Designed for Finance Professionals
The Equity Investments and Risk Management (EIRM) Program is an advanced training program designed to equip professionals with in-depth knowledge, hands-on expertise, and quantitative techniques essential for managing equity investments and risks. This program bridges the gap between theoretical frameworks and practical risk modeling and market data management. Each module is carefully curated to build a deep, layered understanding, from equity market fundamentals and product knowledge to advanced volatility modeling, risk decomposition techniques, and systematic risk analytics.
What You'll Learn
This foundational module establishes the market data infrastructure required for quantitative equity analysis. Participants learn to source, process and manage real-time and historical equity price data, and the data quality criteria needed before that data can be used in risk models. Through hands-on exercises with historical time-series datasets, they analyze major equity market shocks, building intuition for how equity prices behave under stress. The module then introduces the decomposition of equity risk into systematic and unsystematic components, laying the groundwork for portfolio risk measurement. It closes with building a professional, automated market data management system for equities, establishing the data foundation for all subsequent modules.
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This module develops practical expertise in volatility modeling, the core input to equity risk and derivatives pricing. Participants implement the Simple Moving Average (SMA) and Exponentially Weighted Moving Average (EWMA) volatility models under the RiskMetrics framework, learning how each weights historical returns differently in estimating current volatility, before calibrating the EWMA decay factor and validating it against realized volatility. The module then extends to GARCH models, capturing volatility clustering and mean reversion beyond what EWMA can represent. It closes with implied volatility: the skew observed across strikes, the volatility surface across strikes and maturities, and how these are read and used in practice for equity derivatives pricing and risk. Hands-on application has participants implement and calibrate the volatility models on real market data and build an implied volatility surface.
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This module develops the frameworks used to evaluate equity portfolio performance and decompose it into its underlying drivers. Participants begin with return measurement, covering time-weighted versus money-weighted (dollar-weighted) returns and why each is used differently by asset managers and investors. The module then covers risk-adjusted performance measures, including the Sharpe ratio, Treynor ratio, Jensen's alpha, information ratio, and Sortino ratio, and the conditions under which each is appropriate. Participants implement the Capital Asset Pricing Model (CAPM) to estimate expected returns and benchmark-relative alpha and beta, before moving to multi-factor models (Fama-French three-factor and extensions) for a more granular view of return drivers. The core of the module is performance attribution: Brinson-style attribution decomposing active return into allocation, selection and interaction effects, applied at both sector and security level against a benchmark. The module closes with tracking error analysis and active risk budgeting, connecting attribution results back to portfolio construction decisions.
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This module establishes production-level expertise in Value-at-Risk for equity portfolios, covering regulatory capital, internal risk control, and performance evaluation applications. Participants implement Historical Simulation VaR end to end — risk factor identification, scenario generation, scenario revaluation, and building the P&L distribution across long and short positions — before extending to portfolio-level analysis, where correlation and covariance structures drive diversification benefits and the diversification ratio. The module introduces VaR decomposition into Total Market Risk, General Market Risk and Equity Specific Risk, and uses this decomposition for portfolio optimization, hedging market risk, and diversifying away specific risk. Participants then implement Parametric VaR under both the Normal and EWMA volatility assumptions, covering parameter estimation, the estimation-window trade-off between stability and responsiveness, and the Z-score-based analytical VaR formula. Throughout, the three core VaR methodologies — Parametric, Historical Simulation and Monte Carlo Simulation — are compared on a shared diversified equity portfolio, with each model validated against realized outcomes.
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This module establishes dual competency in analytical and numerical pricing of equity derivatives, the standard toolkit for trading and risk management. Participants begin with forwards and futures on single stocks and indices, covering cost-of-carry pricing and dividend adjustments. The module then develops the Black-Scholes-Merton framework for European options, including closed-form pricing and the full set of Greeks (Delta, Gamma, Vega, Theta, Rho), before extending to binomial and trinomial trees for American-style exercise and early-exercise premium. Participants implement Monte Carlo simulation for path-dependent and exotic payoffs, and cover the practical adjustments required for dividends, discrete dividend dates, and borrow costs. The module addresses implied volatility in pricing — using the skew and surface built in Module 302 to price options consistently across strikes and maturities — and covers variance swaps and volatility derivatives as a direct way to trade realized versus implied volatility. It closes with sensitivity-based (Greeks) hedging and P&L attribution for an equity derivatives book, bridging pricing and risk management.
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Pricing Plan
12,000 INR
100% Refund (No Questions Asked) within 2 hours of subscription.