Skip to main content

Early Review on Positive Convexity Trading...

"INNOVATION IS TAKING TWO THINGS THAT ALREADY EXIST AND PUTTING THEM TOGETHER IN A NEW WAY."

-Tom Freston

It's been a couple of weeks since I built out the optimization model and now that market volatility has finally arrived - as I have been predicting - we can take a good look at how it has performed.

The optimization model was built out of a curiosity I had about how to best structure trades and it produced a bit of an unexpected result...

http://tancockstradingblog.blogspot.com/2015/08/optimizing-equity-returns-using.html

http://tancockstradingblog.blogspot.com/2015/08/follow-up-on-qihu-short-trade.html


First, let's review how some of the original trades - that used options as protective overlays - posted on here have performed.



  • 6 Trades; 4 winners & 2 losers
  • Average return of positive 0.9%
  • Average position life of 20.8 market days
  • Average winning trade is up 6.9% on underlying moves of 9.8%
  • Average losing trade is -11% on underlying moves of 14.9%

Now, let's look at the optimized trades:

  • 3 Trades; 2 winners and 1 loser
  • Average return of positive 12.9%
  • Average position life of 5.7 days
  • Average winning trade is up 21.2% on underlying moves of 11.9%
  • Losing trade is -3.8% on underlying move of 5.4%

Wow.  That's a huge difference.  Looking at the results, the convexity benefits are clear and undeniable.

It's important to note that this is an extremely small sample size and the recent market volatility has certainly been tailor made for an options portfolio.  However, I think it would be a mistake to dismiss the results on these premises because the 'traditional' trades have had the same volatility and the results just don't stack up.

 Obviously, I'm going to continue to track this but the initial results are very promising.
Labels:

Comments

Post a Comment

Popular posts from this blog

Modeling Credit Risk...

     Here's a link to a presentation I gave back in August on modeling credit risk.  If anyone would like a copy of the slides, go ahead and drop me a line... https://www.gotostage.com/channel/39b3bd2dd467480a8200e7468c765143/recording/37684fe4e655449f9b473ec796241567/watch      Timeline of the presentation: Presentation Begins:                                                                0:58:00 Logistic Regression:                                     ...
Post a Comment
Read more

Variable Types for Principal Component & Factor Modeling

TRANSFORMING RAW DATA INTO INSIGHT & ACTIONABLE INFORMATION After reading the book Moneyball for the first time, I built a factor model in hopes of finding a way to finally be competitive in my fantasy baseball league - which I had consistently been terrible at.  It worked immediately.  By taking raw data and turning it into actionable information, I was able to solve a problem that had long perplexed me.  It was like discovering a new power.  What else could I do with this? Today, I build models for everything and have come a long way since that first simple spreadsheet but still use a lot of the same concepts. To build a traditional factor model, you would regress a dependent variable against a series of independent variables and use the resulting beta coefficients as the factor weights... assuming your resulting r-squared and t-test showed a meaningful relationship of course. Variables typically fall into one of two categories... continuous or dichotomous....
Post a Comment
Read more

Convexity as a Technical Indicator - Applications of the Second Derivative...

WHAT'S IN A NAME? In investment parlance, the term 'convexity' is typically reserved for the topic of fixed income risk; especially in regards to debt with embedded optionality where negative convexity is a prominent pricing factor.  However, it is important to recall that 'convexity' (or 'concavity', for that matter) is a mathematical measurement used to describe the second derivative of a continuous, nonlinear function on an interval. The issue with confining the term to a single connotation is that - for better or worse - investment tools have become increasingly nonlinear since the days of the 60/40 model and convexity is present in a number of applications.  The extent to which it has been circumscribed to a single asset class is evident in equity option jargon where the second derivative of the pricing function is called 'gamma' instead of convexity.  Of course, the argument could be made that the principle purpose of option vernacular is to c...
Post a Comment
Read more