Welcome to the Quant Market Risk Management Program
A Comprehensive Journey Awaits You!
Dear Professional,
Welcome to the Quant Market Risk Management (QMRM) program, proudly offered by one of the most trusted and recognized platforms in financial education and training. You are now become part of a distinguished network of professionals who are shaping the future of risk management.
The QMRM program is designed to deliver exceptional learning outcomes, blending rigorous theoretical foundations with practical, hands-on experiences. What you learn here is not just theoretical knowledge but practical experiences that are immediately relevant and applicable to the fast-paced world of financial markets.
Your journey has been thoughtfully structured to build layer upon layer of expertise, taking you from foundational principles to advanced applications. Each module is curated to empower you with the ability to perform complex risk calculations, develop risk management strategies, and confidently mitigate financial risks.
Your success in this program is not just about completing the lectures as per the roadmap—it is about your commitment to engaging deeply with the content, asking questions, exploring concepts, and applying what you learn to real-world scenarios. The roadmap below will serve as your guide, but it is your discipline, curiosity, and dedication that will transform your potential. So take this opportunity to fully immerse yourself in each session, every module, and every hands-on project!
Basic Modules (1 to 5)
The foundational modules are designed to provide you with a robust understanding of financial markets, financial products and derivative instruments, and essential data-handling skills, ensuring a solid start to your journey in market risk management.
Module 1: Introduction to Financial Markets, Products, and Derivative Instruments
Develop a comprehensive understanding of the structure and operation of financial markets.
Explore key financial instruments, including equities, bonds, commodities, FX, and derivatives.
Gain insights into derivative structures such as futures, options, swaps, and forwards, and their applications in the financial industry.
Module 2: Equities | Modeling Systematic Risk
Learn the principles and differences between systematic and unsystematic risk.
Use advanced statistical tools to build and analyze models that quantify equity market risks.
Module 3: Interest Rates | Monitoring Yield Spreads
Understand the dynamics of interest rates and their critical implications for market risk.
Analyze yield spreads, interpret their significance, and apply them in portfolio risk assessments.
Module 4: Currencies, Commodities, Cryptocurrencies, and Derivatives
Module 5: Market Data Management
Multi-Asset Class Approach: Apply market data techniques across a variety of financial instruments, including:
Equities (EQ)
Foreign Exchange (FX)
Interest Rates (IR)
Commodities (COM)
Derivatives (F&O)
Develop a comprehensive skill set tailored for diverse market scenarios.
Python Automation: Create automation scripts for: Market data extraction and processing. Financial analysis and real-time market monitoring.
Model Development: Build robust market data models using Python, ensuring efficiency and accuracy in financial data management and analysis.
Hands-On Applications: Extract, process, and automate data handling for multiple asset classes. Use Python for scripting and automation to streamline market data workflows. Develop visualizations to convert raw data into actionable insights for decision-making.
Intermediate Modules (6 to 10)
In this phase, you will build on your foundational knowledge to explore statistical methods, advanced interest rate modeling, volatility modeling, stochastic processes, and performing simulations.
Module 6: Descriptive and Inferential Statistics, and Probability Distributions
Develop statistical literacy essential for analyzing financial data.
Understand the role of probability distributions in financial modeling and risk analysis.
Module 7: Modeling Term-Structure of Interest Rates
Yield Curve Construction:
Linear Interpolation and Extrapolation: Learn how to estimate interest rates for maturities not directly observed in the market using straightforward linear methods.
Bootstrapping: Master the step-by-step approach to derive zero-coupon yields from coupon bond prices, constructing forward and spot rates for various maturities.
Ordinary Least Squares (OLS) Method: Apply regression techniques to model the relationship between interest rates and explanatory variables.
Interest Rate Modeling Techniques: Explore advanced models for analyzing and predicting the term structure of interest rates:
Linear Models: Understand the simplest form of modeling, establishing a straight-line relationship for short-term maturities.
Polynomial Models:
Quadratic: Capture non-linear relationships in interest rates using second-order polynomial equations.
Cubic Spline Piecewise: Create smooth and continuous yield curves with piecewise polynomials, providing flexibility for different maturity segments.
Quartic: Use fourth-order polynomials for higher precision in modeling complex yield curve shapes.
Nelson-Siegel Model: Learn to fit the yield curve with parameters reflecting level, slope, and curvature, providing an intuitive interpretation of yield curve dynamics.
Nelson-Siegel-Svensson Model: Expand on the Nelson-Siegel approach with an additional curvature factor, enabling a better fit for yield curves with multiple humps or complex shapes.
Hands-On Applications:
Yield Curve Construction: Build yield curves from real-world market data using bootstrapping and interpolation techniques.
Model Comparison: Analyze and compare the performance of linear, polynomial, and advanced models like Nelson-Siegel and Svensson.
Model Calibration: Calibrate models to ensure an accurate fit to observed market data.
Module 8: Modeling Short-Rate and Interest Rate Factors
Vasicek Model: Understand the Vasicek model, a mean-reverting stochastic process for modeling interest rate dynamics. Learn its applications in estimating yield curves and pricing bonds.
Cox-Ingersoll-Ross (CIR) Model: Study the CIR model, known for incorporating non-negative interest rates. Explore its use in modeling interest rate volatility and bond pricing.
Hull-White Model: Analyze the Hull-White extension to the Vasicek model, adding time-dependent parameters for greater flexibility. Apply this model to capture complex yield curve movements and dynamics.
Hands-On Applications: Model Calibration: Learn to calibrate Vasicek, CIR, and Hull-White models to historical data for improved accuracy. Validate the effectiveness of these models by comparing predicted yield curves with observed market data.
