Monday, July 1, 2013

OIS Discounting in Asia

Check out the latest edition of Risk Asia. I have been quoted in this excellent article of the current state of OIS discounting at Asian and Australian banks. The key message is that OIS discounting is a fundamental change in the way banks price and view the risk associated with collateralised trades. It is a long and quite complex process and there are PnL opportunities for the early adopters as they can make choices that optimise the the type of collateral they posted. Those banks who wait will become price takers and miss the opportunities OIS discounting presents right now.

Tuesday, April 2, 2013

Random Walkers - Quants in a Pub - Thu 4th April

Singapore's only Quant Finance Social event will convene again this month on Thursday 4th April.

Join quants from banks, funds and academia to talk about all things quant or not and where the highest levels of liquidity can be found in a pint glass.

Date: Thursday 4th April 2013

Time: 6:30pm onwards

Place: The Bull and Bear, 33 Pekin Street, Singapore 048671
+65 6557 0879

The event takes place on the first Thursday of every month at the Bull and Bear.

Thursday, March 28, 2013

FINCAD's F3 v4.0 is Coming Soon

Last week the Maroon office got a sneak peak of the latest version of F3 from FINCAD.

F3 is already well known as the leading financial analytics platform, having recently added the "Risk Management Technology Product of the Year" award from Risk to the "Most Innovative Specialist Vendor" award they picked up in 2012.

The release of F3 - version 4.0 - takes F3 to a new level, with much greater support for market data, the ability to offload calculations from an Excel session to a server and pre-built market data models for added convenience.

Exciting new features include:

  • Market Data Gateway so you can easily integrate customer market data
  • Calculation Server lets you offload calculations too large or time-consuming for Microsoft Excel, but still retrieve them seamlessly in your spreadsheet
  • Built in market models to let you quickly and easily value vanilla rates instruments in the USD, GBP, EUR and other markets
  • GAP risk for CVA to assist you in complying with Basel III and IFRS requirements
  • Wrong way risk so you can obtain a greater understanding of your counterparty credit risk

For more information, check out the website.

Monday, February 18, 2013

Change of Location!!! Random Walkers Roundtable

Due to some last minute issues we have had to change the location this evening

We will now be on the The University of Chicago campus (where coincidentally the Master of Science in Financial Mathematics Program is taught) which can be found at

146 Robinson Road, #10-01
Singapore 068909
Tel: +65 6225 1431

See you there later from about 6.30 this evening.

Monday, February 4, 2013

Random Walkers Singapore - February 2013

Singapore's only Quant Finance Social event before Chinese New Year!!

Thursday 7th February 2013

See you all in the Bull and Bear

33 Pekin Street, Singapore 048671
+65 6557 0879

Thursday, January 24, 2013

Random Walkers Roundtable - Next Monday 18 February

We are delighted to welcome Professor Uwe Naumann from the University of Aachen to Singapore. Professor Naumann has very kindly offered to share his knowledge with us at a roundtable event that will be hosted after work on the 18th February.

Professor Naumann will deliver a presentation and host a Q&A on Adjoint Parameter Estimation in Computational Finance.

“How sensitive are the values of the outputs of my computer program with respect to changes in the values of the inputs? How sensitive are these first-order sensitivities with respect to changes in the values of the inputs? How sensitive are the second-order sensitivities with respect to changes in the values of the inputs? . . .”

Computational scientists, engineers, and economists as well as quantitative analysts in computational finance tend to ask these questions on a regular basis. They write computer programs in order to simulate diverse real-world phenomena. The underlying mathematical models often depend on a possibly large number of (typically unknown or uncertain) parameters. Values for the corresponding inputs of the numerical simulation programs can, for example, be the result of (typically error-prone) observations and measurements. If very small perturbations in these uncertain values yield large changes in the values of the outputs, then the feasibility of the entire simulation becomes questionable. Nobody should make decisions based on such highly uncertain data.

Quantitative information about the extent of this uncertainty is crucial. First- and higher-order I sensitivities of outputs of numerical simulation programs with respect to their inputs (also first and higher derivatives) form the basis for various approximations of uncertainty. They are also crucial ingredients of a large number of numerical algorithms ranging from the solution of (systems of) nonlinear equations to optimization under constraints given as (systems of) partial differential equations. This talk describes a set of techniques for modifying the semantics of numerical simulation programs such that the desired first and higher derivatives can be computed accurately and efficiently. Computer programs implement algorithms. Consequently, the subject is known as Algorithmic (also Automatic) Differentiation (AD).

The calibration of unknown or uncertain parameters in financial models is a prime application for adjoint AD. For example, discrepancies between simulated and observed payoffs can be minimzed by optimizing corresponding least-squares objectives with respect to the N free parameters using first- or second-order numerical methods. Approximation of the required gradients / Hessians by finite difference quotients yields a computational cost that is linear / quadratic in N. Adjoint AD allows for gradients to be computed with a computational cost that is independent of N (typically at a constant factor of the cost of the underlying simulation that ranges between 4 and 20). Hessians can be obtained at linear (in N) cost. Suppose that a single payoff simulation as a function of 40 uncertain free parameters takes 5 seconds. A single sequential gradient approximation by finite differences would take at least 205 seconds. The speedup obtained by using adjoint AD would range between 2 and 10. The savings become even more substantial if large parameter spaces are considered. Last but not least, the numerical values obtained are accurate up to machine precision (no truncation).

Professor Naumann also works with the Numerical Algorithms Group (NAG) who specialise in delivering trusted, high quality numerical computing software and high performance computing services.

7 City, the home of the CQF have offered to host the event at their offices on Robinson Road. Click here for a map.