Designed for Finance Professionals
The Quant Market Risk Management (QMRM) Program is an advanced training program designed to equip professionals with in-depth knowledge, hands-on expertise, and quantitative techniques essential for managing market risks effectively. This program bridges the gap between theoretical risk frameworks and practical risk modeling.​ Each module is carefully curated to build a deep, layered understanding, from core financial concepts to advanced risk measurement metrics and management tactics, ensuring that you develop both the analytical precision and practical expertise required in financial markets.
What You'll Learn
Equities are part of many investment portfolios, and understanding equity risk is fundamental for any risk professional. Module 1 introduces you to equity market data and risk modeling techniques, covering both broad market (systematic) and asset-specific (unsystematic) risk factors. You will learn to calculate different types of returns – absolute, discrete, and continuous – and explore how stock prices behave over time. Key statistical concepts are covered, including the basics of standard deviation for volatility, and covariance and correlation to quantify diversification benefits. The distinction between systematic vs. unsystematic risk is emphasized, highlighting that specific risks can be mitigated by diversification while market-wide risks require broader strategies.
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Hands-On Application: Develop Python-based models to compute equity risk metrics and visualize return distributions. You will recreate scenarios of historical market shocks, applying your knowledge to see how extreme equity moves (like market crashes or sudden rallies) impact risk measures.
Interest rates influence virtually every part of the financial markets – from bond prices and borrowing costs to equity valuations. In Module 2, you will focus on interest rate data and yield curve analysis to understand how shifts in interest rates translate into market risk. The module covers the construction of yield curves and explains different curve shapes – normal, inverted, and humped – along with their economic interpretations. You’ll examine historical interest rate time series and learn to apply interest rate shocks to assess sensitivity in bond portfolios. A special emphasis is placed on yield spreads as key risk indicators: for example, the U.S. 10-year vs 3-month Treasury yield spread, which has historically been a closely watched predictor of recessions.
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Hands-On Application: Build an automated market report with Python that continuously monitors yield spreads and equity market performance. By the end of this module, you will have a functional dashboard that fetches yield curve data and S&P 500 index levels, then generates visualizations and summary reports. This practical project cements your ability to create dynamic, data-driven risk reports for real-world market monitoring.
This module expands your expertise to a multi-asset context and focuses on efficient data management and automation techniques. Modern risk management demands handling high-quality market data from diverse sources and reacting in real time. Module 3 covers data for currencies (FX), various derivatives (forwards, futures, options), and even cryptocurrencies, emphasizing how to integrate and manage these data streams. You will deepen your understanding of financial derivatives through concise guides on forward vs. futures contracts and options (calls, puts, and the concept of moneyness), linking these instruments to their market data characteristics. Crucially, this module is about automation: implementing Python-based pipelines to streamline data collection, cleaning, and analysis across all these asset classes.
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Hands-On Application: You will implement a Python-based market data automation project. This capstone exercise ties together all asset classes: for example, you might create a consolidated dashboard that simultaneously visualizes stock index trends, yield curve movements, FX rate changes, and derivative market indicators. The result is a dynamic market monitoring tool that reflects best practices in data automation and real-time investment and risk management.
A strong foundation in statistical methods and probability distributions is critical for market risk modeling. This module introduces statistical measures, correlation analysis, and probability distributions that underpin risk quantification in financial markets.​
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Compute statistical measures using real market data, analyze historical returns, and assess correlations between different asset classes.
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Simulate asset returns and prices using normal and log-normal distributions, respectively, evaluate risk measures, and apply these concepts to real-world financial scenarios.
Understanding the term structure of interest rates is crucial for pricing fixed-income securities, managing interest rate risk, and constructing yield curves for scenario creation. This module introduces interpolation techniques used to construct smooth and continuous yield curves, regression models for yield curve estimation (including linear and polynomial regressions), and advanced factor-based models such as the Nelson-Seigel and Nelson-Seigel-Svensson models needed for accurate interest rate modeling and forecasting.