Principal Component Analysis (PCA): Understand variance, covariance, and correlation matrices. Learn normalization techniques to prepare data for PCA. Principal Component Identification: Extract key factors affecting interest rates: Level, Slope, and Curvature. Perform Eigen decomposition to compute Eigenvalues and Eigenvectors. Reduce dimensionality for more efficient analysis while retaining key insights.
PCA-Based Shock Modeling: Compute principal components and generate uncorrelated shocks for risk analysis. Understand the reduced model process and how it simplifies large data sets. Learn how sample sets represent populations and adapt to population changes. Master the step-by-step process of PCA for data transformation. Inverse Problem Application: Address the inverse problem to trace original data characteristics from principal components.
Module 9: Modeling Volatilities, Volatility Skew, and Surfaces
Examine the behavior of market volatilities and their implications for risk and pricing.
Learn to analyze volatility skew and surfaces in derivatives markets, and apply these insights to hedging strategies.
Module 10: Stochastic Processes and Simulations
Stochastic Processes: Brownian Motion: Understand the foundational stochastic process used in financial modeling, capturing random movements in asset prices. Mean-Reverting Models: Explore processes like the Ornstein-Uhlenbeck model, which tends to revert to a long-term average.
Simulation Techniques:
Historical Simulation: Use historical market data to simulate future price paths and estimate potential risks. Simplicity and reliance on observed market behaviors. Dependency on historical patterns, which may not capture future conditions.
Monte Carlo Simulation: Point Estimation: Simulate single outcomes for pricing and risk assessment. Path Estimation: Generate multiple paths using stochastic differential equations to provide a comprehensive view of potential market scenarios.
Stochastic Differential Equations (SDEs): Learn how SDEs describe the evolution of financial variables under uncertainty. Use SDEs to model asset prices, interest rate movements, and derivatives. Monte Carlo Simulation with SDEs: Implement simulations using SDEs for pricing complex financial instruments and evaluating portfolio performance under stress scenarios.
Hands-On Applications: 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.
Core Modules (11–15)
The core phase focuses on integrating your knowledge into advanced pricing, valuation, and risk management methodologies.
Module 11 and 12: Pricing and Valuation of Fixed-Income Securities and Derivative Instruments
Interest-Rate Bonds and IR Swaps: Learn the Discounting Cash Flow (DCF) Model for pricing bonds and interest-rate swaps, emphasizing the time value of money and cash flow discounting techniques.
Equity Futures, Forwards, and FRAs: Apply the Cost of Carry Model to evaluate the fair value of futures, forwards, and forward rate agreements (FRAs) based on underlying asset characteristics.
Equity, Rates, and FX Option Derivatives: Understand various pricing approaches, including:
Binomial Model: Single-Period, Two-Period, and Multi-Period binomial trees. Approaches: Risk-Neutralization, Delta-Hedging, and Replicating Portfolio.
Black-Scholes-Merton Option Pricing Model: Master the mathematical framework for option valuation under specific assumptions.
Monte Carlo Simulation: Point Estimation: Determining option prices by simulating a single outcome. Path Estimation: Using multiple simulated paths for more accurate valuation.
Full Revaluation vs. Partial Revaluation: Full Revaluation: Conduct exhaustive calculations by fully revaluing the portfolio for every scenario. Partial Revaluation: Utilize approximations to achieve efficiency without significant accuracy loss, leveraging:
Taylor Series Expansion/Approximation: Simplify complex derivatives calculations.
Ladder-Based Interpolation Technique: Estimate intermediate values efficiently.
Put-Call Parity and Testing Assumptions: Test and validate option prices under this foundational principle, ensuring alignment between theoretical and market prices.
Model Validation and Challenging Assumptions: Critically evaluate model inputs and assumptions, such as:
Distributional assumptions. Constant volatility and interest rate assumptions. Accuracy and reliability of model inputs and formulas under varying market conditions.
Work through real-world examples to: Price fixed-income securities using the DCF model. Value equity futures, forwards, and FRAs using the Cost of Carry Model. Analyze and validate derivative pricing using Binomial Trees, Black-Scholes-Merton, and Monte Carlo techniques. Test the robustness of option prices under the Put-Call Parity theorem and revaluation methods.
Module 13: Scenario Analysis, Sensitivities, and Stress Testing
Risks Across Asset Classes: Understand the risks involved in Equities, Bonds, Options, and Swaps.
Identification of Risk Factors and Computation of Shocks: Measure risks using absolute and proportional shocks to account for potential market fluctuations.
Scenario Generation and Expansion: Generate scenarios for various market conditions:
Spot Scenarios: Changes in price levels.
Volatility Scenarios (Vol): Changes in implied or historical volatility.
SpotVol Scenarios: Combined price and volatility changes.
Ladder Scenarios: Layered stress tests for gradual or extreme shocks.
Scenario Types: Explore different types of scenarios:
Historical Scenarios: Based on past market events.
Hypothetical Scenarios: Assumptions about future market conditions.
Event-Specific Scenarios: Tailored to specific market events or crises.
Scenario Creation for Identified Shocks: Design scenarios for Spot Shocks: Positive and Negative Shocks. Antithetic Scenarios for mirrored outcomes. Applied to asset classes: Equities, Rates, FX, and Commodities.
Develop Volatility Shocks: Normal/Local Volatility: Risk modeling for individual instruments. Log-Normal/ATM Implied Volatility: Comprehensive market impact analysis.
Full Revaluation Model: Revalue portfolios under stress scenarios to assess the true impact on portfolio value.
Scenario Profit & Loss Analysis: Measure the potential gains or losses under different market scenarios. Evaluate the associated risks across: Equities (EQ), Interest Rates (IR), Foreign Exchange (FX), and Commodities (COM). Assess Cross Risks: Analyze the interaction of risks across multiple asset classes.
