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 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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Develops rigorous expertise in yield curve construction using classical interpolation methods and parametric models employed by central banks and investment banks globally. Participants implement linear and polynomial interpolation, Vandermonde matrix inversion, Newton divided difference, Lagrange polynomials, and cubic spline methods—evaluating trade-offs across applications. The curriculum advances through regression-based modeling to the industry-standard Nelson-Siegel (NS) and Nelson-Siegel-Svensson (NSS) frameworks used by the Federal Reserve and ECB. Participants calibrate these models via non-linear optimization, interpret level, slope, and curvature factors economically, and conduct formal model validation — including out-of-sample performance and parameter stability testing.
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Introduces stochastic modeling of interest rate dynamics for derivatives pricing, risk-neutral valuation, and term structure evolution under uncertainty. Participants implement and calibrate the Vasicek model for analytical tractability and mean reversion, the Cox-Ingersoll-Ross (CIR) model ensuring non-negative rates via square-root diffusion, and extended frameworks including Hull-White for exact term structure fitting, Black-Derman-Toy and Black-Karasinski for lognormal rate distributions, and multi-factor models capturing independent level and slope dynamics. Calibration exercises using live market data develop practical judgment for parameter estimation, stability assessment, and model selection across applications from vanilla swap pricing to exotic derivatives and XVA calculations.
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Establishes dual competency in full revaluation and sensitivity-based partial revaluation — the two workhorses of institutional trading and risk management. Coverage spans US Treasury Bills and coupon-bearing Notes/Bonds with comprehensive P&L attribution, DV01/KRDV01 and Convexity/KRC analysis, and first- and second-order Taylor expansion approximations validated against full revaluation benchmarks. The module addresses IFRS 9 and US GAAP fair value accounting, including HFT/HTM classifications and FVTPL/FVOCI treatment. Fixed-income derivatives, such as Interest Rate Swaps, Cross-Currency Swaps, and Swaptions, are priced within multi-curve discounting structures, with Greeks-based approximation models bridging cash and derivatives analytics within unified risk models.
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Addresses the transformation of complex bond cashflow structures into standardized risk factor exposures for portfolio-level VaR, stress testing, and regulatory capital calculations. Participants master mapping methodologies progressing from nearest tenor matching and linear interpolation through duration matching using linear programming that preserves dollar duration (DV01/KRD), to variance matching via quadratic optimization incorporating full yield curve correlation structure — consistent with J.P. Morgan's RiskMetrics framework. The Generalized Reduced Gradient (GRG) algorithm is implemented in both Python's scipy.optimize and Excel Solver. Solutions are validated against full revaluation benchmarks, and the module concludes with diversified portfolio DV01, key rate duration profiles, and portfolio-level VaR.
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Establishes production-level expertise in Value-at-Risk and Expected Shortfall for regulatory capital, internal risk limits, and portfolio risk estimates. Participants implement Historical Simulation VaR using non-parametric return distributions applied to current positions, and Parametric VaR using analytical covariance matrices under normality. Fixed-income coverage spans both full revaluation DCF and partial revaluation sensitivity-based (DV01/KRD, KRDV01 + Convexity) approaches, with rigorous model comparison across accuracy and efficiency dimensions. Advanced topics include variance matching, PCA decomposition of risk factors, and VaR decomposition via Conditional VaR, Incremental VaR, and Marginal VaR for capital allocation, and developing end-to-end risk systems meeting institutional and regulatory standards.
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Subscription
Pricing Plan
16,000 INR
100% Refund (No Questions Asked) within 2 hours of subscription.