The Quantitative solution team covers topics related to market risk, counterparty credit risk, credit risk, and climate risk but also machine learning, AI, and big data models.
We design and implement sound methodologies in line with market practice and compliant with the standard requirements leveraging on our credentials from large investment banks, asset managers, insurance and corporate audit and consulting mandates.
We design, validate and implement complex models in cutting edge programming languages building user friendly interfaces. Services include:
Accounting models
Development of in-house Python pricing libraries for derivatives and mortgage portfolios sold to FinTechs and Funds with automated valuation reports
Independent price verification of Level 3 products for audit
Development/validation of market risk, credit risk, liquidity risk, counterparty credit risk, prudential valuation models for large investment banks on very large projects such as FRTB and on behalf of ECB
Capital optimisation through knowledge of industry best practice.
Experts in litigation cases requiring a quantitative expert
Partnering with our forensic team to deliver highest quality expert reports in the aforementioned domain of expertise. We have credentials in the space of liquidity risk for the FCA and derivative valuation litigation for HMRC.
AI, machine learning algorithms, blockchain
Using our programming skills in Python and R combined with our industry expertise products and models to develop new offers to service our clients.
Case study 1: Implementation of IFRS9 model
Extensive data cleaning and analysis of the segmentation of the Retail portfolio
Review of the internal rating methodology
Modelling of the staging rules and definition of the significant increase of credit risk
Analysis of the compliance of modelling choices with respect to international IFRS9 requirements and to local IFRS9 guidelines (of Tunisian Central Bank)
Calibration of ECL parameters (Exposure At Default, Probability of default) with relevant market data and client portfolio data
Global modelling and calculation of ECL, and assessment of the consistency of the results by segment, stage and rating.
Case study 2: FRTB-IMA implementation for a large UK bank
Mazars partnered with a large UK bank to deliver the design and implementation of their market risk internal models (IMA) in line with FRTB (involving over 10 quant consultants). All aspect of FRTB has been covered such as modelling expected shortfall and stressed expected shortfall for modellable and non-modellable risk factors (NMRF), backtesting, determination of Stressed Period. The key points of the work were:
Discussion of the regulatory texts and formulas as well as market data design and prototyping
Definition of proxying methodologies
Design and implementation stress period selection (including NMRF)
Implementation of PLATS and other relevant statistical tests
Design and implementation of expected shortfall and stressed expected shortfall models
Writing model documentation and validate it through a deep analysis of the consistency of the results
Multiple interactions with various stakeholders in the Risk department.
Case study 3: Liquidity model design for UCITS equity fund for FCA
Mazars acts as liquidity expert for the FCA in the context of an investigation of a suspended equity UCITS fund. Mazars has developed a comprehensive and flexible liquidity solution in Python that helps the client to proactively manage liquidity risks, meet regulatory requirements (such as COLL rules and IOSCO recommendations), and align with industry best practices. The solution:
Leverages automated data feeds to seamlessly retrieve funds' composition and securities volume data
Generates liquidity metrics and stressed liquidity metrics
Benchmarks fund's liquidity metrics against a carefully curated universe of comparable funds leveraging on funds publishing their monthly composition in EIKON
Summarises in a dashboard KPI, Unstressed Liquidity Metrics, Stressed Liquidity Metrics, and benchmarking against peer funds.
Case study 4: Valuation of convertible loan notes for clients
The project involved developing a convertible bond pricer model in Python using the Cox-Rubinstein-Ross stock tree methodology. This model serves as the foundation for valuing convertible bonds. Building upon this, the goal is to extend the pricer's capabilities by integrating extra functionalities:
Inclusion of an optional exercise barrier. This feature introduces a threshold that must be reached for the conversion to take place, adding complexity and customisation to the model
Implementing interpolation techniques for the term structure of interest rates. This refinement allows for a more accurate representation of interest rate dynamics over time
Built a handy API for client to enhance user experience on pricing convertible bond that helped to visualise prices based on different model inputs and get report for audit purposes
Results benchmark of the application and model validation by comparing valuations to those obtained using industry-grade valuation software such as Numerix.
Case study 5: Development of a climate risk solution
Review of different climate risk assessment methodologies
Establish a comprehensive framework for climate risk analysis
Gather financial portfolio data from clients and sectoral data from external sources
Collection of regulated climate scenarios projections data for macroeconomic and climate variables (transition and physical risk)
Calibration of the volatility to climate risks using expert judgement
Global modelling and calculation of CR and ECL
Implementation of the model in an API cloud-based tool for visualising metrics by corporate, segment, location and scenario.
After a really slow year in 2022, convertible issuance is growing again and this trend is expected to accelerate in the next two years as a lot of companies have to refinance in optimal circumstances for convertible issuance.
Subsequent to a recent review across asset management firms, the Financial Conduct Authority (FCA) has observed varying degrees of liquidity risk management standards across firms and has issued a warning to Authorised Fund Managers (AFMs) to better risk-manage their liquidity to avoid investor harm.
We have developed a robust Python-based solution as a response to the increasing interest in the market for liquidity risk management. Our platform allows the user to effectively monitor and optimise their portfolio's liquidity, providing them with valuable insights, and enabling them to make informed decisions and seize opportunities with confidence.
