Risk-Based Investment Strategies: Smart Beta, Robust Risk Parity and Risk Budgeting

Thursday, October 12

09.00 - 09.15 Welcome and Introduction

09.15 - 12.30

Investment Risk in Context

• The philosophy of risk
• The psychology of risk
• The economics of risk

Introduction to Risk-Based Investment Strategies

• Risk-based versus return-based investing
• Target volatility versus constant asset allocation strategies
• The empirical case for managing risk to improve return, risk and risk-adjusted returns
• How to use risk as a trading signal
• Understanding the demand for risk-based strategies

Minimum Variance Portfolio Investing

• Portfolio Construction
• Risk and return characteristics, success factors
• Empirical characteristics

12.30 - 13.30 Lunch

13.30 - 17.30

Risk Parity, Robust Risk Parity and Risk Budgeting

• Portfolio Construction
• Risk and return characteristics, success factors
• Empirical characteristics

Equal-Weighted Investing

• Portfolio Construction
• Risk and return characteristics, success factors
• Empirical characteristics

The Most Diversified Portfolio

• Portfolio Construction
• Risk and return characteristics, success factors
• Empirical characteristics

Friday, October 13

09.00 - 12.30

Volatility Risk

• Calculating volatility
• Annualizing volatility
• The psychology of volatility
• Statistical tests
• Limitations of volatility
• Robust alternatives to volatility
• The impact of leverage
• Volatility as the lowest common denominator: UCITS

Loss-Based Risk Measures beyond Volatility

• Semi-variance, Lower Partial Moments, VaR, CVaR, Maximum Drawdown, Drawdown-At-Risk and Conditional Drawdown-At-Risk

12.30 - 13.30 Lunch

13.30 - 17.30

Applied Risk Measurement

• Some stylized facts about financial return time series
• Historical approaches
  • Dynamic risk analysis: rolling statistics, exponentially-weighted statistics, introduction to GARCH
  • Covariance estimators: Shrinkage estimators (Ledoit/Wolf, Jorion), using random matrix theory to remove noise
  • Using economic and statistical factor models (PCA)
  • On the relative importance of correlations
• Parametric approaches
  • Distributions: NIG, normal mixtures
  • Bootstrapping, resampling
  • The Cornish-Fisher approximation and its limitations
• Scenario-based estimation of risk
• Handling estimation risk
• Applied Stress Testing and Scenario Analysis
  • The historical versus the parametric approach
  • Tweaking volatilities and correlations
  • Handling low probability scenarios

General Topics in Quantitative Portfolio Construction

• Backtesting issues
• Rebalancing strategies
• Turnover analysis
• Benchmarking issues
• Evaluating results: Performance and risk measures to consider
• On numerical optimization
• Using Excel and VBA
• Handling time series data from illiquid assets

Evaluation and Termination of the Seminar