hey there, Professional!
Here's what I've set out for you. Below is a step-by-step guide detailing what we'll tackle each week. In each module, each topic has been chosen to give you the skills and the insights you need. But remember, while this roadmap will guide you, your curiosity will drive you. So, ask questions, be hands-on, and embrace every victory moment.
{ ... } 10 Months Program
Day 1: Setting the Foundation
It's about setting up your development environment. You'll familiarize yourself with Anaconda Navigator, which acts as a central tool for managing your data science tools, and Jupyter Notebook, a powerful interactive tool for coding, visualization, and presenting your Python projects. Additionally, we'll delve into the seamless integration with Excel, harnessing its analytical capabilities to augment our data handling and visualization. By the end of the day, you'll have your tools in place and be ready to start your journey.
Installation Guide: Get Started with Anaconda Navigator: Installation
Watch: Anaconda Navigator Application
Everything You Need To Get Started On Your Machine | Installation Process | Integrated Development Environment vs. Code Editor vs. Compiler Learnings | Python Libraries & Packages | Recommendations.
Watch: Jupyter Notebook
Open-Source Web-Based Interactive Computing Platform Launching Application | Default Directories | Creating a New Jupyter Notebook | Menu Options & Toolbar | Keyboard Shortcuts | Code Cell.
To download and install Microsoft Excel, head to Microsoft's official site. Please be aware that a valid license or subscription may be required to access and use Excel fully.
Read: Interview Guide Question(s)
What is an Integrated Development Environment (IDE) and a Python Code Editor? and highlight the primary differences between the two.
Week 1: Introduction to Financial Markets, Products, and Instruments
In this week, we dive deep into the world of finance. 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 to forex, 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 the end of this week, you'll have a foundational understanding of the landscape of financial markets and the products that drive them.
Watch: Introduction to Financial Markets
Understanding: Financial Markets - Capital Market - Primary | Secondary - Equity | Debt | Forex / Currency Market | Commodity Market | Derivatives Market - Exchange vs. Over-The-Counter Traded.
Watch: Introduction to Financial Products and Instruments – Equities
Understanding: Financial Products - Securities - Stocks | Debt | Loans | Deposits | Stock Market - Primary | Secondary - Order Book | Private Placement | Equities - Capital Appreciation | Dividend | Reinvestment (DRIP)
Read: Balancing Equity Risk and Reward: Tradeoff
Read: Forward Contracts vs. Futures Contracts: What's the Difference?
Watch: Introduction to Financial Products and Instruments – FI Securities
Understanding: Financial Products - Debt Securities | Issuers - Government | Agencies | Municipals | Corporates | Treasury Securities - T-Bills | T-Notes | T-Bonds | TIPS | Interest Rate & Term-Structure of Interest Rates - Short-Term | Medium-Term | Long-Term
Read: Understanding Fixed-Income Treasury Securities
Week 2: Equities | Modeling Systematic Risk
It is all about equities, and trust me, it's an area that's as thrilling as it is vital. Equities are basically your stake in a company. While they offer exciting opportunities, they come with their fair share of ups and downs. Now, to really get a grip on equities, we need to understand the risks involved. There are two main types: systematic and unsystematic risks. Think of these as the big-picture risks that affect all investments and the specific risks unique to individual investments, respectively.
History has shown us that various events can shake up the equity markets, changing the game for investors. We'll take a closer look at these, drawing lessons from the past to better navigate the future.
We'll also dig into some slightly advanced topics. Ever heard of Block Maxima or Extreme Value Theory? By the end of the week, you'll be familiar with these and understand their significance in assessing risks. By the time we wrap up this week, you'll have a solid grasp on equity risks and the tools to analyze them.
Watch: Historical Time Series Data & Equity Shocks – Excel | Python
Absolute Returns/Shocks | Proportional/Relative Shocks - Discrete | ShockType Use | Comparison
Watch: Equity Risk – Systematic & Unsystematic
Risk Measures - Variance | Standard Deviation | Covariance | Correlation | Beta | Systematic (Market) Risk | Unsystematic (Ideo) Risk | Downside Deviation | Annualized Risk-Return Profile - Expected Return | Risk
Read: The Basics of Standard Deviation: A Simple Guide
Read: Covariance and Correlation: From Diversification to Standardization
Watch: Equity Risk – Extreme Value Theory (EVT)
Return Distribution | Cumulative Distribution Function (CDF) | Tail Distribution - Left Tail | Right Tail | Extreme Outcomes | Probability Distribution - Normal | Exponential | Parameters | Regulators' Standpoint | Tail Risk
Watch: Equity Risk – Block Maxima & Peaks-Over-Threshold (POT)
Read: Block Maxima and Extreme Value Theory in Finance
Read: Interview Guide Question(s)
Explain the difference between logarithmic returns and the natural logarithm of stock prices in finance.
Week 3 and 4: Interest Rates | Monitoring Yield Spreads
In this week, 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.
Watch: Historical Time Series Data & Interest Rate Shocks – Excel | Python
Absolute Returns/Shocks | Proportional/Relative Shocks - Discrete | Continuous | Profile of Interest Rates – 10Y & 3M | Variability Profile | YC Profile – Current Rates & Shocks
Watch: US Treasury Rates & 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
Watch: US Treasury Yield Spread
Treasury Yield Spread - 10Y3M Spread | Yield Spread Table | Interpretation & Identification of Yield Curve Profile & Inversions
Project: Monthly Market Report: Monitoring Yield Spreads and S&P 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.
Watch: Market Report: 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
Watch: Market Report: 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
Watch: S&P 500 Performance: Cumulative Loss & Maximum Drawdown
Performance Measures - Growth Index | Cumulative Losses | Maximum Drawdown | Period - 1990 to Present | Generate Consolidated Market Report
Week 5 and 6: Modeling Term-Structure of Interest Rates
Welcome to Week 4, 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 & 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) & 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) & 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)
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 7: Analyzing Time-Series & Modeling Volatilities
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 & 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: Modeling Volatilities – 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: Modeling Volatilities – GARCH Model
Time Series Modeling | Volatility Clustering | Model Features – Innovation | Persistence | Long-Term Mean Reversion | Conditional Volatility | Parameter Estimation
Read: Interview Guide Question(s)
What are the drawbacks of using the Simple Moving Average (EMA) Model?
What is Exponential Moving Average (EMA)? and where can it be applied?
How is the Exponential Moving Average (EMA) different from the Simple Moving Average (SMA)?
What are the drawbacks of using the Exponential Moving Average (EMA) Model?
Week 8: Stochastic Processes & Simulations
Watch: Simulations – Historical Simulation Method – EQ
Introduction | Simulation Process | Underlying Variable/Factor - Properties/Behaviour | Estimation Techniques - Point & Path Estimation | Return Expectations | Generating Simulated Prices & Simulated Paths | Price Estimates | Limitations
Week 9 and 10: Pricing And Valuation of Fixed-Income Securities
Welcome to Weeks 9 and 10, 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: 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
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 11: Scenario Analysis And Stress Testing of Fixed-Income Portfolios
Welcome to Week 11, 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: 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
Watch: Sensitivity-Based Risk And PnL Attribution – Fixed-Income Securities
Partial Revaluation vs. Full Revaluation DCF Methodologies | Taylor-Series Approximation | Risk Sensitivities – Duration | DV01 | Convexity | Duration-Convexity PnL | Residual PnL | Methodology Difference – Full Valuation vs. Duration-Convexity PnL | Model Limitations | Market Risk Report
Here's to your success and the exciting path ahead! happy learning, Professional!