Wednesday, July 16, 2014

New SABR Formulae

In a talk at the Global Derivatives conference of Amsterdam (2014), Pat Hagan presented some new SABR formulas, supposedly close to the arbitrage free PDE behavior.

I tried to code those from the slides, but somehow that did not work out well on his example, I just had something very close to the good old SABR formulas. I am not 100% sure (only 99%) that it is due to a mistake in my code. Here is what I was looking to reproduce:
Pat Hagan Global Derivatives example

Fortunately, I then found in some thesis the idea of using Andersen & Brotherton-Ratcliffe local volatility expansion. In deed, the arbitrage free PDE from Hagan is equivalent to some Dupire local volatility forward PDE (see, so Hagan just gave us the local volatility expansion to expand on  (the thesis uses Doust, which is not so different in this case).

And then it produces on this global derivatives example the following:

The AB suffix are the new SABR formula. Even though the formulas are different, that looks very much like Hagan's own illustration (with a better scale)!

I have a draft paper around this and more practical ideas to calibrate SABR:

Thursday, July 03, 2014

Heston or Schobel-Zhu issues with short expiries

It's relatively well known that Heston does not fit the market for short expiries. Given that there are just 5 parameters to fit a full surface, it's almost logical that one part of the surface of it is not going to fit well the market.
I was more surprised to see how bad Heston or Schobel-Zhu were to fit a single short expiry volatility slice. As an example I looked at SP500 options with 1 week expiry. It does not really matter if one forces kappa and rho to constant values (even to 0) the behavior is the same and the error in fit does not change much.

Schobel-Zhu fit for a slice of maturity 1 week
In this graph, the brown, green and red smiles corresponds to Schobel-Zhu fit using an explicit guess (matching skew & curvature ATM), using Levenberg-Marquardt on this guess, and using plain differential evolution.
What happens is that the smiles flattens to quickly in the strike dimension. One consequence is that the implied volatility can not be computed for extreme strikes: the smile being too low, the price becomes extremely small, under machine epsilon and the numerical method (Cos) fails. There is also a bogus angle in the right wing, because of numerical error. I paid attention to ignore too small prices in the calibration by truncating the initial data.
Heston fit, with Lord-Kahl (exact wings)

SABR behaves much better (fixing beta=1 in this case) in comparison (As I use the same truncation as for Schobel-Zhu, the flat left wing part is ignored).
SABR fit for a slice of maturity 1 week
For longer expiries, Heston & Schobel-Zhu, even limited to 3 parameters, actually give a better fit in general than SABR.

Sunday, June 29, 2014

On the Role of Static Types and Generic Types on Productivity

Most developers have strong opinions on dynamic types programming languages vs static types programming languages. The former is often assumed to be good for small projects/prototyping while the later better for bigger projects. But there is a surprisingly small number of studies to back those claims.

One such study is "An experiment about static and dynamic type systems: doubts about the positive impact of static type systems on development time" and came to the conclusion that on a small project, static typing did not decrease programming time, and actually increased debugging time. However 4 years later, "An empirical comparison of static and dynamic type systems on API usage in the presence of an IDE: Java vs. groovy with eclipse" shows that a developer is 2x more productive with Java than with Groovy using an unknown API. This contrasts a bit (but does not contradict) with their previous study "Static Type Systems (Sometimes) have a Positive Impact on the Usability of Undocumented Software: An Empirical Evaluation" that showed Groovy to be more productive on small projects. One problem is that all these studies stem from the same person.

It's more interesting to look at generic types vs raw types use, where even less studies have been done. "Do developers benefit from generic types?: an empirical comparison of generic and raw types in java" concludes that generic types do not provide any advantages to fix typing errors, hardly surprising in my opinion. Generic types (especially with type erasure as in Java) is the typical idea that sounds good but that in practice does not really help: it makes the code actually more awkward to read and tend to make developers too lazy to create new classes that would often be more appropriate than a generic type (think Map>).