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Published byJacquelyn Heslop Modified over 10 years ago

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**Chapter 6 The Mathematics of Diversification**

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**O! This learning, what a thing it is!**

- William Shakespeare

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**Outline Introduction Linear combinations Single-index model**

Multi-index model

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**Introduction The reason for portfolio theory mathematics:**

To show why diversification is a good idea To show why diversification makes sense logically

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**Introduction (cont’d)**

Harry Markowitz’s efficient portfolios: Those portfolios providing the maximum return for their level of risk Those portfolios providing the minimum risk for a certain level of return

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Linear Combinations Introduction Return Variance

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Introduction A portfolio’s performance is the result of the performance of its components The return realized on a portfolio is a linear combination of the returns on the individual investments The variance of the portfolio is not a linear combination of component variances

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Return The expected return of a portfolio is a weighted average of the expected returns of the components:

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**Variance Introduction Two-security case Minimum variance portfolio**

Correlation and risk reduction The n-security case

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Introduction Understanding portfolio variance is the essence of understanding the mathematics of diversification The variance of a linear combination of random variables is not a weighted average of the component variances

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**Introduction (cont’d)**

For an n-security portfolio, the portfolio variance is:

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Two-Security Case For a two-security portfolio containing Stock A and Stock B, the variance is:

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**Two Security Case (cont’d)**

Example Assume the following statistics for Stock A and Stock B: Stock A Stock B Expected return .015 .020 Variance .050 .060 Standard deviation .224 .245 Weight 40% 60% Correlation coefficient .50

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**Two Security Case (cont’d)**

Example (cont’d) What is the expected return and variance of this two-security portfolio?

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**Two Security Case (cont’d)**

Example (cont’d) Solution: The expected return of this two-security portfolio is:

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**Two Security Case (cont’d)**

Example (cont’d) Solution (cont’d): The variance of this two-security portfolio is:

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**Minimum Variance Portfolio**

The minimum variance portfolio is the particular combination of securities that will result in the least possible variance Solving for the minimum variance portfolio requires basic calculus

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**Minimum Variance Portfolio (cont’d)**

For a two-security minimum variance portfolio, the proportions invested in stocks A and B are:

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**Minimum Variance Portfolio (cont’d)**

Example (cont’d) Assume the same statistics for Stocks A and B as in the previous example. What are the weights of the minimum variance portfolio in this case?

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**Minimum Variance Portfolio (cont’d)**

Example (cont’d) Solution: The weights of the minimum variance portfolios in this case are:

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**Minimum Variance Portfolio (cont’d)**

Example (cont’d) Weight A Portfolio Variance

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**Correlation and Risk Reduction**

Portfolio risk decreases as the correlation coefficient in the returns of two securities decreases Risk reduction is greatest when the securities are perfectly negatively correlated If the securities are perfectly positively correlated, there is no risk reduction

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The n-Security Case For an n-security portfolio, the variance is:

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**The n-Security Case (cont’d)**

The equation includes the correlation coefficient (or covariance) between all pairs of securities in the portfolio

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**The n-Security Case (cont’d)**

A covariance matrix is a tabular presentation of the pairwise combinations of all portfolio components The required number of covariances to compute a portfolio variance is (n2 – n)/2 Any portfolio construction technique using the full covariance matrix is called a Markowitz model

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**Single-Index Model Computational advantages**

Portfolio statistics with the single-index model

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**Computational Advantages**

The single-index model compares all securities to a single benchmark An alternative to comparing a security to each of the others By observing how two independent securities behave relative to a third value, we learn something about how the securities are likely to behave relative to each other

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**Computational Advantages (cont’d)**

A single index drastically reduces the number of computations needed to determine portfolio variance A security’s beta is an example:

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**Portfolio Statistics With the Single-Index Model**

Beta of a portfolio: Variance of a portfolio:

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**Portfolio Statistics With the Single-Index Model (cont’d)**

Variance of a portfolio component: Covariance of two portfolio components:

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Multi-Index Model A multi-index model considers independent variables other than the performance of an overall market index Of particular interest are industry effects Factors associated with a particular line of business E.g., the performance of grocery stores vs. steel companies in a recession

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**Multi-Index Model (cont’d)**

The general form of a multi-index model:

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