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Chapter 3 Delineating Efficient Portfolios. Jordan Eimer Danielle Ko Raegen Richard Jon Greenwald. Goal. Examine attributes of combinations of two risky assets Analysis of two or more is very similar This will allow us to delineate the preferred portfolio THE EFFICIENT FRONTIER!!!!.

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chapter 3 delineating efficient portfolios

Chapter 3Delineating Efficient Portfolios

Jordan Eimer

Danielle Ko

Raegen Richard

Jon Greenwald

slide2
Goal
  • Examine attributes of combinations of two risky assets
    • Analysis of two or more is very similar
    • This will allow us to delineate the preferred portfolio
      • THE EFFICIENT FRONTIER!!!!
combination of two risky assets
Combination of two risky assets
  • Expected Return
  • Investor must be fully invested
    • Therefore weights add to one
  • Standard deviation
    • Not a simple weighted average
      • Weights do not, in general add to one
      • Cross-product terms are involved
    • We next examine co-movement between securities to understand this
case 1 perfect positive correlation p 1
Case 1-Perfect Positive Correlation(p=+1)
  • C=Colonel Motors
  • S=Separated Edison
  • Here, risk and return of the portfolio are linear combinations of the risk and return of each security
case2 perfect negative correlation p 1
Case2-Perfect Negative Correlation (p=-1)
  • This examination yields two straight lines
    • Due to the square root of a negative number
  • This std. deviation is always smaller than p=+1
    • Risk is smaller when p=-1
    • It is possible to find two securities with zero risk
no relationship between returns on the assets 0
No Relationship between Returns on the Assets ( = 0)
  • The expression for return on the portfolio remains the same
  • The covariance term is eliminated from the standard deviation
  • Resulting in the following equation for the standard deviation of a 2 asset portfolio
minimum variance portfolio
Minimum Variance Portfolio
  • The point on the Mean Variance Efficient Frontier that has the lowest variance
  • To find the optimal percentage in each asset, take the derivative of the risk equation with respect to Xc
  • Then set this derivative equal to 0 and solve for Xc
intermediate risk 5
Intermediate Risk ( = .5)
  • A more practical example
  • There may be a combination of assets that results in a lower overall variance with a higher expected return when 0 < < 1
  • Note: Depending on the correlation between the assets, the minimum risk portfolio may only contain one asset
2 asset portfolio conclusions
2 Asset Portfolio Conclusions
  • The closer the correlation between the two assets is to -1.0, the greater the diversification benefits
  • The combination of two assets can never have more risk than their individual variances
the shape of the portfolio possibilities curve

The Shape of the Portfolio Possibilities Curve

The Minimum Variance Portfolio

Only legitimate shape is a concave curve

The Efficient Frontier with No Short Sales

All portfolios between global min and max return portfolios

The Efficient Frontier with Short Sales

No finite upper bound

the efficient frontier with riskless lending and borrowing
The Efficient Frontier with Riskless Lending and Borrowing

All combinations of riskless lending and borrowing lie on a straight line

input estimation uncertainty
Input Estimation Uncertainty
  • Reliable inputs are crucial to the proper use of mean-variance optimization in the asset allocation decision
  • Assuming stationary expected returns and returns uncorrelated through time, increasing N improves expected return estimate
  • All else equal, given two investments with equal return and variance, prefer investment with more data (less risky)
input estimation uncertainty1
Input Estimation Uncertainty
  • Predicted returns with have mean R and

variance σPred2 = σ2 + σ2/T where:

σPred2 is the predicted variance series

σ2 is the variance of monthly return

T is the number of time periods

  • σ2 captures inherent risk
  • σ2/T captures the uncertainty that comes from lack of knowledge about true mean return
  • In Bayesian analysis, σ2 + σ2/T is known as the predictive distribution of returns
  • Uncertainty: predicted variance > historical variance
input estimation uncertainty2
Input Estimation Uncertainty
  • Characteristics of security returns usually change over time.
  • There is a tradeoff between using a longer time frame and having inaccuracies.
  • Most analysts modify their estimates.
  • Choice of time period is complicated when a relatively new asset class is added to the mix.
short horizon inputs and long horizon portfolio choice
Short Horizon Inputs and Long Horizon Portfolio Choice
  • Important consideration in estimate inputs: Time horizon affects variance
  • In theory, returns are uncorrelated from one period to the next.
  • In reality, some securities have highly correlated returns over time.
  • Treasury bill returns tend to be highly autocorrelated – standard deviation is low over short intervals but increases on a percentage basis as time period increases
example
Example
  • Solving for Xc yields for the minimum variance portfolio:

Xc = (σs2 – σcσsρcs)

(σc2 + σs2 - 2σcσsρcs)

  • In a portfolio of assets, adding bonds to combination of S&P and international portfolio does not lead to much improvement in the efficient frontier with riskless lending and borrowing.