Skip to main content

Using Options to Drive Returns...

In working with the new optimization model, I've definitely noticed some interesting things as I mentioned in yesterday's post detailing the QIHU short trade.

First off, I had originally intended on setting up QIHU as a long position as it looked to be in a bullish pattern and that's what I used when describing the new model.  However, when calculating the expected return to the September options expiration, the exponential return model produced a significantly negative projection of -4.68%.

In seeing this, I ran the model to short the position using both a traditional equity approach - with an option overlay as protection - and as a traditional long.  However, I used a constraint in the long model that gave me a short position using options as the return driver and the stock as the overlay protection.  The contrast between the model outputs is definitely worth examining.

Here's the output from the traditional short position:


Expected values from this trade ranged between $250 - $500 with a total trade cost over $17k.  Also, notice the value of the trade with a hypothetical stock price of $50, it's roughly $2,750.


Now, here's what the model produced for the option short:


The expected values for this trade range between $500 and $1,100 with a total trade cost of less than $10k.  Also the profit on the trade with the stock at $50 is over $6k.

In fairness, the first trade has a lower max loss figure by about $500... but the second trade clearly illustrates the leverage gained from using options as the return driver.

Comments

Popular posts from this blog

Modeling Black-Litterman; Part 2 - Incorporating Manager Views

  "The 'radical' of one century is the 'conservative' of the next." -Mark Twain In this series, I'm going to explore some of the advances in portfolio management, construction, and modeling since the advent of Harry Markowitz's Nobel Prize winning Modern Portfolio Theory (MPT) in 1952. MPT's mean-variance optimization approach shaped theoretical asset allocation models for decades after its introduction.  However, the theory failed to become an accepted industry practice, so we'll explore why that is and what advances have developed in recent years to address the shortcomings of the original model. The Black-Litterman Formula The Black-Litterman formula incorporates two distinct inputs; the first is the Implied Equilibrium Return Vector we constructed in Part 1, the second is a series of vectors and matrices that incorporate a manager's views/forecasts of the market.  The product of the formula is an updated Combined Expected Excess Return ...

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....

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...