Quantitative Asset Allocation: Modern Portfolio Theory, Risk Budgeting, and Factor Models

Objetivo:
En este curso de dos días, nos enfocaremos en las herramientas cuantitativas subyacentes a los modelos contemporáneos que se utilizan en la asignación de activos.
Partiremos de la clásica Teoría Moderna de Portafolios y a continuación revisaremos las herramientas de la teoría de matrices, la correlación y la volatilidad. Después analizaremos el enfoque de presupuesto de riesgos y veremos cómo puede aplicarse a la administración de portafolios.
También revisaremos los modelos de factores y, en particular, los nuevos enfoques. Mediante una combinación de clases teóricas y talleres prácticos con computadora, este curso ofrece una revisión práctica de las últimas herramientas utilizadas en este campo.

Temario:
Día 1:
Modern Portfolio Theory
1. Harry Markowitz (1952)
2. Modern Portfolio Theory (MPT)
3. Risk and Return (again)
4. Modelling Equations N=2, N=3 and Matrix Equations
5. The Attainable Region
6. The Efficient Frontier
7. The mu-problem and the sigma-problem
8. Example 1: Portfolio N=2 in Excel
9. Example 2: Portfolio N=3 in Excel
10. Case Study: What is wrong with Minimum Variance Optimization?

Quantitative Tools
1. Principal Component Analysis (PCA)
2. Dimensionality reduction
3. PCA and Factor analysis
4. Correlation Matrix
5. Covariance matrix
6. Pearson Correlation Coefficient
7. Anscombe’s Quartet
8. Correlation Matrix Stability
9. Eigenvalues and Eigenvectors
10. Singular value decomposition
11. Positive Definite Matrices
12. Computing Volatility: The Hurst Exponent
13. Example 1: Estimate Correlation Matrix from Real Asset Returns
14. Example 2: Eigenvalues with Mathematica
15. Example 3: Calculating PCA (Scikit-learn)
16. Case Study: Fixing a broken correlation matrix (Numerical Algorithms Group)

Portfolio Optimization
1. Basic Theory and Practice
2. Mean-Variance Analysis: Overview
3. Classical Framework for Mean-Variance Optimization
4. Mean-Variance Optimization with a Risk-Free Asset
5. Portfolio Constraints Commonly Used in Practice
6. Estimating the Inputs Used in Mean-Variance Optimization
7. Portfolio Optimization with Other Risk Measures
8. Example 1: The Markowitz Optimization Problem, (Fabozzi and Focardi, 2010) inMATLAB
9. Example 2: The Risk Budgeting Optimization Problem, (Roncalli, 2013) in MATLAB

Día 2:
Risk Budgeting
1. What is wrong with Markowitz?
2. The Risk Budgeting Solution
3. Value at risk (VaR) and Expected Shortfall (ES) as measures of risk
4. VAR and ES: Definitions, methodologies (analytical, historical, Monte Carlo)
5. Euler Allocation and Risk Budgets
6. Example 1: VaR and ES for portfolio, (Roncalli, 2013) in MATLAB
7. Example 2: Risk Budgeting, (Roncalli, 2013) in MATLAB
8. Case study: MORNINGSTAR, Risk Budgeting Where Do You Spend YourRisk?(Thomas M. Idzorek)
Factor Models
1. The Notion of Factors
2. Static Factor Models
3. Factor Analysis and Principal Components Analysis
4. Why We Need Factor Models of Returns
5. Approximate Factor Models
6. Dynamic Factor Models
7. Examples: Fabozzi and Focardi (2010), Ang (2014)
Factor-Based Asset Allocation vs. Asset-Class-Based Asset Allocation
1. Do Factors Offer Superior Diversification and Noise Reduction?
2. What Is a Factor?
3. Equivalence of Asset Class and Factor Diversification
4. Noise Reduction
5. Where Does This Leave Us?
6. Examples: (Kinlaw, Kritzman, Turkington, 2017)
7. Case Study: Thomas M. Idzorek, Maciej Kowara. Factor-Based Asset Allocationvs. Asset-Class- Based Asset Allocation. Financial Analysts Journal, Volume 69Issue 3, May/June 2013

Date

Jun 23 2021 - Jun 26 2021

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Quantitative Asset Allocation: Modern Portfolio Theory, Risk Budgeting, and Factor Models