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As a sequel to the material here, please see our lecture [two modifications of mean-variance portfolio theory](https://python-advanced.quantecon.org/black_litterman.html).
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## Key Equation
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## Key equation
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We begin with a **key asset pricing equation**:
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In **incomplete markets** models like those illustrated in this lecture [the Aiyagari model](https://python.quantecon.org/aiyagari.html), the stochastic discount factor is not unique.
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## Implications of Key Equation
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## Implications of key equation
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We combine key equation {eq}`eq:EMR1` with a remark of Lars Peter Hansen that "asset pricing theory is all about covariances".
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Equation {eq}`eq:EMR3` can be rearranged to display important parts of asset pricing theory.
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## Expected Return - Beta Representation
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## Expected return - beta representation
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We can obtain the celebrated **expected-return-Beta -representation** for gross return $R^i$ by simply rearranging excess return equation {eq}`eq:EMR3` to become
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## Mean-Variance Frontier
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## Mean-variance frontier
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Now we'll derive the celebrated **mean-variance frontier**.
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This is a measure of the part of the risk in $R^j$ that is not priced because it is uncorrelated with the stochastic discount factor and so can be diversified away (i.e., averaged out to zero by holding a diversified portfolio).
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## Sharpe Ratios and the Price of Risk
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## Sharpe ratios and the price of risk
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An asset's **Sharpe ratio** is defined as
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Evidently it equals the maximum Sharpe ratio for any asset or portfolio of assets.
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## Mathematical Structure of Frontier
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## Mathematical structure of frontier
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The mathematical structure of the mean-variance frontier described by inequality {eq}`eq:ERM6` implies
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that
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## Multi-factor Models
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## Multi-factor models
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The single-beta representation {eq}`eq:EMR7` is a special case of the multi-factor model
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* $\lambda_{a}$ is the price of exposure to risk factor $f_a$
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## Empirical Implementations
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## Empirical implementations
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We briefly describe empirical implementations of multi-factor generalizations of the single-factor model described above.
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