Excel And Python-Powered Investment And Risk Management Program
Fixed-Income Investment And Risk Management
The Fixed-Income Investment and Risk Management (FIRM) Program is an advanced training program designed to equip professionals with in-depth knowledge, hands-on expertise, and quantitative techniques essential for effectively managing fixed-income investments and risks. Offered by one of the most trusted platforms in financial education, this program bridges the gap between theoretical frameworks and real-world investment strategies, risk measurement, fixed-income modeling, and automation techniques. Ideal for: Financial Risk Managers, Traders and Portfolio Managers, Financial Engineers, Fixed-Income Analysts, Post-Grad Students and Professionals preparing for roles in: Banks, Investment Firms, Asset Management Companies, Consulting Firms, Mutual Funds and Hedge Funds, and Other Financial Institutions
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HOURS ON-DEMAND
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QUANT RISK MODELS
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REAL-TIME PROJECTS
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4.9
BLOGS PUBLISHED
RATING ACHIEVED
What You'll Learn
Equities, Interest Rates, Modeling Systematic Risk, and Monitoring Yield Spreads: Market Data for Equities, Equity Shocks, Measuring Equity Risk, and Extreme Risk Analysis. Fixed-Income Markers, Yield Curve Interpretation, Interest Rate Shocks, Market Monitoring, and Reporting.
Market Data Management and Automation: Multi-Asset Market Data – Equities, Interest Rates, Currencies, Commodities, Cryptocurrencies, Including Derivatives, Understanding Derivative Instruments – Options, Futures, Forwards, and Swaps, and Python Automation for Market Data. Descriptive and Inferential Statistics, and Probability Distributions: Normal and Log-Normal Distributions.
Modeling Term-Structure of Interest Rates: Yield Curve Construction, Basic and Advanced Interpolation Methods: Vandermonde Matrix, Newton's Divided Difference, Lagrange, Cubic Spline, Modeling Yield Curve using Nelson-Seigel (NS) and Nelson-Seigel-Svensson (NSS) Models, and Model Validation.
Modeling Short-Rate and Interest Rate Factors: Stochastic Interest Rate Models – Vesicek Model, Cox-Ingersoll-Ross (CIR) Model, Hull-White Model, Backtesting Models, Introduction to Principal Component Analysis (PCA) for Interest Rate Modeling, PCA Modeling, and Reduced Model in Perspective.
Pricing and Valuation of Fixed-Income Securities:
Full Valuation DCF Model for US Treasury Securities, Corporate Bonds, IRS, FRAs, Swaptions, Mark-to-Market PnL Computation, Interest Rate Scenario Analysis and Sensitivities. Partial Revaluation Sensitivity-Based Model – Duration Approach (DV01), Duration-Convexity (DC) Approach, and Residual PnL, Bond Cashflow Mapping Procedure – Nearest Tenor Matching and Variance Matching Approach.
Scenario and Sensitivity Analysis, and Stress Testing:
Interest Rate Sensitivities – Duration, DV01, Convexity. DV01-Neutral Curve Spread Strategy. Introduction to Interest Rate Scenarios – Parallel, Non-Parallel Shifts – Bull-Bear Steepener and Flattener, and Regulatory Scenarios. Scenario Creation Methodologies – Ladder-Based, Historical, Event-Specific, Hypothetical, and Antithetic, PCA Model Calibration and Scenario Generation. Scenario Revaluation and PnL Computation.
Value-at-Risk, Stress Value-at-Risk, Expected Shortfall, and Advancements: Introduction, VaR, SVaR, IVaR, MVaR for Equities, Fixed-Income Securities (Bonds, IR Swaps), Option Derivatives with Historical, Variance-Covariance, Monte-Carlo Method, PCA Model for VaR. Stress Period Selection for Stressed VaR. Model Development and Validation through Backtesting & Stress Testing. Basel Regulations, Fundamental Review of Trading Book (FRTB).
Premium Subscription
Fixed-Income Investment And Risk
reach out to us at contact@thefinanalytics.com
INR 12,000
USD 140
Subscription Info.:
Prerequisites: Python Basics
50+ hrs Duration (12 Months Access)
Live Sessions (Instructor-led Interactive) + Recordings
Recorded Sessions (Self-Paced)
8+ Hands-On Projects (Team Collaboration)
Supported Devices: Desktop, Laptop, iPad (Not Supported on
Mobile Devices)
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Program Coverage Curated By Experienced Mentors
We're focused on delivering practical skills to data science, data analytics, and finance professionals with an in-depth understanding & implementation using python. We never stop adding more content to it.
