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

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