3A Product Internship - Trading Macs - Validation Of The Forward Market Model For Interest Rate Derivatives H/F - 75

The Team:

You will become a part of the Front Office Trading Product Development Domain (PDD) which is at heart of MX.3 software evolution, where you will integrate the Financial Engineering team. Our multi-cultural team designs, validates and delivers Murex Advanced Analytics (MACS) which is a combination of rich catalogue of derivative products covering all asset classes, a large set of models for evaluation and risk management of derivatives.

We work closely with the quant development and integration teams to enhance our products and models. We provide our quantitative expertise and collaborate with FO Trading PDD teams like EQD, Non-Linear Rates, FXD, COM, etc. to build trading solutions. Similarly, we assist Client Services and regional offices across the globe to provide cutting edge solutions to our clients.

The Mission:

Under natural market conditions, short-term rates are lower than long-term rates - reflecting the uncertainty premium for the longer duration of borrowing. However, recently, inflation has pushed short-term rates above long-term rates, inverting the yield curve. These market conditions led to the proliferation of CMS spreads and their derivatives, which allow market participants to gain exposure to the slope of the rate curve.

Pricing CMS spread derivatives requires advanced models that can de-correlate the short- and long-term of the rate curve. The 2-factor Forward Market Model (FMM) proposed by Murex offers great flexibility in how forward rates interact, making it ideal for pricing products that are sensitive to curve shape and spread movements.

The goal of the internship is to:

- Validate the 2-factor FMM model when calibrated to CMS spread options.

- Validate the accuracy of the Monte Carlo simulation method applied to the best-seller payoffs.

The ultimate deliverable of your mission is a model validation document summarizing the test results, as well as a list of model gaps and limitations.

The validation activity will be primarily conducted via a web service using Python for executing tests and visualizing results.