Introduction to Financial Markets, Products, And Derivative Instruments
→ Introduction to Financial Markets, Introduction to Financial Products and Instruments – Equities, FI Securities - Debt Securities | Issuers - Government | Agencies | Municipals | Corporates | Treasury Securities - T-Bills | T-Notes | T-Bonds | TIPS | Term-Structure of Interest Rates - Short-Term | Medium-Term | Long-Term, Market Data for Equities, Interest Rates, Historical Time Series Data And Interest Rate Shocks
→ US Treasury Yield Spread, Monitoring USD10Y3M Yield Spread - Historical Levels - Peak | Trough | Current, Monitoring SnP500 Equity Index - Performance Measures - Rolling Maximum Cumulative Loss | Maximum Drawdown | Preparing Market Report - Description | Financial Crisis | Economic Recessions, S&P 500 Performance: Cumulative Loss And Maximum Drawdown, Monitoring Yield Spreads And S&P 500 Performance
Introduction to Python Programming [7 Sessions]
Modeling Term-Structure of Interest Rates
→ Yield Curve Construction – Interpolation Methods → Advanced Interpolation Methods – Vandermonde Matrix, Newton Divided Difference, Lagrange, And Cubic Spline Interpolation → Modeling Yield Curve – Linear Regression Model, Polynomial Regression Model (Single Factor), Nelson Siegel (NS) And Nelson Siegel Svensson (NSS) Models → Model Validation – Nelson Siegel (NS) And Nelson Siegel Svensson (NSS) Models | Evaluation Metrics – Mean Absolute Error (MAE) | Mean Squared Error (MSE) | Root Mean Squared Error (RMSE) | Median Absolute Error (MedAE) | Maximum Error (ME) | Mean Absolute Percentage Error (MAPE) | Residual Sum of Squares (RSS) | Total Sum of Squares (TSS) | Coefficient of Determination (R²)
→ Project: A Research Beyond Yield Curves: Best-Fit Model For Yield Curve Estimation
Modeling Short-Rate And Interest Rate Factors
→ Modeling Interest Rates – Merton's Model, Vesicek Model, Cox-Ingersoll-Ross Model, Ho-Lee Model, Hull-White Model | Extensions - Adding Jumps, Stochastic Volatilities, Other Factors & Correlations
→ Modeling Interest Rate Risk Factors – Principal Component Analysis (PCA) | Statistics – Variance | Covariance-Correlation Matrix | Normalization | Principal Component Identification – Level | Slope | Curvature | Eigen Decomposition – Values & Vectors | Dimensionality Reduction | PC Computation & Uncorrelated Shocks,
→ The Reduced Model In Perspective | Reduced Model Process | Population | Sample Set Represents The Population | Population Change | The Inverse Problem | Steps To Generate Principal Components | General Data Transformation
Pricing And Valuation of Fixed-Income Securities
→ Full Valuation DCF Model – US Treasury Bills, US Treasury Notes/Bonds
→ Interest Rate Movement And Mark-to-Market PnL | Constant Rate Simulation | Incremental PnL – Interest Rate Movement vs. Pull-to-Par Effect | Price-Yield Relationship | Analysis Report And Commentary
→ Full Valuation DCF Model – US Treasury Notes/Bonds – Mark-to-Market | Interest Rates Term-Structure | Interest Rate Curve Construction | Fixed-Income Product - 10Y US Treasury Note | Discounting Cashflow Model | Discount Factors | Present Values | Model Price vs. Issue/Market Price | Model Price Difference | Valuation Report | Partial Revaluation - Taylor Series Expansion/Approximation - Duration-Convexity PnL
→ Valuation Report of US Treasury Securities And Mark-to-Market
Fixed-Income Portfolio Risk Measurement And Management
→ Market Interest Rate Scenario – Parallel Shifts, Non-Parallel Shifts - Bull And Bear Steepening/Flattening
→ Sensitivity-Based Risk And PnL Attribution – Fixed-Income Securities | Partial Revaluation vs. Full Revaluation DCF Methodologies | Taylor-Series Approximation | Risk Sensitivities – Duration | DV01 | Convexity | Duration-Convexity PnL | Residual PnL | Methodology Difference – Full Valuation vs. Taylor Series Expansion/Approximation - Duration-Convexity PnL vs. Ladder-Based Interpolation Technique | Model Limitations | Market Risk Report
→ Introduction to Value-at-Risk (VaR) Measure, Fixed-Income Portfolio Value-at-Risk – Historical Simulation Method | Risk Attribution to Market Risk Factor(s) And Specific Factors - TMR, GMR, ESR | Market VaR Report