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Hands-On Application: Implement yield curve modeling techniques, calibrate model parameters using real-world market data, and validate their predictive accuracy.
Interest rates are the primary driver of financial portfolios, influencing bond pricing, derivatives valuation, and risk management strategies. This module introduces short-rate models and principal component analysis (PCA), key techniques for modeling interest rate dynamics (level, slope, and curvature) and understanding market risk factors.
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Implement and calibrate Vasicek and CIR models using historical interest rate data, simulate interest rate paths, and compare model accuracy in forecasting yield curve dynamics.
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Perform PCA on yield curve data, analyze historical interest rate movements, and use PCA-based shock modeling to simulate interest rate stress scenarios.
Volatility is a crucial risk metric in financial markets, affecting asset pricing, portfolio risk assessment, and derivative valuation. This module explores historical volatility modeling, including moving averages, advanced time-series techniques, including exponential weighted methods, autoregressive models, and the construction of volatility surfaces for equity options.
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Market Participants often observe non-uniform volatility levels across strike prices and maturities (known as volatility skew and surface formation). This module also covers volatility smile effects, implied volatility modeling, and trading strategies based on volatility skew.
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Implement EWMA and GARCH models to estimate volatility, analyze historical market shocks, and compare forecasting accuracy for risk management and pricing derivatives.
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Construct volatility skew and surfaces using options market data, analyze implied volatility skews, and develop trading strategies based on volatility spreads.
Stochastic processes are essential for modeling asset price dynamics, risk management, and pricing derivatives. This module introduces non-parametric (historical simulation technique, widely used in Value-at-Risk calculations and portfolio stress testing) and parametric (Monte-Carlo Simulation, widely used for pricing derivatives and estimating risk) simulation methods, equipping learners with the ability to forecast risk exposures and assess potential market outcomes.
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Implement historical simulation techniques to model equity market fluctuations, asses portfolio risk under different stress conditions, and compare simulated outcomes to actual market movements.
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Develop Monte-Carlo simulations to model stock price and interest rate movements, simulate fixed-income portfolio risk, and compare the performance of historical vs. parametric simulations in market risk analysis.
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Use stochastic models and Monte Carlo methods to price options, swaps, and other structured financial products. Simulate portfolio performance under varying market conditions to identify vulnerabilities and develop mitigation strategies.
Fixed-income securities are the most traded and concentrated in the financial markets. This module provides a comprehensive framework for pricing and valuing bonds and interest rate swaps, covering discounting cash flow (DCF) modeling, interest rate sensitivities, and scenario analysis techniques.
Interest rate swaps and options (swaptions) are crucial in hedging risk, managing yield curve exposures, and structuring fixed-income portfolios. This module also covers swap pricing models and option-based valuation approaches.
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Valuation Report of US Treasury Securities and Mark-to-Market: Construct a comprehensive valuation report, summarizing bond pricing methodologies and market risk assessments, conduct interest rate risk assessments, and prepare mark-to-market valuation reports using real market data.
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Implement pricing models for interest rate swaps and swaptions, calibrate Black’s model for swaption pricing, and develop risk reports for swap exposures.
Derivatives play a critical role in financial markets, enabling hedging, speculation, and risk transfer. This module provides a comprehensive framework for pricing and valuing derivative instruments, covering equity futures, options, interest rate derivatives, and exotic instruments.
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​For complex derivative products, analytical solutions are not always feasible. This module also covers Monte Carlo simulation techniques, essential for pricing options with path dependencies and stochastic behaviours. Exotic derivatives, including interest rate swaps, cross-currency swaps, and swaptions, require specialized valuation techniques. This module also covers pricing methodologies for complex financial instruments.
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​Derivative pricing models require careful calibration and validation to ensure robustness in changing market conditions. This module also covers model development, implementation, and backtesting techniques.
