2004 is here and the XIV data back to that same date is available here.

You may not have time or do not wish to implement the pricing formulas or want future UVXY 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) Example of a parameterizable model to generate sell 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 UVXY price. You may also use it to make forecasts of how the UVXY will be affected depending on future VIX futures prices. It includes UVXY price forecasts based on VIX futures values, with which you can play with, to estimate what the UVXY 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 UVXY 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 UVXY model alone for 40 US$ and the VXX model alone for 35 US$). I have also a model for the XIV for sale, as well as a SVXY and a TVIX model. The more models you buy the less you pay, for example if you buy all the five models (UVXY+VXX+TVIX+SVXY+XIV) you pay 100 US$, less that the 40+40+40+35+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. 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 !

Cheers!

jrv

**PD1 -> Why doesn’t the UVXY 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 UVXY 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. What the product does is to go up or down twice as much as the VXX on a daily basis. If both the VXX and the UVXY traced their indexes perfectly and the VXX falls 10% on one day and then it goes up 11.11% on the next then it will reach the same level. On the other hand the UVXY would have fist fallen 20% (2×10%) and then the next day gone up 22.22% to reach a bit under 97.8% of its original value ! That means that by design the UVXY has a strong decay. Imagine that if that happens only in a couple of days what it could mean in the long run !?

**PD2 -> How is Contango/Backwardation calculated and why does it affect the VXX and therefore the XIV 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.

A way to have a fixed initial 2004 value is by arbitrarily fixing 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 UVXY started trading up until the last available market value. The model fixes the 1st and last available UVXY market values and uses all the VIX futures market values in between to calculate the UVXY and project it back to 2004 (see UVXY model 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 decline of the UVXY 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.

Cheers!

jrv