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 have taken a significant step toward building or advancing your career in market risk.
The QMRM program is designed to provide structured learning and hands-on experience, combining rigorous theoretical foundations with real-world applications. Each module is carefully curated to build a deep, layered understanding—from core financial concepts to advanced risk measurement tactics—ensuring that you develop both the analytical precision and practical expertise required in today's financial markets.
We encourage you to fully immerse yourself—participate actively, ask questions, and be ready to apply your knowledge and expertise to tackle real-world challenges.
Basic Modules (1 to 5)
The foundational modules are designed to provide you with a strong foundational understanding of financial markets, financial products and derivative instruments, and essential data-handling techniques, ensuring a solid start and that you develop a comprehensive understanding of equities, interest rates, foreign exchange, commodities, financial derivatives, and market data automation.
Module 1: Equities and Modeling Systematic Risk (6.4 hrs)
Equities form a core component of financial markets, and understanding their risks is fundamental for market risk management. This module introduces equity market data, return calculations, and risk modeling techniques, covering both systematic and unsystematic risk factors.
Market Data for Equities: Extract, process, and analyze historical equity time-series data, including price movements and returns.
Equity Returns and Shocks: Understand absolute, discrete, and continuous return calculation to quantify equity price movements.
Measuring Equity Risk: Assess volatility using standard deviation, correlation, and covariance to understand portfolio diversification benefits.
Extreme Risk Analysis: Apply Extreme Value Theory (EVT) and techniques such as Block Maxima and Peaks-Over-Threshold (POT) to model tail risks in financial markets.
Hands-On Application: Develop Python-based models to compute equity risk metrics, visualize return distributions, and analyze historical market shocks.
Module 2: Interest Rates and Monitoring Yield Spreads (9.3 hrs)
Interest rates influence financial markets, corporate financing, and investment decisions. This module covers yield curves, interest rate shocks, and the impact of changing interest rates on market risk.
Fixed-Income Markets: Gain a structured understanding of US Treasury securities, bonds, and interest rate instruments.
Yield Curve Interpretation: Analyze the normal, inverted, and humped yield curves, and their implications on macroeconomic conditions.
Interest Rate Shocks: Compute absolute and relative interest rate changes to measure their impact on portfolio valuation.
Market Monitoring and Reporting: Track and analyze US Treasury yield spread, particularly the 10Y-3M spread, as a key indicator of economic cycles.
Hands-On Application: Build automated market reports that monitor yield spread and SnP 500 equity market performance, using Python to extract, process, visualize, and generate reports.
Module 3: Market Data Management and Automation (7.8 hrs)
Efficient market risk management relies on high-quality market data across multiple asset classes. This module introduces financial data extraction, automation, and real-time monitoring techniques.
Multi-Asset Market Data: Learn to extract and manage data for equities, interest rates, currencies, and commodities, and derivatives.
Understanding Derivative Instruments: Explore futures, forwards, and options, including their price structures and risk profiles.
Python Automated for Market Data: Write Python scripts to automate data extraction, storage, and processing for financial instruments.
Crypto Market Data: Extract and analyze historical data for cryptocurrencies and understand their unique market characteristics.
Hands-On Application: Implement Python-based automation to streamline market data workflows, visualize price trends, and create dynamic dashboards for risk monitoring.
Modules 4 and 5: Additional topics are planned to be included soon!
Interview Guide: Financial Instruments, Market Data Management, and Automation
To reinforce learning, participants will have access to a structured interview guide covering essential concepts on financial instruments, market data management, and automation.
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. These modules provide quantitative techniques for risk assessment, pricing models, and help in risk management decisions.
Module 6: Descriptive and Inferential Statistics and Probability Distributions (7.1 hrs)
A strong foundation in statistical methods and probability distributions is critical for market risk modeling. This module introduces statistical measures, correlation analysis, and probability distributions that underpin risk quantification in financial markets.
Introduction to Basic Statistics: Understanding the statistical properties of financial data is essential for analyzing risk, covering descriptive statistics, variance measures, and correlation techniques used in financial analysis.
