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