Breaches and Exceptions in Scenario Limits: Flagging Exceptions: Identify breaches in predefined scenario limits, flags, or thresholds. Monitor deviations that may indicate significant risk exposures. Management Action: Provide actionable recommendations to mitigate identified risks. Develop contingency strategies for handling exceptions.
Practical Applications: Design and simulate stress-testing frameworks tailored to equity, fixed-income, FX, and commodity portfolios. Perform sensitivity analysis to measure the impact of specific risk factors on portfolio performance. Develop scenario-based profit and loss (PnL) reports to inform risk management strategies.
Module 14: Scenario Analysis, Sensitivities, and Stress Testing
Market Risk Across Asset Classes: Understand the scope of market risk for the following asset classes: Equity: Price movements and market beta. Interest Rate: Changes in yields, duration, and convexity. Currency/FX: Exchange rate fluctuations and their impact on portfolios. Commodity: Price volatility in raw materials and goods markets.
Risk Sensitivities: Gain in-depth knowledge of key risk sensitivities and their applications in risk analysis: Equity Risk Sensitivities: Beta for market exposure. Fixed-Income Sensitivities: Duration, DV01, and Convexity for interest rate risks. Derivative Sensitivities (Greeks):
Delta: Sensitivity to price changes in the underlying asset.
Gamma: Rate of change of Delta, indicating non-linear risk.
Vega: Sensitivity to volatility changes.
Theta: Impact of time decay on option prices.
Rho: Sensitivity to changes in interest rates.
Vanna: Impact of changes in Delta and volatility simultaneously.
Volga: Sensitivity to second-order changes in volatility.
Understand the impact of: Moneyness: In-the-money (ITM), at-the-money (ATM), and out-of-the-money (OTM) options. Volatility: Its effect on option pricing and portfolio value. Time: Influence of time decay on option values.
Sensitivity-Based Models: Explore models that leverage sensitivity analysis to estimate portfolio risks:
Taylor Approximation: Simplify the modeling of non-linear risks by approximating changes using first and second-order derivatives.
Ladder-Based Interpolation: Use ladder structures to estimate intermediate values efficiently in risk modeling.
Hedging Techniques: Learn to apply static and dynamic hedging techniques to remain risk-neutral:
Static Hedging: Implement once and monitor without frequent adjustments.
Dynamic Hedging And Rebalancing: Continuously adjust positions to respond to market changes. Hedging Strategies:
Delta Hedging: Neutralizing directional risk.
Gamma Hedging: Managing second-order risks in non-linear portfolios.
Delta-Gamma Hedging: Combining first and second-order hedges for comprehensive risk management.
Vega Hedging: Managing volatility sensitivity.
Delta-Vega Hedging: Balancing directional and volatility exposures.
Multiple Risk Sensitivity Trades: Construct trades to offset exposures across multiple Greeks simultaneously for holistic risk management.
Monitoring and Managing Breaches: Exceptions and Breaches: Identify and manage breaches in sensitivity limits, flags, or thresholds. Implement effective escalation protocols and corrective actions to maintain portfolio integrity.
Module 15: Value-at-Risk (VaR) Methodologies and Advancements
Master industry-standard VaR approaches:
Historical Simulation Method: Learn to estimate VaR using historical data, capturing real-world market behaviors.
Parametric Method: Understand analytical approaches relying on distributional assumptions, such as variance-covariance techniques.
Monte Carlo Simulation Method: Learn to use probabilistic techniques for modeling potential losses under complex scenarios.
Explore advanced methodologies:
Downside Risk and Tail Risk: Assess potential losses beyond expected scenarios.
Conditional Value-at-Risk (CVaR or Expected Shortfall): Go beyond VaR to measure the average loss in the worst-case scenarios.
Stressed Value-at-Risk (SVaR): Analyze risk under extreme market conditions, a requirement in regulatory frameworks.
Incremental and Marginal Value-at-Risk (IVaR & MVaR): Quantify the contribution of individual assets or positions to overall portfolio risk.
Full Revaluation: Perform detailed calculations by fully revaluing the portfolio for every scenario. Partial Revaluation (Taylor Approximation): Use approximations to save time while maintaining reasonable accuracy for VaR estimates.
Backtesting VaR and ES Models: Validate model performance by comparing predicted losses with actual outcomes. Exceptions and Breaches: Understand how to flag, monitor, and address breaches in VaR limits or thresholds. Traffic Light Approach: Learn regulatory methods for evaluating the accuracy of VaR models based on exception frequency.
Explore the implications of VaR and ES on a bank’s Capital Adequacy Requirement, focusing on compliance with Basel III regulations and internal risk policies. Equity Risk Assessment: Implement and compare Historical Simulation and Parametric VaR methods. Analyze historical data, generate VaR estimates, and validate results using backtesting techniques.
Note: We are currently running the latest live batch QMRM 7.8 and 7.10, which are at the 40% and 8% completion stage, respectively. Recorded sessions are being uploaded in sync with this progress, and you’ll find 40% of the content added to the resource and the roadmap at present, with the rest being added as we advance through the live batch for up-to-date content.
Getting Started!
Your journey begins with setting up your development environment—a critical first step in ensuring a seamless and productive experience throughout the program. You’ll establish your tools, gain familiarity with essential platforms, and integrate Python with Excel for a powerful analytics workflow.
What You’ll Learn
How to use Anaconda Navigator as your central hub for performing automated tasks.
Introduction to Jupyter Notebook, an interactive platform for coding, data visualization, and presenting Python-based projects.
Seamless integration of Python with Microsoft Excel for enhanced data handling and visualization capabilities.