Descriptive Statistics – Univariate Analysis: Explore measures of central tendency and dispersion to understand data distributions.
Understanding Standard Deviation: A crucial risk measure used to quantify asset price volatility.
Descriptive Statistics – Bivariate Analysis: Examine relationships between two financial variables using statistical techniques.
Covariance and Correlation: Differentiate between covariance and correlation, and understand their role in portfolio diversification and market risk assessment.
Hands-On Application: Compute statistical measures using real market data, analyze historical returns, and assess correlations between different asset classes.
Probability distributions form the foundation of risk modeling and stochastic processes. This module also focuses on normal and log-normal distributions, essentially for pricing models and financial simulations.
Normal Distribution: Understand. the Gaussian distribution, probability density function (PDF), cumulative distribution function (CDF), percent point function (PPF), and their applications in VaR modeling and Black-Scholes pricing.
Log-Normal Distribution: Explore how log-normal distributions are used in modeling stock price movements, option pricing, and risk management. Understand the transformation of a normal to log-normal distribution and log-normal to normal distribution.
Hands-On Application: Simulate asset returns and prices using normal and log-normal distributions, respectively, evaluate risk measures, and apply these concepts to real-world financial scenarios.
Module 7: Modeling Term-Structure of Interest Rates (10.4 hrs)
Understanding the term structure of interest rates is crucial for pricing fixed-income securities, managing interest rate risk, and constructing yield curves for scenario creation. This module introduces interpolation techniques used to construct smooth and continuous yield curves, regression models for yield curve estimation (including linear and polynomial regressions), and advanced factor-based models such as the Nelson-Seigel and Nelson-Seigel-Svensson models needed for accurate interest rate modeling and forecasting.
Yield Curve Construction – Interpolation Methods: Learn basic interpolation techniques such as linear interpolation, polynomial fitting, and piecewise interpolation to estimate missing interest rate data.
Advanced Interpolation Methods:
Vandermonde Matrix: Used Understand how the Vandermonde matrix approach is used for higher-order polynomial fitting in yield curve estimation.
Newton Divided Difference: Explore Newton's divided difference technique, which improves curve fitting accuracy by considering successive rate differences.
Lagrange and Cubic Spline Interpolation: Compare Lagrange polynomial interpolation and cubic spline methods, which offer more flexible and smooth curve fitting for complex yield structures.
Modeling Yield Curve:
Linear Regression Model (Single Factor): Apply simple linear regression techniques to estimate interest rate relationships and predict yield curve movements.
Polynomial Refression Model (Single Factor): Extend the regression framework to quadratic and cubic polynomial models, capturing non-linear interest rate dynamics.
Nelson-Seigel (NS) and Nelson-Seigel-Svensonn (NSS) Models: Explore econometric models that describe interest rate curve movements using parameters for level, slope, and curvature.
Model Validation – NS and NSS Models: Evaluate model performance using error metrics such as RMSE, MSAR, and R², ensuring accuracy in interest rate curve fitting.
Hands-On Application: Implement yield curve modeling techniques, calibrate model parametrics using real-world market data, and validate their predictive accuracy.
Module 8: Modeling Short-Rate and Interest Rate Factors (8.6 hrs)
Interest rates are. fundamental driver of financial portfolios, influencing bond pricing, derivatives valuation, and risk management strategies. This module introduces short-rate models and principal component analysis (PCA)—key techniques for modeling interest rate dynamics (level, slope, and curvature) and understanding market risk factors.
Vasicek Model: A mean-reverting stochastic process used for modeling interest rate movements. Learn how the model estimates yield curves, bond pricing, and risk factors under different market conditions.
Cox-Ingersoll-Ross (CIR) Model: An extension of the Vesicel model that ensures interest rates remain non-negative. This model is widely used for interest rate derivative pricing and risk modeling.
Hands-On Application: Implement and calibrate Vasicek and CIR models using historical interest rate data, simulate interest rate paths, and compare model accuracy in forecasting yield curve dynamics.
