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Robust Bayesian Portfolio Construction. Josh Davis, PIMCO Jan 12, 2009 UC Santa Barbara Seminar on Statistics and Applied Probability. Introduction. Modern portfolio theory Markowitz’s seminal work (1952, JoF) Sharpe’s CAPM (1964, JoF) Ross’s APT (1976,JET)

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robust bayesian portfolio construction

Robust Bayesian Portfolio Construction

Josh Davis, PIMCO

Jan 12, 2009

UC Santa Barbara

Seminar on Statistics and Applied Probability

  • Modern portfolio theory
    • Markowitz’s seminal work (1952, JoF)
    • Sharpe’s CAPM (1964, JoF)
    • Ross’s APT (1976,JET)
      • Pimco’s approach to asset allocation
  • 2 Main Ingredients
    • Utility Function of Investor
    • Distribution of Asset Returns
markowitz mean variance efficiency
Markowitz Mean-Variance Efficiency
  • Original representation of portfolio problem
    • Investor maximizes following utility function:
    • Subject to:
    • Where:
      • Investor indifferent to higher order moments!
      • Gaussian distribution carries all information relevant to investor’s problem!
markowitz solution
Markowitz Solution
  • Theoretical Result: Diversification!
    • “Don’t put all your eggs in one basket”
  • Practical Issues to Model Implementation
    • “Good” estimates of first two moments
      • These moments are state dependent
      • These moments are also ‘endogenous’
        • General Equilibrium vs. Partial Equilibrium
        • Solution: Assume Investor is infinitesimal
bayesian portfolio construction
Bayesian Portfolio Construction
  • Black-Litterman popularized the approach
    • Combine subjective investor ‘views’ with the sampling distribution in a consistent manner
    • Origins in the economics literature: ‘Minnesotta Prior’
      • See Doan, Litterman, Sims (1984) or Litterman (1986)
      • See Jay Walters’ excellent outline for more details
    • Exploit conjugate priors and Bayes Rule:
  • Bayesian approach naturally integrates observed data and opinion
    • Does the Gaussian updating distribution represent the investor’s beliefs accurately?
      • Black/Litterman implementation very mechanical and unintuitive
      • Inconsistent with bounded rationality, rational inattention
    • Is the sampling distribution (prior) accurately represented by a Gaussian?
      • Quality of asymptotic approximation?
      • Regime switch?
  • Posterior moments a function of this Gaussian framework
    • Efficient Frontier particularly sensitive to the expected return inputs (Merton, 1992)
  • What about the utility function?
    • A wealth of economic literature suggests it doesn’t describe investor behavior accurately
  • As we know, There are known knowns. There are things we know we know. We also know There are known unknowns. That is to say We know there are some things We do not know. But there are also unknown unknowns, The ones we don't know We don't know.

—Donald Rumsfeld, Feb. 12, 2002, Department of Defense news briefing

  • Two types of uncertainties
    • Statistical uncertainty (Calculable Risk)
    • Model uncertainty (‘Knightian’ uncertainty)
      • Ellsberg Paradox provides empirical evidence
      • Multi-prior representation (Gilboa and Schmeidler)
      • Also related to literature on error detection probabilities
  • Is the investor 100% certain in the model inputs?
    • No!
  • Shouldn’t portfolio construction be robust to model misspecification?
    • Yes!
incorporating uncertainty
Incorporating Uncertainty
  • Today I will follow the statistical approach of Garlappi, Uppal and Wang (RFS, 2007)
    • For a complete and rigorous treatment see Hansen and Sargent’s book Robustness
  • Critical modification: max-min objective
    • Subject to:
characterizing uncertainty
Characterizing Uncertainty
  • GUW take a ‘statistical approach’ based on confidence intervals
  • I modify this for the BL framework
  • Parameter ‘e’ determined by investor’s ‘confidence’ in the expected return
  • The inner minimization can be removed via the following adjustment:
  • Where the adjustment puts the expected return on the boundary of the plausible region
example posterior moments
Example Posterior Moments
  • Commodities: 4% (12%)
  • US Bonds: 5.5% (14%)
  • US Large Cap: 8% (22%)
  • US Small Cap: 9% (25%)
  • Sovereign Bonds: 6.5% (18%)
  • EM Equity: 10% (28%)
  • Real Estate: 6% (16%)
correlations from monthly jan 96 dec 08
Correlations from…(Monthly Jan ’96-Dec ’08)
  • Commodities: GSCI
  • US Bonds: LBAG
  • US Large Cap: Russell 200
  • US Small Cap: Russell 2000
  • Global Bonds: Citi Sovereign Index
  • EM Equity: MSCI Em Index
  • Real Estate: MSCI US Reit Index
  • Also, added constraint of weights b/w 0 and 1
  • Reference Model:
  • Plausible Worst Case Model
    • Where:
  • Bayesian Portfolio Methods theoretically appealing…
    • Attempts to correct for misspecification by incorporating additional information
    • Doesn’t rule out misspecification
  • Robust methods insure against plausible worst-case scenarios
  • Accounting for uncertainty leads to…
    • Lower volatility under ‘reference model’
    • Lower expected return under ‘reference model’
    • Improved risk/return tradeoff under ‘worst-case’ scenarios
example derivation of prior
Example Derivation of Prior
  • In BL views take the following form:
  • Which can be represented as:
  • The investor’s updating distribution is
posterior derivation
Posterior Derivation
  • The prior and updating distributions take the form
  • The posterior is Gaussian