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.

**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 â€“ 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 â€“ 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 â€“ 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 ShocksÂ â€“Â Excel | Python

Absolute Returns/Shocks | Proportional/Relative Shocks - Discrete | ShockType Use | Comparison

Watch: Equity Risk â€“ 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 â€“ 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 â€“ Block Maxima & Peaks-Over-Threshold (POT)

Read: Block Maxima and Extreme Value Theory 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 ShocksÂ â€“Â Excel | Python

Absolute Returns/Shocks | Proportional/Relative Shocks - Discrete | Continuous | Profile of Interest Rates â€“ 10Y & 3M | Variability Profile | YC Profile â€“ 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 â€“ Interpolation Methods

Yield Curve Construction - Interpolation Methods - Linear | Polynomial | Higher-Order Polynomials - Quadratic | Cubic | Quartic | Day Count Convention - 30/360

Watch: Advanced Interpolation Methods â€“ Vandermonde Matrix

Yield Curve Construction - Interpolation Methods - Vandermonde Matrix | System of Linear Equations | Determinant | Coefficients | Curve Fitting | Limitations

Watch: Advanced Interpolation Methods â€“ 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 â€“ Lagrange & Cubic Spline Interpolation

Yield Curve Construction - Interpolation Methods - Lagrange & Cubic Spline | Coefficients | Curve Fitting | Limitations

Watch: Modeling Yield Curve â€“ 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 â€“ 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 â€“ 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 ValidationÂ â€“ Nelson Siegel (NS) & Nelson Siegel Svensson (NSS) Models

Model Parameters â€“ Level (ÃŸ0) | Slope (ÃŸ1) | Curvature (ÃŸ2, ÃŸ3, ÃŸ4) | Scale (Ï„1, Ï„2) | Evaluation Metrics â€“ 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 (RÂ²)

Watch: Modeling Interest Rate Risk FactorsÂ â€“ Principal Component Analysis (PCA)

Statistics â€“ Variance | Covariance-Correlation Matrix | Normalization | Principal Component Identification â€“ Level | Slope | Curvature | Eigen Decomposition â€“ Values & Vectors | Dimensionality Reduction | PC Computation & Uncorrelated Shocks

Watch: Principal Component Analysis (PCA) â€“Â 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 â€“ Standard Deviation

Historical/Realized Volatility - Standard Deviation - Normal | Downside | Annualized Volatility | Simple Moving Average (SMA) Model

Project: Applied Time-Series Models for Equity Risk â€“ Simple vs. Exponential

Watch: Modeling Volatilities â€“ Exponential Weighted Moving Average (EWMA) Model

Time Series Modeling | Volatility Clustering | Model Features â€“ Innovation | Persistence | Conditional Volatility | Parameter Estimation

Watch: Estimating Parameters â€“ Maximum Likelihood Estimator (MLE)

Model Fitting | Likelihood Function | Probability Distributions

Watch: Modeling Volatilities â€“ GARCH Model

Time Series Modeling | Volatility Clustering | Model Features â€“ Innovation | Persistence | Long-Term Mean Reversion | Conditional Volatility | Parameter Estimation

**Week 8: Stochastic Processes & Simulations**

Watch: Simulations â€“ Historical Simulation Method â€“ Point Estimation â€“Â EQ

Introduction | Simulation Process | Underlying Variable/Factor - Properties/Behaviour | Point Estimation Technique | Return Expectations | Generating Simulated Prices & Simulated Paths | Price Estimates | Limitations

Watch: Simulations â€“ Historical Simulation Method â€“ Path Estimation â€“Â EQ

Introduction | Simulation Process | Underlying Variable/Factor - Properties/Behaviour | Path Estimation Technique | Return Expectations | Generating Simulated Prices & Simulated Paths | Price Estimates | Limitations

Watch: Normal Probability Distribution

Standard Normal Probability 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 Probability Distribution | Random Variates/Sample | Probability Distribution | Cumulative Probability Distribution | PDF | CDF | PPF | Normal vs. Log-Normal Distribution | Transformation | Parameters â€“ Mean | Standard Deviation | Skewness | Kurtosis

Watch: Simulations â€“ Monte-Carlo Simulation Method â€“Â EQ

Introduction | Simulation Process - Drift And Volatility | Path Estimation Technique | 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 â€“ 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 â€“Â Interest Rate Movement vs. Pull-to-Par Effect | Price-Yield Relationship | Analysis Report And Commentary

Watch: Full Valuation DCF Model â€“ 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 â€“ US Treasury Notes/Bonds â€“ Mark-to-Market

Mark-to-Market Price And PnL | Accrued Interest | US Treasury Interest Rate Curve Analysis â€“ Valuation Date vs. Issue Date | Model Price Difference â€“ 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" â€“ 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 â€“ Parallel Shifts

Interest Rate Scenario Shock Definition â€“Â 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 â€“ Non-Parallel Shifts

Interest Rate Scenario Definition â€“ Bull & Bear Steepening, Bull & Bear Flattening | Stress Test Scenario Definition â€“ 2023 Exploratory Market Shock Component Scenario, 2022 Severely Adverse Scenario | EOD vs. Scenario Present Value | Scenario PnL | Market Risk Stress Test Report

Watch: FED2024 Stress Test Scenarios

Interest Rate Scenario Definitions | Identified Domestic And International Variables | Stress Testing Process And Timelines | FED 2024 vs 2023 Scenario Comparison

Watch: Sensitivity-Based Risk And PnL Attribution â€“ Fixed-Income Securities

Partial Revaluation vs. Full Revaluation DCF Methodologies | Taylor-Series Approximation | Risk Sensitivities â€“ Duration | DV01 | Convexity | Duration-Convexity PnL | Residual PnL | Methodology Difference â€“ Full Valuation vs. Duration-Convexity PnL | Model Limitations | Market Risk Report

**Week 12 and 13: Value-at-Risk Methodologies And Advancements**

Welcome to Week 12 and 13, 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 Measure 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 â€“Â 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

Here's to your success and the exciting path ahead! happy learning, Professional!

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