Paul Notot

Background

About Paul Notot

2025 Goals : FRM Certified - Expertise in Quantitative Risk Management - Be autonomous on Python for financial risks Currently engaged in an intensive self-directed training program to strengthen expertise in risk management within financial institutions. This program focuses on both regulatory risks and quantitative risk analysis, and Python learning. Core Areas of Focus: Regulatory Risks: Basel FRTB, SREP, Pillar 1 & 2 (including ICAAP and LCAAP) Quantitative Risk Management: Expected Shortfall (ES), Value at Risk (VaR), Expected Credit Loss (ECL), Expected Loss (EL), DV01, Notional Beta-Equivalent Stress Testing & Capital Adequacy: Risk-weighted assets (RWA), capital requirements, and stress testing methodologies Key Reference Materials for Quantitative Risk Management-> Quantitative Risk Management: A Practical Guide to Financial Risk (2012) – Thomas Coleman -> Introductory Econometrics for Finance (2019)- Cambridge University Press -> Risk Management and Financial Institutions (2023) – John Hull -> Quantitative Risk Management: Concepts, Techniques and Tools (2015) – Alexander McNeil -> Financial Risk Manager Handbook, FRM Part I & II (2014) – Philippe Jorion -> Schweser Notes FRM (2024 -> Introductory Econometry for Finance (2019)- Cambridge University Press -> Statistics and Probabilities for Economics (2023)- Paul Newbold Reference Works on Basel Regulatory Frameworks (EU Context)-> Handbook of Basel Capital (2017) – Juan Ramirez -> The New Generation of Risk-Weighted Assets under Basel IV (2019) – Martin Neisen -> CRR The EU Implementation of Basel IV (2023) – Martin Neisen Reference Materials on IFRS Standards and Financial Instruments-> iGAAP 2015: Financial Instruments IFRS 9 – Deloitte -> Accounting for Derivatives: Advanced Hedging under IFRS 9 (2015) – Juan Ramirez -> Options, Futures, and Other Derivatives (2021) – John Hull Specialized Reading on Stress Testing-> Handbook of Financial Stress Testing (2022) – Cambridge University Press Objective: To combine in-depth regulatory knowledge with a strong foundation in quantitative risk modeling on Python to deliver impactful solutions in risk management for financial institutions.