Quasar Chunawala

Summary

I am a software engineer-turned-quantitative analyst. I have 6 years of modeling/implementation experience with skills in advanced C++/Rust. I am highly driven with an ability to quickly learn new tech-stacks and a focus on timely turn-around.

Work Experience

Quantitative Engineer, Goldman Sachs, London, UK

Sep 2024 – Present

PruVal UCS CVA Model Risk AVA. ECB requires us to calculate Prudent valuations for all trading and banking book positions. UCS (Unearned Credit Spreads) is the valuation uncertainty in credit spreads. Extended the macro desk CVA engine to support:

  • Gap Risk (MpOR): Extended the macro desk CVA engine to support Gap Risk (MpOR) by modeling a 10-day gap between collateral posting and closeout dates using AMC regression coefficients.
  • Implemented the impact of Initial Margin (IM), conservatively excluding counterparty-posted IM and modeling GS-posted, credit-risky portions as uncollateralized synthetic forwards.
  • MTA impacts to CVA: Modeled the impact of Minimum Transfer Amounts (MTA) on CVA by forking the residual exposure calculation and using a dense 5-day simulation grid to include actual collateral transfer amounts.
  • Addressed and closed all MRM findings related to a few edge cases in a timely manner to ensure that the model can be submitted to the regulator within deadlines.

Supervised and mentored new team members, fostering their growth within the team.

Quantitative Analyst, Credit Suisse, Wroclaw, Poland

April 2019 – Jun 2024

Developed improvements to the C++/F# global quant library, focusing on bonds, asset swaps/ASW01 calculations, CDS, IndexCDS, CDSwaptions.

Built an orchestrator framework in F#, together with pricer spreadsheets which streamline the viewing of future cashflows, pricing of all legs, risks and performing bookings involved in the issuance of bond repacks for the structured credit desk, improving trading and execution efficiency.

Enhanced C++ bond analytics to use ObservationScheduleDCF for compounding rate calculations for bonds referencing ARR e.g. USD SOFR.

Developed and enhanced the Buy-Sell signal report that analyses historical RFQ volumes and enables corporate bonds desk to take a view if there is more interest on the buy-side or sell-side for various bond buckets.

Education

Bachelors of Engineering (Computer Science)

Vidyavardhini’s College of Engineering, University of Mumbai, Mumbai
2004 – 2008

Relevant coursework: Computer Architecture, Operating Systems, Network programming, Multivariate Calculus.

Skills Matrix

Coding Skills

Proficient: C++ 20. - Implementing vector as a learning exercise, Overload magazine, Feb 2025. - Coroutines - a deep-dive, Overload Magazine, Dec 2025. - An SPSC lock-free queue design, Meeting C++ 2025.

Intermediate: Rust, Python 3.14.

Financial Product Knowledge

Asset Swaps, Risky Bond Valuations, CDS and Index CDS, CDSwaptions, CLNs, Repacks.

Financial Mathematics

Stochastic Calculus, AAD (Adjoint Algorithmic Differentiation), Margrabe’s formula, Girsanov’s theorem and change-of-numeraire, Feynmann-Kac, collateralized discounting.

OS/Tools/Scripting

Linux, Bash.

Projects

Order-book matching engine

[Git Repo] A matching engine that keeps track of buy-side and sell-side in constant time and supports market orders, limit orders, Fill-And-Kill and Fill-Or-Kill orders.

The Vanna-Volga method for FX implied volatility smile

Pythonic implementation of the Vanna-Volga method. Uses three market-quotes to rebuild the whole implied volatility surface, and in particular, for far in-the-money and out-of-the-money strikes.