Installation Guide: Getting Started with Anaconda Navigator: Installation Guide
to begin, download and install Anaconda Navigator, a comprehensive platform for managing Python libraries, packages, and environments.
Watch: Anaconda Navigator Application
In this session, You’ll explore everything you need to get started with Anaconda Navigator:
Topics Covered:
Installation Process: From download to setup on your machine.
Understanding the differences between an Integrated Development Environment (IDE), Code Editor, and Compiler.
Managing Python Libraries and Packages for efficient workflows.
Recommended tools and configurations for this course.
Watch: Jupyter Notebook
Dive into Jupyter Notebook, a powerful and versatile platform for interactive computing. This session covers:
Launching Jupyter Notebook from Anaconda Navigator.
Navigating the interface: Default directories, creating new notebooks, and managing files.
Understanding the Menu Options, Toolbar, and commonly used Keyboard Shortcuts.
Using Code Cells for writing and executing Python code.
To complete your setup, ensure you have Microsoft Excel installed: Microsoft's official site
How to Download: Visit Microsoft’s official site to download Excel. Note that a valid license or subscription may be required for full access.
Module 1: Introduction to Financial Markets, Products, And Derivative Instruments
financial markets are the bedrock of the global economy, and understanding their mechanics is crucial for anyone looking to make informed decisions, whether you're an investor, a business professional, or just a curious learner. We'll start by examining the different types of financial markets, from equity, fixed-income, and forex to commodities, and understand their significance. But markets are just venues – what's traded in them? that's where financial products come in. Instruments like stocks, bonds, and derivatives that investors buy and sell. By understanding the key components of financial markets and the diverse instruments traded within them, you’ll build a foundation for analyzing market dynamics effectively.
Lecture: Introduction to Financial Markets
In this session, we’ll explore the fundamental structure and functions of financial markets, including:
Capital Markets: Primary and Secondary Markets.
Equity Markets: Buying and selling of shares.
Debt Markets: Bonds and other fixed-income securities.
Forex/Currency Markets: Exchange of currencies.
Commodity Markets: Trading of physical or financial commodity products.
Derivatives Markets: Trading instruments like futures, options, and swaps.
Exchange-traded vs. Over-the-Counter (OTC) transactions.
Lecture: Introduction to Financial Products And Instruments – Equities
Explore equities and understand their role in financial markets. Key topics include:
Financial Products and Securities: Stocks, debt instruments, loans, and deposits.
Equity Markets: The difference between Primary Markets (IPO issuance, private placement) and Secondary Markets (trading existing shares). Understanding the Order Book in stock exchanges.
Equities: Benefits of owning stocks: Capital Appreciation, Dividend Income, and Dividend Reinvestment Plans (DRIP).
Recommended Readings:
"Balancing Equity Risk and Reward: Tradeoff": Learn about the delicate balance between risk and reward in equity investments.
"Forward Contracts vs. Futures Contracts: What's the Difference?": Understand the critical distinctions between these two derivatives.
Lecture: Introduction to Financial Products And Instruments – FI Securities
Explore fixed-income securities and their role in financial markets. Key focus areas include:
Debt Securities: Types of issuers: Government, Agencies, Municipals, and Corporates.
Treasury Securities: T-Bills, T-Notes, T-Bonds, and TIPS (Treasury Inflation-Protected Securities).
Interest Rate Concepts: The term-structure of interest rates: Understanding Short-Term, Medium-Term, and Long-Term rates and their impact on fixed-income investments.
Recommended Reading:
"Understanding Fixed-Income Treasury Securities": Deepen your understanding of the various treasury securities and how they function in the market.
Module 2: Equities | Modeling Systematic And Unsystematic Risk
Equities are thrilling, complex, and incredibly important to financial markets, offering exciting opportunities but with inherent risks that every investor must understand. This week, we’ll dive deep into equities—your stake in a company—and explore the factors that drive their value. While equities can yield impressive returns, they are also subject to fluctuations influenced by various risk factors.
To master equities, it’s essential to understand the two primary types of risks:
Systematic Risk: These are the macro-level risks that affect the entire market, such as economic downturns, geopolitical events, or monetary policy changes.
Unsystematic Risk: These are specific to individual investments or sectors, like management decisions, product failures, or competitive pressures.
History provides a wealth of examples where these risks have reshaped equity markets. We’ll analyze past events, drawing insights to help you better understand and anticipate equity market dynamics.
It isn’t just about the basics. We’ll also explore advanced risk assessment methodologies, including:
Block Maxima: A statistical method used to study the extreme highs in equity returns over specified periods.
Extreme Value Theory (EVT): A powerful tool for assessing the likelihood and impact of rare but extreme market events, such as crashes or surges.
These tools will give you a deeper understanding of how to model and manage risks, especially during extreme market conditions.
Lecture: Market Data for Equities
Learn how to extract and analyze equity market data efficiently. This session focuses on:
Historical and Intraday Time-Series Data Extraction: Single and multiple stocks. Data points: Open, High, Low, Close, Adjusted Close, and Volume.
Data Visualization: Create price charts for stocks to identify trends and patterns. Use data indexing techniques for efficient data manipulation and analysis.
Lecture: Historical Time Series Data And Equity Returns/Shocks
the concept of equity returns (shocks) to understand their role in financial analysis and risk assessment. This session focuses on:
Absolute Returns/Shocks: Understanding raw changes in price over time.
Proportional/Relative Shocks (Discrete): Measuring percentage changes in price for relative comparisons.
Shock Type Use and Comparison: When and why to use absolute vs. relative shocks in financial analysis.