Introduction to PCA and Preliminaries: Learn the mathematical foundations of PCA, including variance-covariance matrics, eigenvalues, and eigenvectors, to identify key risk factors.
PCA for Interest Rate Risk Modeling: Decompose yield curve movements into primary components—level, slope, and curvature—to quantify how different maturities respond to interest rate shocks.
PCA – The Reduced Model in Perspective: Understand how PCA simplifies risk analysis by reducing dimensionality while preserving essential information about market dynamics.
Hands-On Application: Perform PCA on yield curve data, analyze historical interest rate movements, and use PCA-based shock modeling to simulate interest rate stress scenarios.
Module 9: Modeling Volatilities, Volatility Skew, and Surfaces (12.6 hrs)
Volatility is a crucial risk metric in financial markets, affecting asset pricing, portfolio risk assessment, and derivative valuation. This module explores historical volatility modeling, including moving averages, advanced time-series techniques including exponential weighted methods, autoregressive models, and the construction of volatility surfaces for equity options.
Time-Series Modeling: Moving Average Models for Equity Prices and Returns: Learn how simple and exponential moving averages (SMA and EMA) are used to smooth price and return series, capturing market trends.
Standard Deviation and Downside Standard Deviation as Volatility Measures: Compare historical volatility calculations using standard deviation and downside risk measures to account for negative shocks.
Exponential Weighted Moving Average (EWMA) Model: Apply EWMA for volatility estimation, emphasizing recent price movements over historical ones to better reflect changing market conditions.
Maximum Likelihood Estimation (MLE) for Parameter Estimation: Learn how MLE is used to estimate volatility model parameters, optimizing statistical fits to market data.
Generalized Autoregressive COnditional Heteroskedasticity (GARCH) Models: Explore GARCG(1,1) models, a cornerstone of financial volatility modeling, capturing volatility clustering and persistence in asset returns.
Hands-On Application: Implement EWMA and GARCG models to estimate volatility, analyze historical market shocks, and compare forecasting accuracy for risk management and pricing derivatives.
Market Participants often observe non-uniform volatility levels across strike prices and maturities—a phenomenon known as volatility skew and surface formation. This module also covers volatility smile effects, implied volatility modeling, and trading strategies based on volatility skew.
Volatility Skrew and Surface Construction for Equity Options: Understand why implied volatility varies across strikes and how it impacts option pricing and hedging strategies.
Implied Volatility and Skew – Call-Put Implied Volatility Spread Trading: Explore trading strategies that exploit volatility mispricing, including risk reversal and volatility arbitrage.
Hands-On Application: Construct volatility skew and surfaces using options market data, analyze implied volatility skews, and develop trading strategies based on volatility spreads.
Module 10: Stochastic Processes and Simulations (9.2 hrs)
Stochastic processes are essential for modeling asset price dynamics, risk management, and pricing derivatives. This module introduces non-parametric (historical simulation technique—widely used in Value-at-Risk calculations and portfolio stress testing) and parametric (Monte-Carlo Simulation—widely used for pricing derivatives and estimating risk) simulation methods, equipping learners with the ability to forecast risk exposures and assess potential market outcomes.
Introduction to Stochastic Processes and Simulations for Equities: Understand how stochastic models describe random price movements in equity markets.
Historical Simulation Method – Point and Path Estimation Techniques for EQuities: Explore how historical price data is used to simulate future price paths and assess risk scenarios. Learn how single-point estimates are generated using past market data to assess potential losses and extend the simulation to multiple price paths, capturing a range of potential future scenarios.
Hands-On Application: Implement historical simulation techniques to model equity market fluctuations, asses portfolio risk under different stress conditions, and compare simulated outcomes to actual market movements.
Monte-Carlo Simulation Method for Equities: Learn how to generate thousands of potential price paths using stochastic differential equations, incorporating drift and volatility factors.
