Portfolios - EDHEC-Risk

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Implementing the Diversifiers • Estimating covariance matrix parameters is a serious challenge, but a number of statistical techniques can prove effective in addressing this challenge. • If statistical techniques are useful for estimating risk parameter, statistics on the other hand does not allow us to estimate expected returns reliably. • Direct estimation of expected returns from past returns is close to useless (Merton (1980)). • What can we do in practice? – Give up “naïvely” on expected return estimation – Give up “smartly” on expected return estimation 14
Giving Up Naïvely – Global Minimum Variance <strong>Portfolios</strong> • The only scientifically diversified portfolio that relies only on risk parameters is the minimum risk portfolio (GMV in MV setting). • In its purest form, “magic” of diversification is not working properly with GMV portfolios because low volatility stocks are not penalized and therefore over-weighted. • We need to penalize low volatility stocks to avoid concentration in such stocks through weight constraints (flexible norm constraints versus rigid weight constraints – DeMiguel, Garlappi, Nogales and Uppal (2009)). 15
Page 2 and 3: EDHEC-Risk Days 2012 London, March
Page 4 and 5: Managing Investments t in Alternati
Page 6 and 7: • Alternatives to Cap-Weighted In
Page 8 and 9: Indices versus Benchmarks • The w
Page 10 and 11: Fundamental Weighting for Represent
Page 12 and 13: Scientific Approaches: Towards the
Page 16 and 17: Giving Up Smartly - MSR • How to
Page 18 and 19: Ad-Hoc Approaches: Maximum Diversif
Page 20 and 21: Selection Risk - Relative Returns
Page 22 and 23: Diversifying the Diversifiers - Lon
Page 24 and 25: Tracking Error Across Market Condit
Page 26 and 27: • Alternatives to Cap-Weighted In
Page 28 and 29: Tracking Error Constraints • If w
Page 30 and 31: What Separation Theorems Say and Do
Page 32 and 33: Impact of Relative Risk Control Pan

Portfolios - EDHEC-Risk

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