Lecture: Equity Risk – Systematic And Unsystematic
In this session, we explore the two major components of equity risk and the metrics used to measure and analyze them:
Risk Measures: Variance and Standard Deviation: Quantifying the volatility of equity returns. Covariance and Correlation: Understanding relationships between assets for diversification. Beta: Measuring sensitivity to market movements.
Types of Equity Risk: Systematic (Market) Risk: Risks inherent to the entire market, such as macroeconomic factors. Unsystematic (Idiosyncratic) Risk: Asset-specific risks, such as company performance.
Other Key Metrics: Downside Deviation: Assessing potential for losses. Annualized Risk-Return Profile: Combining expected return with risk metrics for better decision-making.
Recommended Readings:
"The Basics of Standard Deviation: A Simple Guide": Build a clear understanding of how standard deviation measures volatility.
"Covariance and Correlation: From Diversification to Standardization": Learn the importance of these metrics in creating a diversified portfolio.
Lecture: Equity Risk – Extreme Value Theory (EVT)
In this session, you’ll explore the statistical techniques used to model and analyze extreme risks in equity markets. Topics include:
Return Distribution: Understanding the statistical behavior of equity returns. Analyzing the characteristics of extreme outcomes within a return distribution.
Cumulative Distribution Function (CDF): Using CDFs to determine probabilities of returns within a given range. Focus on tail distributions for extreme event analysis.
Tail Distributions: Left Tail: Measuring downside risk (losses). Right Tail: Understanding extreme upside potential (gains).
Probability Distributions: Exploring key distributions, including Normal and Exponential distributions. Understanding parameters and their implications for risk modeling.
Regulators' Standpoint: How regulators assess financial risk stability. Importance of tail risk management in compliance and reporting.
Lecture: Equity Risk – Block Maxima And Peaks-Over-Threshold (POT)
In this session, you'll explore advanced EVT methodologies for analyzing extreme events in financial markets:
Block Maxima Method: Divide data into blocks and analyze the maximum within each block. Fit the maxima to a generalized extreme value distribution (GEV) for forecasting risks.
Peaks-Over-Threshold (POT) Method: Focus on values exceeding a predefined threshold to capture extreme observations. Fit the excesses to a generalized Pareto distribution (GPD) for precise tail analysis.
Recommended Reading:
"Block Maxima and Extreme Value Theory in Finance": Gain a deeper understanding of EVT applications, the limitations of each method, and their relevance to financial risk management.
Module 3: Interest Rates | Monitoring Yield Spreads
Welcome to Week 3, where we will understand the market of interest rates and the critical concept of monitoring yield spreads. Understanding interest rates is paramount, as they influence various aspects of the financial landscape. We'll explore historical time series data to decode interest rate shocks and their impact on the market.
In our exploration, we will discuss the Treasury Yield Curve, examining its normal, inverted, and humped/flat shapes. This week's lessons will equip you with the knowledge to interpret different yield curve profiles and understand their implications. Additionally, we'll investigate the US Treasury Yield Spread, focusing on a specific spread and how to identify yield curve profile changes.
As part of your practical application, you'll engage in a project: Monthly Market Report. This hands-on exercise involves monitoring yield spreads and analyzing S&P 500 performance. By the end of the week, you'll have a comprehensive understanding of interest rates, yield spreads, and the practical skills to navigate these delicacies in the financial market.
Lecture: Market Data for Interest Rates And Yield Curve
Treasury Yield Curve - Normal | Inverted | Humped/Flat | Historical Time-Series of Interest Rates | 2007-08 & 2022-23 Interest Rate Profiles | Market Sentiments | FED 2024-25 Targets
Read: Normal, Inverted, and Humped Interest Rate Curve
Lecture: Historical Time Series Data And Interest Rate Shocks
Absolute Returns/Shocks | Proportional/Relative Shocks - Discrete | Continuous | Profile of Interest Rates – 10Y & 3M | Variability Profile | YC Profile – Current Rates & Shocks
Lecture: US Treasury Yield Spread
Treasury Yield Spread - 10Y3M Spread | Yield Spread Table | Interpretation And Identification of Yield Curve Profile And Inversions
Lecture: Monthly Market Report: Monitoring Yield Spreads And SnP 500 Performance
Your objective is to prepare a market report, focusing on the dynamics of the US Treasury Yield Spread and S&P 500 Equity Index data from 1990 to the present.