Monte-Carlo Simulation Integrated with Vasicek and CIR Models for Interest Rates: Apply monte-carlo methods to simulate interest rate paths, integrating the Vasicek and Cox-Ingersoll-Ross (CIR) models for yield curve forecasting and fixed-income risk assessment.
Monte Carlo Simulation with SDEs: Implement simulations using SDEs for pricing complex financial instruments and evaluating portfolio performance under stress scenarios.
Hands-On Application:
Develop Monte-Carlo simulations to model stock price and interest rate movements, simulate fixed-income portfolio risk, and compare the performance of historical vs. parametric sumulations in market risk analysis.
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 of Market Risk (11–15)
The core phase focuses on integrating your knowledge into advanced pricing, valuation, and risk management methodologies.
Module 11: Pricing and Valuation of Fixed-Income Securities (14.2 hrs)
Fixed-income securities are the most traded and concentrated in the financial markets. This module provides a comprehensive framework for pricing and valuing bonds and interest rate swaps, covering discounting cashflow (DCF) modeling, interest rate sensitivities, and scenario analysis techniques.
Full Valuation DCF Model for US Treasury Bills, Interest Rate Movements, and Mark-to-Market PnL: Learn to price short-term fixed-income securities using the DCF method, factoring in risk-free rates and discount yields. Analyze how changes in interest rates affect bond prices and track market-to-market profit and loss (PnL).
Interest Rate Sceanrio Analysis and Sensitivies – Duration, DV01, Convexity, and Residual: Compute bond price sensitivity (Delta) and dollar value of a basis point (DV01) to asses risk exposures. Extend risk analysis to include convexity adjustments, improving the accuracy in pricing fixed-income instruments.
Partial Revaluation Sensitivity-Based Model – First-Order, Higher-Order Approximation, and PnL Attribution: Implement duration and DV01-based approximations to estimate interest rate risk with greater efficiency. Implement second-order risk effects and PnL attribution methodologies to break down portfolio performance.
Full Valuation DCF Model – US Treasury Notes/Bonds – Mark-to-Market Adjustments: Understand pricing discrepancies between model estimates and market prices, and analyze their implications.
Partial Revaluation Sensitivity-Based Model – Duration and Convexity (DC) Approach: Use duration and convexity measures to estimate bond price changes under different interest rate scenarios.
Bond Cash Flow Mapping Procedure – Nearest Tenor Matching and Variance Matching Approaches: Understand techniques for mapping bond cash flows to yield curve tenors, crucial for risk management and portfolio optimization.
Hands-On Application:
Valuation Report of US Treasury Securities and Mark-to-Market: Construct a comprehensive valuation report, summarizing bond pricing methodologies and market risk assessments—conduct interest rate risk assessments and prepare mark-to-market valuation reports using real market data.
Interest rate swaps and options (swaptions) play a crucial role in hedging risk, managing yield curve exposures, and structuring fixed-income portfolios. This module also covers swap pricing models and option-based valuation approaches.
Pricing Interest Rate Swaps (IRS) – Fixed vs. Floating Leg Valuation: Understand the mechanics of interest rate swaps, including cashflow calculations, swap curves, and discounting methodologies.
Forward Rate Agreements (FRAs) and Swap Rate Determination: Learn how FRAs are used to lock in future interest rates and how swap rates are derived from the yield curve.
Swaptions – Pricing Interest Rate Options: Introduce Black’s model for pricing swaptions, using volatility surfaces and forward rate dynamics.
Risk Sensitivities of Interest Rate Swaps and Swaptions: Compute Delta, Gamma, and Vega exposures for swaps and swaptions to assess their risk in a portfolio.
Impact of Interest Rate Shocks on Swaps and Swaption Portfolios: Perform scenario analysis to measure the impact of yield curve shifts on swap positions and option valuations.
Hands-On Applications: Implement pricing models for interest rate swaps and swaptions, calibrate Black’s model for swaption pricing, and develop risk reports for swap exposures.
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.
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 | Solving System of Linear Equations | Determinant | Matrix Inversion | 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
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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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