Designed for Finance Professionals
The Fixed-Income Investments and Risk Management (FIIRM) Program is designed to equip finance professionals with the quantitative expertise, fixed-income analytical frameworks, and hands-on implementation required for institutional fixed-income trading, portfolio management, and risk analytics. This rigorous program bridges theoretical foundations with practical applications, mirroring the workflows and modeling standards of fixed-income desks
at global investment banks, asset management and consulting firms, and other financial institutions.
What You'll Learn
This foundational module establishes essential market data infrastructure for quantitative fixed-income analysis. Participants learn to source, process, and manage both real-time and historical interest rate data across sovereign and corporate rates/curves, including Treasuries and benchmark rates used in institutional systems. Through hands-on exercises with historical time-series datasets, learners analyze major rate shock events (2008 crisis, 2013 Taper Tantrum, 2020 COVID cuts), building intuition for rate dynamics critical for risk modeling. The module covers yield spread analysis, including Z-spreads, OAS, and term spreads (2s10s, 3m10y) as economic indicators, culminating in building professional market data management systems with automated data pipelines, curve bootstrapping, and multi-currency structures—establishing the foundation for all subsequent fixed-income modeling and risk management modules.
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This module develops expertise in curve construction and modeling using classical interpolation techniques and sophisticated parametric models employed by central banks and investment banks globally. Participants master interpolation methodologies from linear methods to polynomial using advanced techniques, including Vandermonde matrix inversion, Newton divided difference, Lagrange polynomials, and cubic spline interpolation, implementing each in Python and Excel, comparing properties for various applications. The curriculum advances to regression-based modeling and culminates in the industry-standard Nelson-Siegel (NS) and Nelson-Siegel-Svensson (NSS) frameworks used by the Federal Reserve and ECB. Participants learn economic interpretation of level, slope, and curvature factors, implement non-linear optimization for parameter estimation, and conduct rigorous model validation, including out-of-sample performance and stability testing—developing model governance capabilities essential for quantitative research functions.
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This module introduces stochastic modeling frameworks for interest rate dynamics, providing mathematical foundations for derivatives pricing, risk-neutral valuation, and term structure evolution under uncertainty. Participants develop expertise implementing and calibrating short-rate models including the Vasicek model for analytical tractability with mean reversion, the Cox-Ingersoll-Ross (CIR) model ensuring non-negative rates through square-root diffusion, and extended frameworks including Hull-White for perfect term structure fitting, Black-Derman-Toy and Black-Karasinski for lognormal distributions, and multi-factor models capturing independent level and slope dynamics. Through hands-on calibration exercises using market data, participants master parameter estimation, stability assessment, and practical challenges in matching term structures and implied volatility surfaces—developing judgment to select appropriate models for applications ranging from vanilla swap pricing to exotic derivatives valuation and XVA calculations.
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This module establishes mastery of fixed-income valuation from fundamental DCF frameworks to sophisticated sensitivity-based approximation models used in institutional trading and risk management. Participants develop dual capabilities in full revaluation techniques for precise pricing and partial revaluation approaches for efficient large-scale portfolio analytics. Coverage spans US Treasury Bills and coupon-bearing Notes/Bonds with comprehensive P&L attribution, Delta (DV01) and convexity sensitivity analysis, first-order and second-order Taylor expansion approximations with validation against full revaluation benchmarks, and IFRS 9/US GAAP fair value accounting including HFT/HTM classifications and FVTPL/FVOCI treatment. The module extends to fixed-income derivatives including Interest Rate Swaps, Cross-Currency Swaps, and Swaptions, implementing multi-curve discounting frameworks and Greeks-based approximation models—bridging cash instruments and derivatives valuation within unified analytical frameworks used in modern multi-asset risk systems.
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This advanced module addresses transforming complex bond cashflow structures into standardized risk factor exposures for portfolio-level VaR, stress testing, and risk aggregation at institutional scale. Participants master sophisticated mapping methodologies from foundational nearest tenor matching and linear interpolation through duration matching using linear programming frameworks that preserve dollar duration (DV01) under parallel shifts, to variance matching employing non-linear quadratic optimization incorporating correlation structure across the yield curve. Participants implement the Generalized Reduced Gradient (GRG) algorithm using Python's scipy.optimize and Excel Solver, validate solutions against full revaluation benchmarks, and assess computational trade-offs. The module culminates in comprehensive bond portfolio risk frameworks calculating diversified portfolio DV01 with correlation structure, generating portfolio-level VaR measures and key rate duration profiles—mirroring production risk systems at global investment banks for regulatory capital calculations and internal risk limits.
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Subscription
Pricing Plan
15,000 INR
100% Refund (No Questions Asked) within 2 hours of subscription.