Lecture: Monitoring USD10Y3M Yield Spread
Treasury Yield Spread - USD10Y3M Spread | Historical Levels - Peak | Trough | Current | Preparing Market Report - Description | Financial Crisis | Economic Recessions | Advice - Long/Short Position
Lecture: Monitoring SnP500 Equity Index
Equity Market Index - SnP 500 Index | Performance Measures - Rolling Maximum Cumulative Loss | Maximum Drawdown | Preparing Market Report - Description | Financial Crisis | Economic Recessions | Chart
Lecture: SnP 500 Performance: Cumulative Loss And Maximum Drawdown
Performance Measures - Growth Index | Cumulative Losses | Maximum Drawdown | Period - 1990 to Present | Generate Consolidated Market Report
Week 5: Market Data Management
Welcome to Week 5
Watch: Market Data Management – Equities
Model Development - Equities - Fetching Historical Time-Series Data | Identification of Current Prices | Computation of Returns - Absolute Returns, Proportional Returns - Discrete, Continuous | Export Data
Watch: Market Data for Currencies
Extract Historical Time-Series Data - Currencies - Developed Markets, Emerging Markets, Frontier Markets - Open | High | Low | Close | Adjusted Close | Volume | Data Visualization - Price Chart | Currency Conversion Methodology | Quotations - Direct, Indirect
Watch: Market Data for Derivatives
Extract End-Of-Day And Historical Time-Series Data - Multiple Options | Option Chain for Calls and Puts at Multiple Strikes for Multiple Expiries | Important Dates
Watch: Market Data Management – Interest Rates And Currencies
Model Development - Interest Rates And Currencies - Fetching Historical Time-Series Data | Identification of Current Prices | Computation of Shocks | Latest Exchange Rates Matrix | Export Data
Watch: Market Data Management – Derivatives
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Watch: Market Data Management – CryptoCurrencies
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Week 6: Descriptive And Inferential Statistics, And Probability Distributions
Welcome to Week 6
Watch: Descriptive Statistics - Univariate And Bivariate Analysis
Univariate Analysis - Measures of Central Tendency - Arithmetic Mean | Geometric Mean | Median | Mode | Measures of Variability/Dispersion - Range | Variance | Standard Deviation
Bivariate Analysis - Variance-Covariance | Correlation Coefficient [Pearson] | Correlation Classification Table | Visualization
Read: The Basics of Standard Deviation: A Simple Guide
Read: Covariance & Correlation: Diversification/Standard
Python Project: Identify Perfect Negative, Strong Negative, and Weak Negative Correlations, and Visualize
Watch: Normal Probability Distribution
Standard Normal Distribution | Random Variates/Sample | Probability Distribution | Cumulative Probability Distribution | Probability Density Function (PDF) | Cumulative Distribution Function (CDF) | Percent Point Function (PPF) | Python IGQs
Watch: Log-Normal Probability Distribution
Standard Log-Normal Distribution | Random Variates/Sample | Probability Distribution | Cumulative Probability Distribution | PDF | CDF | PPF | Normal vs. Log-Normal Distribution | Transformation | Parameters – Mean | Standard Deviation | Skewness | Kurtosis
Week 7, 8: Modeling Term-Structure of Interest Rates
Welcome to Week 6, where we venture into the fascinating domain of modeling the term-structure of interest rates. This week is dedicated to understanding the complexities of yield curve construction through various methods. We'll kick off by exploring Yield Curve Construction using Interpolation Methods such as linear, polynomial, and cubic spline. You'll gain insights into the construction process and the significance of day count conventions.
Next up, we'll delve into the Ordinary Least Squares (OLS) Regression Method for yield curve construction. This statistical approach involves understanding simple linear regression, model coefficients, and the unexplained component, allowing you to grasp the nuances of modeling the term-structure, tops with some advanced models: the Nelson Siegel (NS) and Nelson Siegel Svensson (NSS) models. These polynomial regression models provide a deeper understanding of the level, slope, and curvature components of the yield curve.
Watch: Yield Curve Construction – Interpolation Methods
Yield Curve Construction - Interpolation Methods - Linear | Polynomial | Higher-Order Polynomials - Quadratic | Cubic | Quartic | Day Count Convention - 30/360
Watch: Advanced Interpolation Methods – Vandermonde Matrix
Yield Curve Construction - Interpolation Methods - Vandermonde Matrix | System of Linear Equations | Determinant | Coefficients | Curve Fitting | Limitations
Watch: Advanced Interpolation Methods – Newton Divided Difference
Yield Curve Construction - Interpolation Methods - Newton's Divided Difference | Newton (Divided Difference) - First/Second/Third-Order Derivatives | Coefficients | Curve Fitting | Limitations - Degree & Extrapolation
Watch: Advanced Interpolation Methods – Lagrange And Cubic Spline Interpolation
Yield Curve Construction - Interpolation Methods - Lagrange & Cubic Spline | Coefficients | Curve Fitting | Limitations
Watch: Modeling Yield Curve – Linear Regression Model (Single Factor)
Modeling Term-Structure of Interest Rates - Ordinary Least Squares Method - Simple Linear Regression | Dependent & Independent Variable | Model Coefficients - Slope & Intercept | Unexplained Component - Error Term/Sum of Squared Residuals | Model Predictions | Best Fit Line
Watch: Modeling Yield Curve – Polynomial Regression Model (Single Factor)
Modeling Term-Structure of Interest Rates - Ordinary Least Squares Method - Polynomial Regression | Quadratic Regression | Cubic Regression | Dependent & Independent Variable | Model Coefficients - Slope & Intercept | High-Order Coefficients - Curvature | Unexplained Component - Error Term/Sum of Squared Residuals | Model Predictions | Best Fit Curve
Project: A Research Beyond Yield Curves: Best-Fit Model For Yield Curve Estimation
Actual vs. Predicted Interest Rates | Coefficient Table | Residuals | R-squared (Coefficient of Determination) | Model Performance
Watch: Modeling Yield Curve – Nelson Siegel (NS) And Nelson Siegel Svensson (NSS) Models
Yield Curve Construction - Nelson Siegel & Nelson Siegel Svensson Model - Polynomial Regression | Model Coefficients - Level, Slope & Curvature | Unexplained Component - Error Term | Model Predictions | Best Fit Line
Watch: Model Validation – Nelson Siegel (NS) And Nelson Siegel Svensson (NSS) Models
Model Parameters – Level (ß0) | Slope (ß1) | Curvature (ß2, ß3, ß4) | Scale (τ1, τ2) | 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)
Complete: Interview Guide Question(s)
Week 9: Modeling Short-Rate And Interest Rate Factors
Welcome to Week 9
Watch: Modeling Interest Rates – Vasicek Model (1977)
Model Parameters – Long-Term Mean | Mean Reversion Speed | Volatility | Time Change | Model Calibration Process – Interest Rate Shocks | Stochastic Equation – Drift | Random Volatility | Model Error – Actual vs. Model | Maximum Likelihood Estimator | Model Performance Metrics – Mean Absolute Error (MAE) | Mean Squared Error (MSE) | Root Mean Squared Error (RMSE) | Simulated Rates
Watch: 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
Watch: Principal Component Analysis (PCA) – 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
Complete: Interview Guide Question(s)
Week 10, 11: Modeling Volatilities, Vol Skew And Surfaces
We'll focus on analyzing time-series data and mastering the art of modeling volatilities. Time-series analysis is a powerful tool for understanding the dynamics of financial markets, and I'm here to guide you through it.
