2017-04-17

Profit explain explained

I was once asked to explain the P/L explain report. Something must be wrong in my story telling if I have to explain the explanation. After the failure of my story telling career, I decided to move to story writing. This note is an attempt at starting my new career.

The full text is available as PDF:

Profit explain explained

2017-04-06

The Thalesians seminar

I will be speaking at The Thalesians seminar on

SIMM and SA FRTB: double AD


Seminar starts at 6:30pm on Wednesday 19 April 2017 at

City University Club


Champagne will be served before the seminar, around 6:00pm.

Register at Meetup:

Abstract


Algorithmic Differentiation (AD) has been used in engineering and computer science for a long time. The term Algorithmic Differentiation can be explained as ``the art of calculating the differentiation of functions with a computer.
Over the last 5 years, AD has made its road to quantitative finance. The most straight forward use of AD is to compute the sensitivity of PV to market inputs. In the frame of SIMM and SA FRTB computation, those sensitivities are the main input and having an efficient way to produce them is important.
Once the IM/Capital number is computed, there are a lot of potential analysis which are handy, like marginal IM and IM attribution. Those analysis also require some form of differentiation, this time with respect to the positions.

Agenda


SIMM and sensitivity based FRTB: double AD
  • Algorithmic Differentiation and computation of sensitivities
  • First AD: fast inputs for SIMM/FRTB
  • Second AD: sensitivity of the IM/Capital itself w.r.t. sensitivities
  • Second AD applications: attribution and marginal IM/Capital