Market Risk Quant Roadmap
A Practitioner Path from Fundamentals to Advanced Modelling
What is Market Risk?
Risk of losses in on- and off-balance sheet positions arising from adverse movements in market prices and risk factors (Rates, FX, Equities, Commodities, and Credit Spreads).
Market Products & Data
Understand products, markets and data that drive P&L
What you learn
- • Asset Classes: Rates, FX, Equities, Credits, Commodities
- • Product Types: Bonds, Forwards, Swaps, Options
- • Data types: prices, curves, volatilities, correlations
- • Stylized Facts of Financial Time Series
- • Pricing Conventions
Key Concepts
- • Yield curves & term structures
- • Spot, forward, and discounting math
- • Heavy tail, kurtosis
- • (historical) volatility clustering
- • (implied) volatility smile/skew
Risk Factors & Sensitivities
Identify risk factors and measure sensitivities
What you learn
- • Risk factor & market risk drivers
- • Greeks: Delta, Vega, Gamma, Rho
- • Fixed Income: DV01 & Bucketed Durations
- • Risk factor mapping methodologies
Key Concepts
- • Risk factor mapping ((e.g., specific vertices on a yield curve))
- • Finite differences & bumping
- • Linear vs non-linear risk behavior
- • P&L Attribution
- • Sensitivity aggregation logic
VaR & Expected Shortfall
Measure market risk using VaR and ES methodologies
What you learn
- • Historical & Parametric (Delta-Normal) VaR
- • Sensitivity-based VaR (Delta-Gamma-Vega)
- • Monte Carlo VaR methodologies
- • Expected Shortfall (ES) properties
Key Concepts
- • Full Revaluation vs. Greek-based Approximation
- • Taylor series expansion for risk mapping
- • P&L distribution & Volatility estimation
- • Horizon scaling
- • Coherent risk measure
Backtesting & Validation
Verify model accuracy and ensure reliability of risk numbers
What you learn
- • VaR backtesting & exception handling
- • Traffic-light framework (Basel standards)
- • Kupiec Proportion of Failures test
- • Christoffersen Independence test
Key Concepts
- • Predicted vs. Realized P&L Analysis
- • Exception clustering
- • Model risk inventory & limitations
- • Challenger models & benchmarking (eg., different calibration window, EWMV)
Stress Testing & Scenarios
Assess tail risks under extreme but plausible market conditions
What you learn
- • Historical scenarios (2008, COVID-19)
- • Hypothetical scenarios (rate shocks, equity meltdown)
- • Reverse stress testing
Key Concepts
- • Scenario shocks (severe but plausible)
- • Non-linear risk capture in stress
- • Tail risk assessment
FRTB Framework
Implement FRTB requirements and P&L attribution
What you learn
- • FRTB overview & principles
- • Trading Book vs Banking Book
- • Expected Shortfall under FRTB
- • Standardized Approach (SA/SbM)
- • Internal Models Approach (IMA)
Key Concepts
- • Sensitivities-based Method (SbM)
- • P&L Attribution (PLA) testing
- • Non-modellable Risk Factors (NMRF)
- • Liquidity horizons
Advanced Modelling
Model complex dynamics and dependencies
What you learn
- • Simulation & Statistical Modelling
- • Filtered Historical Simulation (FHS)
- • GARCH(1,1) Volatility filtering
- • Dependence & Credit
- • Default Risk Charge (DRC)
- • Migration & Default Modelling
- • Dependence Modelling
- • Multivariate tail-risk aggregation
Key Concepts
- • Conditional Volatility Modelling
- • Volatility Clustering & Mean Reversion
- • Gaussian vs T copula
- • Tail Dependence & Joint Extremes
- • Default Risk
- • Multivariate Aggregation & Tail Risk
Phase Output (Proof of Competence)
Successful implementation validates technical readiness for Level .
Recommended Videos
Core Tech Stack
The Success Formula
From Theory to Practice