Designed for Finance Professionals
The Quant Market Risk Management (QMRM) Program is designed to equip finance professionals with in-depth knowledge, hands-on expertise, and quantitative techniques required for managing market risks effectively. This rigorous program bridges the gap between theoretical risk frameworks and practical, real-time risk modeling, mirroring the workflows and modeling standards of institutional risk desks at global investment banks, asset management and consulting firms, and other financial institutions.
What You'll Learn
This foundational module introduces participants to market risk concepts, measurement frameworks, and governance structures essential for managing trading book exposures. Participants master market risk taxonomy spanning directional risk (equity, interest rate, FX, commodity price exposure), basis risk (spread relationships, curve risk, correlation breakdown), volatility risk (vega exposure and vol-of-vol), correlation risk (multi-asset dependencies), and liquidity risk (bid-ask spreads, liquidation horizons). Coverage addresses trading book versus banking book boundary determination, risk factor identification and mapping frameworks expressing portfolio exposures as standardized factor sensitivities, and P&L analysis examining volatility measurement, return distribution properties (fat tails, skewness, kurtosis), and correlation structures. Regulatory framework evolution traces Basel I through FRTB, examining how VaR-based capital emerged and evolved through crisis-driven enhancements. Market risk governance addresses the three lines of defense model, risk appetite frameworks cascading board-level tolerance through limits, and daily risk reporting, providing management visibility—establishing a foundational understanding for advanced quantitative measurement techniques.
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This module develops expertise in quantifying and managing market risk through sensitivity measures (Greeks), guiding hedging strategies, and dynamic risk management. Interest rate sensitivities include duration, DV01, key rate durations, and convexity for fixed-income portfolios, with practical applications in DV01-neutral curve spread trades (steepeners, flatteners). Options Greeks receive detailed treatment: Delta measuring directional exposure, Gamma capturing non-linearity, particularly for ATM options near expiration, Vega measuring volatility sensitivity, Theta quantifying time decay, and higher-order Greeks (Volga, Vanna, Charm). Coverage addresses Greek behavior across moneyness, time to expiration, and volatility regimes. Dynamic hedging implementation spans Delta hedging, maintaining directional neutrality through rebalancing, examining frequency trade-offs between hedge accuracy and transaction costs; Delta-Gamma hedging using two instruments achieving zero Delta and Gamma simultaneously; and Delta-Gamma-Vega hedging requiring three instruments. Options strategy risk profiles examine straddles and strangles, providing pure volatility exposure, and directional spreads (bull/bear spreads) offering defined-risk exposure—developing complete sensitivity-based risk measurement and hedging capabilities for derivatives portfolios.
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This module develops expertise in scenario-based risk measurement, examining portfolio behavior under specific market conditions, historical crises, and hypothetical extreme events, complementing VaR. Participants master scenario types: sensitivity scenarios (isolated equity -20%, rates +100bp shocks), historical scenarios replicating crises (2008 Lehman, 2020 COVID, 2022 inflation shock), hypothetical scenarios (geopolitical conflicts, cyber attacks, climate events), and reverse stress testing identifying catastrophic loss scenarios. Equity scenarios combine spot price shocks with volatility changes, capturing crisis dynamics. Interest rate scenarios span parallel shifts, non-parallel shifts (steepening, flattening, butterfly), and PCA-based systematic curve deformations identifying level, slope, and curvature factors. FX scenarios address currency shocks and FX options, combining spot and volatility moves. Regulatory stress testing covers CCAR/DFAST scenarios and FRTB requirements. Scenario revaluation contrasts full revaluation, recalculating values from first principles (Black-Scholes for options, DCF for swaps) against partial revaluation using Greeks approximations—participants develop production scenario engines with comprehensive risk reporting showing P&L impacts, worst-case scenarios, and limit breaches.