We begin by exploring time-series modeling of equity price and returns, introducing concepts such as moving average (MA) models and their variations. You'll gain a nuanced understanding of the strengths, limitations, and real-world applications of these models.
Our journey continues with an in-depth look at the standard deviation as a measure of historical volatility. We'll examine different types of volatility, including normal, downside, and annualized volatility. You'll also engage in a practical project comparing the effectiveness of simple and exponential moving averages in analyzing equity risk. As we progress, you'll encounter advanced topics such as the Exponential Weighted Moving Average (EWMA) model, parameter estimation using Maximum Likelihood Estimator (MLE), and the powerful GARCH model for modeling volatilities.
Watch: Time-Series Modeling of Equity Price And Returns
Time-Series Data | Moving Average (MA) Models - Simple Moving Average (SMA) Model | Exponential Moving Average (EMA) Model | Short-Term vs. Long-Term Moving Average | Simple vs. Exponential Moving Average - Behaviour | Relation | Limitations
Watch: Modeling Volatilities – Standard Deviation
Historical/Realized Volatility - Standard Deviation - Normal | Downside | Annualized Volatility | Simple Moving Average (SMA) Model
Project: Applied Time-Series Models for Equity Risk – Simple vs. Exponential
Watch: Exponential Weighted Moving Average (EWMA) Model
Time Series Modeling | Volatility Clustering | Model Features – Innovation | Persistence | Conditional Volatility | Parameter Estimation
Watch: Estimating Parameters – Maximum Likelihood Estimator (MLE)
Model Fitting | Likelihood Function | Probability Distributions
Watch: Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Model
Time Series Modeling | Volatility Clustering | Model Features – Innovation | Persistence | Long-Term Mean Reversion | Conditional Volatility | Parameter Estimation
Watch: Modeling Vol Skew And Surfaces
...
Week 12, 13: Stochastic Processes And Simulations
Welcome to Week 12
Watch: Simulations – Historical Simulation Method – Point Estimation – EQ
Introduction | Simulation Process | Underlying Variable/Factor - Properties/Behaviour | Point Estimation Technique | Return Expectations | Generating Simulated Prices & Simulated Paths | Price Estimates | Limitations
Watch: Simulations – Historical Simulation Method – Path Estimation – EQ
Introduction | Simulation Process | Underlying Variable/Factor - Properties/Behaviour | Path Estimation Technique | Return Expectations | Generating Simulated Prices & Simulated Paths | Price Estimates | Limitations
Watch: Simulations – Monte-Carlo Simulation Method – EQ
Introduction | Simulation Process - Drift And Volatility | Path Estimation Technique | Return Expectations | Generating Simulated Prices & Simulated Paths | Price Estimates | Limitations
Week 14, 15, 16: Pricing And Valuation of Fixed-Income Securities
Welcome to Week 14, You'll tackle the Pricing and Valuation of Fixed-Income Securities, focusing on US Treasury securities. You'll learn how to figure out the worth of fixed-income securities, starting with US Treasury Bills. We'll use a method called the Discounted Cash Flow (DCF) model to crunch numbers such as discount rates and present values.
We'll move on to more to analyzing longer-dated Treasury securities and understanding how their value changes over time by mark-to-market securities and recording profit/loss. We'll also look at how to compare our calculated prices with the actual market prices and analyze the price difference due to model errors. Along the way, You'll do a practical project to sharpen analytical skills in valuing US Treasury Securities.
Watch: Full Valuation DCF Model – US Treasury Bills
Market Data - Discount Rates | Fixed-Income Product - 26W Treasury Bill | Discount Factor | Discounting Cashflow Equation | Model Price vs. Issue Price | Discount Basis Yield | Effective Yield | Money Market Yield | Bond Equivalent Yield | Effective Annualized Yield | Valuation Report
Watch: Interest Rate Movement And Mark-to-Market PnL
Mark-to-Market Price And PnL | Constant Rate Simulation | Incremental PnL – Interest Rate Movement vs. Pull-to-Par Effect | Price-Yield Relationship | Analysis Report And Commentary
Watch: Interest Rate Scenario Analysis And Sensitivities
Shock-Adjusted Ladder Using Delta And DV01 Sensitivities | Scenario And Shock Ladder | Partial Revaluation – Delta And Dv01 | Scenario PnL | Approximation Error | Risk Attribution - Interest Rate Delta & Incremental PnL (Residual)
Watch: Partial Revaluation Sensi-Based Model – First-Order And Higher-Order
Understanding Full Valuation vs. Partial Revaluation Methodologies | Sensitivities - DV01, Convexity | Basis Point Value | Interest Rate Shock | First-Order, Second-Order PnL, And Residual PnL | Risk Attribution | Valuation Report | Interview Questions
Watch: Full Valuation DCF Model – US Treasury Notes/Bonds
Market Data - 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 Price | Model Price Difference | Valuation Report
Watch: Full Valuation DCF Model – US Treasury Notes/Bonds – Mark-to-Market
Mark-to-Market Price And PnL | Accrued Interest | US Treasury Interest Rate Curve Analysis – Valuation Date vs. Issue Date | Model Price Difference – Model Price vs. Market Price | Model Limitations | Valuation Report
Watch: Partial Revaluation Sensi-Based Model – US Treasury Bonds
Sensitivities - DV01, Convexity | Basis Point Value And Aggregation | Interest Rate Curve Shock | First-Order, Second-Order PnL, And Residual PnL | Risk And PnL Attribution | Valuation Report
Project: Valuation Report of US Treasury Securities And Mark-to-Market
We're thrilled to introduce our fixed-income pricing and valuation project, the "Valuation Report of US Treasury Securities And MTM" – A unique opportunity for participants to deepen their understanding with respect to the pricing and valuation of fixed-income securities, impact of change in market risk factors on portfolio performance, and market commentaries.