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Implement binomial pricing models, Black-Scholes option valuations, and risk-neutral hedging strategies. Develop Monte Carlo pricing models, simulate exotic option payoffs, and analyze path-dependent risk exposures. Perform sensitivity analysis, backtest option pricing models, and validate results using real market data.
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Price interest rate swaps and swaptions, conduct scenario analysis, and implement risk factor attribution for structured products.
Risk sensitivity analysis is a cornerstone of market risk management, allowing traders and risk managers to quantify portfolio risk exposure, optimize hedging strategies (as option positions are influenced by multiple risk factors, including price movements, volatility shifts, and time decay), and mitigate financial risks. This module focuses on fixed-income risk sensitivities (Duration, DV01, and Convexity), option greeks (Delta, Gamma, Vega, Theta, Rho, Vanna, and Volga), and advanced hedging techniques for equity, interest rate, and derivatives.
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Hedging strategies allow traders and risk managers to neutralize market exposures while optimizing capital efficiency.​ ​Understanding risk profiles across different trading strategies is essential for managing exposure effectively. This module also covers practical approaches to risk mitigation using Delta, Gamma, and Vega hedging techniques, and the risk-return characteristics of complex option structures.
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Compute duration, DV01, and convexity for bond portfolios, implement yield curve strategies, and construct hedged fixed-income positions. Compute option Greeks, visualize Delta-Gamma relationships, and analyze how volatility affects Vega and Vanna risk.
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Construct Delta-neutral portfolios, rebalance Gamma hedging strategies, and evaluate the impact of volatility shifts on Vega risk.
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Model profit/loss distributions, assess risk profile shifts across different market regimes, and optimize hedging techniques for structured option positions.
Scenario analysis and stress testing are essential risk management techniques that evaluate a portfolio's resilience under extreme market conditions. This module focuses on designing market stress scenarios, asset prices, interest rate, exchange rate shifts, regulatory stress test methodologies to asses the impact of adverse conditions on equity and fixed-income portfolios.
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This module expands on scenario generation techniques and introduces advanced methodologies for stress-testing equity and fixed-income portfolios.
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Build stress-testing models for equities and fixed income, simulate parallel and non-parallel yield curve shifts, and evaluate portfolio resilience using regulatory stress test cases.
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Design custom market stress scenarios, implement PCA-based interest rate shocks, and generate scenario-driven PnL reports for risk assessment.
Value-at-Risk (VaR) is a fundamental risk measure used by financial institutions to quantify potential losses under adverse market conditions. This module provides a comprehensive exploration of VaR, stress VaR, Expected Shortfall (ES), and risk model validation techniques. Participants will gain hands-on experience in historical simulation, parametric VaR, Monte Carlo simulations, and PCA-based risk estimation for equities, bonds, futures, options, and swaps.
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Expected Shortfall (ES) provides a more accurate measure of tail risk, capturing average losses beyond VaR estimates. Stressed VaR measures risk under extreme historical market conditions, helping financial institutions prepare for black swan events and crisis scenarios.
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Model validation is crucial for ensuring accuracy, robustness, and compliance in risk management frameworks. This module also covers backtesting, stress testing, and other model validation techniques.
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Market Value-at-Risk Report for Fixed-Income Securities and Portfolio: Generate a structured risk report summarizing VaR-based risk assessments for bond portfolios.
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Compute VaR across asset classes, calibrate PCA models for fixed-income risk, and analyze incremental and marginal VaR for portfolio optimization. Compute CVaR for multi-asset portfolios, compare VaR vs. Expected Shortfall performance, and analyze tail risk distributions.
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Develop stressed VaR models, analyze historical crisis periods, and simulate black swan events for portfolio risk assessments.
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Market Risk Validation Report: Backtesting and Stress Testing Risk Methodologies – Investment Portfolio: Generate a comprehensive model validation report, summarizing key risk methodologies, backtesting results, and stress testing outcomes.
Subscription
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Professional Plan
20,000 INR
100% Refund (No Questions Asked) within 2 hours of subscription.