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This module establishes mastery of Value-at-Risk as an industry-standard quantitative risk measure for regulatory capital, internal limits, and performance evaluation. Participants develop expertise in all three VaR methodologies: Historical Simulation, applying historical returns to current positions using non-parametric distributions, Parametric approaches employing covariance matrices with analytical calculation assuming normality, and Monte Carlo simulation for complex derivatives using stochastic process modeling. Coverage spans equities with VaR decomposition into Total Market Risk, General Market Risk, and Specific Risk; fixed-income through both full revaluation DCF and partial revaluation sensitivity-based models preserving accuracy-efficiency trade-offs; and derivatives, including equity options, futures, interest rate swaps, and cross-currency swaps, using full Black-Scholes/DCF revaluation and Greeks-based approximations. Advanced techniques include variance matching for bond portfolios employing quadratic optimization, mapping complex cashflows to standard tenors, PCA decomposing yield curves into principal components, and VaR decomposition using Conditional VaR, Incremental VaR, and Marginal VaR for capital allocation—developing production-ready VaR systems across multi-asset portfolios meeting regulatory requirements.
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This advanced module addresses sophisticated risk measures capturing tail risk, stress conditions, and incremental contributions. PCA for yield curve VaR implements eigenvalue decomposition, identifying level, slope, and curvature factors, enabling factor-based risk measurement and hedging. Risk decomposition measures include Conditional VaR, allocating portfolio VaR to positions for capital allocation, Incremental VaR, measuring VaR change from trading decisions, and Marginal VaR for portfolio optimization. Expected Shortfall (ES) addresses VaR's tail risk limitation as a coherent risk measure (subadditive, incentivizes diversification), with implementation via Historical Simulation, Parametric, and Monte Carlo calculating tail averages—ES forms FRTB's core risk measure at 97.5% confidence, replacing VaR. Stressed period selection identifies 12-month crisis windows exhibiting portfolio-relevant stress using quantitative scoring across candidate periods. Stressed VaR implementation applies stressed period returns to current portfolios, examining volatility scaling, correlation structure during stress, and multi-asset amplification—coverage integrates regulatory requirements under Basel III, requiring capital equal to the maximum of standard VaR and Stressed VaR, alongside ES implementation for FRTB Internal Models Approach.
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This regulatory module addresses FRTB, Basel's fundamental redesign of market risk capital, replacing Basel II.5 with more risk-sensitive methodologies and stricter standards. Coverage examines FRTB motivation from the 2008 crisis failures, key changes replacing VaR with Expected Shortfall at 97.5%, desk-level IMA application, and enhanced validation. Trading book boundary determination addresses presumptive lists, Internal Risk Transfers, and switching restrictions. FRTB Standardized Approach implements Sensitivities-Based Method (SBM), calculating delta, vega, and curvature risk across five risk classes (interest rate, FX, equity, commodity, credit spread) using prescribed risk weights, correlations, and bucket structures, alongside Default Risk Charge and Residual Risk Add-On. Internal Models Approach covers Expected Shortfall replacing VaR, liquidity horizons scaling capital for risk factor-specific holding periods, stressed calibration, rigorous P&L Attribution Testing ensuring models explain trading P&L, backtesting with traffic light approaches, and Non-Modellable Risk Factor treatment. Implementation challenges address data requirements, system builds, model development, validation standards, desk approval processes, and capital impact analysis—preparing participants for FRTB compliance roles and supervisory engagement.
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This module addresses the complete model lifecycle from development through validation, backtesting, and ongoing monitoring. Model development covers requirements gathering, data quality assessment, methodology selection, parameter calibration using maximum likelihood estimation, production code implementation, and comprehensive documentation. VaR model development spans Historical Simulation data selection and scenario generation, Parametric covariance matrix estimation, Monte Carlo stochastic process selection, and Expected Shortfall tail risk estimation. Independent model validation frameworks implement SR 11-7 guidance, assessing conceptual soundness (methodology appropriateness, assumption validity), implementation verification (code review, calculation replication), and ongoing monitoring. VaR backtesting employs Kupiec exception frequency testing, Christoffersen independence testing for clustering, and the Basel traffic light approach with green/yellow/red zones—participants conduct root cause analysis and develop remediation plans. P&L attribution decomposes trading P&L into risk factor contributions, compares hypothetical P&L to risk-theoretical P&L, analyzes unexplained P&L indicating model gaps, and implements FRTB P&L Attribution Tests. Coverage addresses Expected Shortfall backtesting challenges, model performance monitoring through KPIs, recalibration frameworks, and change management—developing complete model governance capabilities.
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Subscription
Pricing Plan
20,000 INR
100% Refund (No Questions Asked) within 2 hours of subscription.