Complete: Interview Guide Question(s)
Week 17, 18: Scenario Analysis And Stress Testing of Fixed-Income Portfolios
Welcome to Week 17, our focus is squarely on understanding scenario analysis and stress testing of fixed-income portfolios. It's all about equipping ourselves with the knowledge needed to effectively navigate through various market conditions.
We'll delve deep into different market scenarios and their impact on fixed-income portfolios. From interest rate fluctuations to market volatility, we'll explore how different scenarios can affect portfolio performance and risk management strategies. through a combination of theoretical learning and hands-on project, We'll develop a comprehensive understanding of how to analyze and mitigate risks in fixed-income portfolios.
Watch: Interest Rate Scenario Analysis And Sensitivities – IR Delta And DV01
Shock-Adjusted Ladder Using Delta And DV01 Sensitivities | Scenario And Shock Ladder | Partial Revaluation – Delta And Dv01 | Scenario PnL | Approximation Error | PnL Attribution - Interest Rate Delta & Incremental PnL (Residual)
Watch: Partial Revaluation Sensitivity-Based Model – First-Order
Understanding Full Valuation vs. Partial Revaluation Methodologies | DV01 Sensi And Interest Rate Shock | First-Order PnL And Residual PnL | Valuation Report | Interview Questions
Watch: Sensitivity-Based Risk And PnL Attribution – Fixed-Income Securities
Partial Revaluation vs. Full Revaluation DCF Methodologies | Risk Sensitivities – DV01 | Convexity | First And Second-Order PnL | Residual PnL | Methodology Difference – Full Valuation vs. First And Second-Order PnL | Model Limitations | Market Risk Report
Watch: Market Interest Rate Scenario – Parallel Shifts
Interest Rate Scenario Shock Definition – Parallel Shift Up, Parallel Shift Down | EOD vs. Scenario Present Value | Scenario PnL | Scenario Spot Ladder | Bond Price-Yield Relationship | Bond Convexity | Market Risk Scenario Report
Watch: Market Interest Rate Scenario – Non-Parallel Shifts
Interest Rate Scenario Definition – Bull & Bear Steepening, Bull & Bear Flattening | Stress Test Scenario Definition – 2023 Exploratory Market Shock Component Scenario, 2022 Severely Adverse Scenario | EOD vs. Scenario Present Value | Scenario PnL | Market Risk Stress Test Report
Week 19, 20, 21, 22: Value-at-Risk Methodologies And Advancements
Welcome to Week 19, our focus for these weeks is on Value-at-Risk (VaR) methodologies and the advancements in this essential risk management tool. We'll begin by understanding the fundamental concepts of VaR and then dive into various calculation methods and their applications for different financial instruments and portfolios.
You'll learn about the importance of backtesting and validating VaR models to ensure their accuracy and reliability. Understand the processes and metrics used in model validation and how VaR models are used to determine capital requirements, ensuring that financial institutions maintain adequate capital to cover potential losses. Through a combination of practical learning and projects, we will develop a comprehensive understanding of how to calculate and apply VaR measures.
Watch: Introduction to Value-at-Risk (VaR) Measure
Introduction to VaR And Basic Concepts | VaR Calculation Methods - Historical Simulation, Parametric, Monte-Carlo Simulation | VaR Limitations | Advancements And Improvements - Conditional VaR/Expected Shortfall, Stress Testing And Scenario Analysis, Stressed VaR, Incremental VaR, Marginal VaR, VaR under Different Distributions, Backtesting And Model Validation, Liquidity-Adjusted VaR
Watch: Historical Simulation VaR – Equities And Equity Portfolio
Introduction to Historical Simulation VaR And Process | Historical Time-Series Risk Factors' Data | Risk Factor's Shocks | Historical Simulation | Scenario Generation | Profit And Loss Determination - Individual Assets, Portfolio | Methodology | Absolute And Relative VaR | Risk Attribution to Market Risk Factor(s) And Specific Factors - Total Market Risk, General Market Risk, Equity Specific Risk
Watch: Historical Simulation VaR – Interest Rate Bonds
Introduction to Historical Simulation VaR And Process | Risk Factor Determination | Historical Time-Series Risk Factors' Data | Risk Factor's Shocks | Historical Simulation | Scenario Generation | Profit And Loss Determination | Methodology | Absolute And Relative VaR | Market Risk Results
Project: Market Value-at-Risk Report for US Treasury Securities and Portfolio
We're thrilled to introduce our latest fixed-income risk measurement project, the "Market Value-at-Risk Report for US Treasury Securities and Portfolio" – A unique opportunity for participants to deepen their understanding of risk calculations on fixed-income securities, the impact of change in market risk factors on portfolio risk, and insightful market risk commentaries.
This roadmap is designed to guide you step-by-step, but your dedication will determine how much you gain from the program. here are a few tips to make the most of it:
Allocate regular time each week to review content, complete assignments, and explore additional resources.
Join discussions, ask questions, and participate in any live sessions.
The hands-on exercises are not just supplementary; they are essential to mastering the concepts.
If you’re stuck, don’t hesitate to ask questions or seek clarification.
You’ll not only have a strong theoretical understanding but also the practical hands-on experience to apply these concepts in the real world.
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