Ritesh Parbhoo

Quantitative finance graduate and FRM candidate with a strong analytical background in derivative pricing, financial risk analysis and statistical modelling. Experienced in building pricing, credit, and portfolio risk models using Excel, R, Python and SQL. Comfortable working with uncertainty, complex datasets, and translating quantitative results into actionable risk insights.

Financial Risk Modelling (Microsoft Excel)

Built and interpreted credit risk and portfolio risk models using various financial frameworks

• Built and interpreted credit risk and portfolio risk models (Merton, CAPM, Utility Theory) to evaluate default probabilities and optimise portfolios
• Implemented options pricing models, such as binomial tree models and the Black–Scholes model using Monte Carlo simulation to support portfolio hedging
• Developed life contingencies and structured cashflow models and risk evaluation exercises
• Computed market risk measures (Value at Risk, Expected Shortfall) and applied stress testing to assess portfolio resilience under adverse conditions

Statistical Modelling (R statistical programming language)

Constructed and analysed various statistical models for risk-related data

• Constructed and analysed linear and generalized linear models for risk-related data
• Applied copula models to analyse dependence structures between risk variables
• Used empirical Bayes credibility methods for experience-based modelling
• Conducted time series modelling for trend analysis and forecasting

Data Science & Machine Learning (Python, SQL)

Built machine learning models and performed data analysis tasks

• Built machine learning models, specifically for classification (unsupervised learning) and clustering (k-means) problems (keras, scikit-learn)
• Performed data cleaning and visualisation, feature engineering, and model evaluation (seaborn, pandas)

Bachelor of Commerce in Mathematical Sciences – Stellenbosch University

National Senior Certificate – Rondebosch Boys' High School