Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    New Data Analytics Breakthroughs Give eCommerce Startups a Fighting Chance
    New Data Analytics Breakthroughs Give eCommerce Startups a Fighting Chance
    6 Min Read
    How Data Analytics Is Reshaping Patient Financing Decisions
    How Data Analytics Is Reshaping Patient Financing Decisions
    13 Min Read
    business using business intelligence
    How to Use a Competitive Intelligence Dashboard to Turn Market Data Into Smarter Marketing Decisions 
    9 Min Read
    unusual trading activity
    Signal Or Noise? A Decision Tree For Evaluating Unusual Trading Activity
    3 Min Read
    software developer using ai
    How Data Analytics Helps Developers Deliver Better Tech Services
    8 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: SKF: Inverse Construction and Volatility
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Analytics > Predictive Analytics > SKF: Inverse Construction and Volatility
Predictive Analytics

SKF: Inverse Construction and Volatility

Editor SDC
Editor SDC
8 Min Read
SHARE

I previously explained why market returns should be lognormally distributed with positive daily expectation (not continuous). However, imagine a security that is artificially constructed to make daily returns opposite of what it is based on. Then it should have negative daily expectation. This is the Ultrashort Financial Sector ETF, SKF, with a dash of leverage added in.

If you look at the chart of UYG and SKF over one year:

it is obvious that the mean of the two return paths is way less than 0%. UYG is the 2x leveraged financial sector ETF and SKF is the inverse of that. SKF is at +32.99% and UYG is at -89.54% from where they were a year ago. Intuitively you probably expect that the sum of the returns is equal to 0%. If not, it should be possible to constuct a pairs trading strategy which shorts both of them and makes a high return, e.g. -1*(32.99-89.54)/2= 28.75% annualized, with very, very low risk (since they move opposite each other daily).

First of all, how are SKF’s returns engineered?

Some kind of swap- basically a bet on the direction of the financial sector index put out by Dow Jones.

More Read

Image
That’s Sick! Text Mining and Words with Multiple Definitions
Here’s how decision management simplifies process management
Stratified Sampling vs. Posterior Probability Thresholds
Two Books of Interest
Some thoughts on Next Generation Warranty Systems

However I find it more intuitive put another way. Consider this thought experiment:
You think…


I previously explained why market returns should be lognormally distributed with positive daily expectation (not continuous). However, imagine a security that is artificially constructed to make daily returns opposite of what it is based on. Then it should have negative daily expectation. This is the Ultrashort Financial Sector ETF, SKF, with a dash of leverage added in.

If you look at the chart of UYG and SKF over one year:

it is obvious that the mean of the two return paths is way less than 0%. UYG is the 2x leveraged financial sector ETF and SKF is the inverse of that. SKF is at +32.99% and UYG is at -89.54% from where they were a year ago. Intuitively you probably expect that the sum of the returns is equal to 0%. If not, it should be possible to constuct a pairs trading strategy which shorts both of them and makes a high return, e.g. -1*(32.99-89.54)/2= 28.75% annualized, with very, very low risk (since they move opposite each other daily).

First of all, how are SKF’s returns engineered?

Some kind of swap- basically a bet on the direction of the financial sector index put out by Dow Jones.

However I find it more intuitive put another way. Consider this thought experiment:
You think the financial sector is going to drop more over the next 3 days- how will you replicate the daily returns of UYG, inverted? For the sake of example, imagine the price of UYG is currently at $100. Your first inclination is to short UYG and then just stay in that position for 3 days. The first day it falls 10%, you now have $10/10% of available, uninvested capital. The next day it is down another 10%, i.e. -9$ = ($100-$10)*-10%. But this only translates to you having made 9%! Next day, another 10% i.e. $8.1 = ($90-$9)*10%. Now it’s way off, only 8.1% when you aimed for 10%. The total is 127.1

Obviously the problem is that you had uninvested profits sitting on the sideline at the beginning of each day. If you cover the short at the end of the first day and then use all your money, $110, to open a new short position on UYG for the next day, when it falls 10% on day 2 you will make 11$. And the next day, covering and reinvesting in a similar fashion you will be up to $133.1 total. The trick is compounding the short position by reinvesting. It’s very, very risky because you essentially buy high and sell low to to match the daily returns (remember- buy low and sell high is supposed to be how to make money).

Take a look at p. 18-20 (20-22 of the pdf doc) of Statement of Additional Information for Proshares Trust. The colored tables show exactly how volatility and expected return interact, which I explored in the previous note. It’s quite well hidden, even the watered down version has only a tiny little link embedded on the SKF product webpage:

Another “problem” with SKF is its excessive leverage. Using data for the year up to 2/9/09, this Excel sheet shows that the optimal leverage would be .569 . Anything less than one means it’s overleveraged. I used Excel’s ‘solver’ add-in to find how much leverage maximized ending wealth, but feel free to test different numbers, including less than 0, de-inverting it. The cell you modify is in orange and the effect on final price can be seen in blue. (fyi spreadsheet methodology: leveraged returns are the daily closing price ratios, minus one, times the leverage multiplier. Finally this is turned back into a stream of prices, with the oldest price on 2/12/08 being the basis- the formulas are simple) Basically SKF is inappropriate for anyone who wants to hold it for a long time because it goes over the optimal Kelly leverage.

I like the Ultrashorts because I’m too young to open my own margin account, but it’s hard to look past the steady historical downtrend of the Ultrashort ETFs. However they make for interesting studies in financial engineering and position sizing and probability. I doubt most investors understand exactly what they are getting. I’ve been doing quite a bit of trading (compared to fundamental long-only “investing”) to enjoy the volatility of the past year and the Ultrashorts can be profitable. It’s nice having a short position that cannot lose over 100% no matter what, unlike a normal short.

Unfortunately it doesn’t look like I’ve found an arbitrage opportunity. Poor performance is just the result of uncommon volatility. Please leave a comment if you have any ideas related to this or anything else – I always may have missed something.

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

New Data Analytics Breakthroughs Give eCommerce Startups a Fighting Chance
New Data Analytics Breakthroughs Give eCommerce Startups a Fighting Chance
Analytics Big Data Exclusive
data driven businesses
How Data-Driven Businesses Choose Storage That Reduces Risk and Drag
Big Data Exclusive
Operational Data Becomes Business Value in the Age of AIoT
Operational Data Becomes Business Value in the Age of AIoT
Big Data Exclusive Internet of Things
growth guide
Growing Smarter: The Role Of Strategic Partnerships From Startup To Scale
Infographic News

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

“Complex systems science is a new field of science studying how parts of a system give rise to its…”

1 Min Read

Careful with the S-word

5 Min Read

The power of business analytics

5 Min Read

Better than Brute Force: Big Data Analytics Tips

10 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

ai chatbot
The Art of Conversation: Enhancing Chatbots with Advanced AI Prompts
Chatbots
giveaway chatbots
How To Get An Award Winning Giveaway Bot
Big Data Chatbots Exclusive

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?