portfolio optimization under uncertainty
DESCRIPTION
Presentation to the Queen's Master of Finance, Class of 2014TRANSCRIPT
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Portfolio Optimization Under Uncertainty
Guest LectureAdam Butler, CFA CAIA
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THE GOLDEN RULE OF INVESTMENT MANAGEMENT
Risk is the probability of not achieving financial objectives.
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For most investors, risk is defined as the probability of not meeting financial objectives.
Investing should have the exclusive objective of minimizing this risk.
What is the primary risk for most investors?
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The smooth geometric growth curve is a myth.
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Investing is a stochastic process; it is all about probabilities.
© Gestaltu
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The probability of any investment outcome is a function of expected return, and volatility around that expectation.
© Gestaltu
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Given an expected return and volatility we can quantify a range of outcomes at any investment horizon.
Source: Shiller, FRED (2013)
© Gestaltu
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STRUCTURAL DIVERSIFICATIONPortfolios must be robust to many possible market regimes.
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Traditional stock/bond portfolios are very sensitive to market regime.
Source: Deutsche Bank
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As a result, investors are vulnerable to an alarming range of outcomes, even over long horizons.
© Gestaltu
Source: Shiller, FRED (2013)
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How can we make portfolios resilient to a wider range of regimes?
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• Stocks react favorably to accelerating economic growth and decelerating inflation.
• Treasuries respond favorably to decelerating growth and inflation
• Commodities respond favorably to accelerating inflation.• Gold responds well to some kinds of inflation, and to the
actions that authorities take to battle deflation.• Etc.
Financial theory offers clues about how assets will react in different environments.
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Portfolios with assets that thrive in each major regime are ‘structurally diversified’.
© Gestaltu
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One possible structurally diversified portfolio.
© Gestaltu
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RISK BASED OPTIMIZATIONCombining structural diversification with dynamic portfolio estimates.
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U.S. Stocks – VTI European Stocks - VGK
Japanese Stocks – EWJ Emerging Market Stocks - EEM
U.S. REITs - ICF International REITs - RWX
Commodities - DBC Gold - GLD
Intermediate Treasuries – IEF Long Treasuries - TLT
A simple structurally diversified universe for investigation.
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Equal weight resembles a traditional policy portfolio.
40% Equities20% Real Estate20% Alternatives20% Fixed Income
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Results: Equal Weight, Rebalanced Monthly
© Gestaltu
Data source: Bloomberg
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Results: Equal Weight, Rebalanced Monthly
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• Assets included in the portfolio have wildly different ambient volatilities.
• Asset volatilities change profoundly over time.• Asset correlations change dramatically over time.
• As a result, asset risk contributions are highly unstable.
The simple policy portfolio framework has some challenges.
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Asset class volatilities are wildly unstable.
Ranges of Asset Class Volatility© Gestaltu
Data source: Bloomberg
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• Examples of potential dynamic optimizations:– Naïve risk parity– Robust risk parity– Mean-variance optimization
• The following examples use short-term historical realized volatility and covariance as inputs for dynamic portfolio optimization.– Volatility estimate = 60 day historical observed volatility– Covariance estimate = 250 day historical observed covariance
Dynamic Asset Allocation applies dynamic parameter estimates to re-optimize portfolios at each rebalance period.
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In an equal weight portfolio, the lunatics run the asylum.Proportion of rolling 60-day historical volatility
© Gestaltu
Data source: Bloomberg
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If we can estimate volatility, we can use these estimates to scale weights by inverse volatility: naïve risk parity.
Portfolio weights scaled by 1/rolling 60-day historical volatility
© Gestaltu
Data source: Bloomberg
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Results: Naïve Risk Parity, Rebalanced Monthly
© Gestaltu
Data source: Bloomberg
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Results: Naïve Risk Parity, Rebalanced Monthly
Data source: Bloomberg
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Naïve risk parity assumes all assets have similar returns, and are similarly correlated. Is this a reasonable assumption?
© Gestaltu
Data source: Bloomberg
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An asset contributes risk to a portfolio in proportion to its volatility AND its correlation to the portfolio itself.
37% Volatility Reduction
Negative MRC
© Gestaltu
Data source: Bloomberg
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Robust risk parity seeks portfolio weights which equalize marginal risk contributions.
© Gestaltu
Data source: Bloomberg
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Results: Robust Risk Parity (Equal Risk Contribution), Rebalanced Monthly
© Gestaltu
Data source: Bloomberg
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Results: Robust Risk Parity (Equal Risk Contribution), Rebalanced Monthly
Data source: Bloomberg
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MEAN VARIANCE OPTIMIZATIONMaximizing portfolio Sharpe ratio.
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Asset returns are even more unstable than volatility and covariance.
© Gestaltu
Data source: Bloomberg
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Returns are more forecastable in the very short term, and the very long term. Not so much in the middle.
High frequency arms race
Momentum sweet spot
Value (long-term mean-reversion)
Data source: Bloomberg
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Is it helpful to use long-term average return estimates with dynamic covariance estimates?
*Source: JPM Long Term Capital Market Return Assumptions, December 2013
Long-Term Returns*
DBC 3.8%
EEM 9.0%
EWJ 7.8%
GLD 4.3%
ICF 6.8%
IEF 4.3%
RWX 6.0%
TLT 3.3%
VGK 7.8%
VTI 7.5%
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Results: Long-term average returns with dynamic covariance estimates.
© Gestaltu
Data source: Bloomberg
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Results: Mean variance using long-term average returns with dynamic covariance estimates.
Data source: Bloomberg
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This intuition is sound because returns are empirically and theoretically proportional to risk.
Source: BridgewaterData source: Bloomberg
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• Mean variance optimization is implemented by maximizing the Sharpe Ratio.
• Maximum Diversification (Choueifaty, 2008) is mean-variance optimization where E(ri) = E(σi).
Are historical averages the only source of return estimates?
Data source: Bloomberg
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Volatility is not the only measure of risk. And it’s worthwhile considering other simple methods like rank.
VolatilityDownside
Semivariance Drawdown Rank
DBC 12.5% 16.0% 60.3% 1
EEM 13.1% 19.4% 69.9% 5
EWJ 14.4% 19.5% 58.9% 5
GLD 9.0% 11.9% 38.8% 1
ICF 10.2% 16.6% 77.6% 4
IEF 4.2% 5.5% 11.4% 2
RWX 8.3% 13.0% 73.8% 4
TLT 6.9% 9.8% 26.6% 3
VGK 11.4% 16.7% 63.3% 5
VTI 10.1% 14.1% 55.5% 5
Data source: Bloomberg
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Results: Heuristic mean-variance optimization with alternative return estimates.
max.drawdown
rank
downside.semi
volatility
© Gestaltu
Data source: Bloomberg
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Results: Heuristic mean-variance optimization with alternative return estimates.
Data source: Bloomberg
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REVISITING THE GOLDEN RULE
Did we achieve our goal of minimizing the risk of not achieving financial objectives?
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Thoughtful optimization can materially reduce the probability of not achieving financial objectives.
© Gestaltu
Data source: Bloomberg
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Questions?
Thank you very much.