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Deriving Black–Litterman Risk Aversion from the Market Sharpe Ratio

Article Quant Q&A · Author: sci9

Summary

The note derives the Black–Litterman risk-aversion coefficient from the market portfolio’s equilibrium weights and implied excess returns. Starting from the unconstrained mean–variance objective, its first-order solution gives implied returns as risk aversion times covariance multiplied by portfolio weights. Multiplying by market weights yields risk aversion as the portfolio’s expected excess return divided by its variance.

Expressing the numerator in volatility units identifies it as the market Sharpe ratio; dividing by market volatility gives the stated relationship. The derivation depends on treating market weights as the unconstrained mean–variance solution and on using excess returns consistently. It explains the algebra rather than offering empirical validation, and does not address constraints, estimation error, or alternative conventions for defining the Sharpe ratio.

Key ideas

  • The unconstrained mean–variance optimum implies expected excess returns equal risk aversion times covariance and portfolio weights.
  • Using market weights gives the Black–Litterman implied equilibrium returns.
  • Multiplying the implied-return equation by market weights expresses risk aversion as expected excess return divided by market variance.
  • Rewriting that ratio yields market Sharpe ratio divided by market volatility.

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Full text
# relation between risk averson coefficient and maximum Sharp ratio in Black-Litterman context


# relation between risk averson coefficient and maximum Sharp ratio in Black-Litterman context












BL model compute the implied returns based on the reverse optimization where the objective is:

$${\underbrace U_{{\rm{investor's \ risk \ utility}}} \buildrel \Delta \over = {\bf{w}}_M^T{\bf{\Pi }} - \frac{\delta }{2}{\bf{w}}_M^T{\bf{\Sigma w}}_M^{}}$$

A mentioned here, we can compute the risk aversion parameter by multiplying both sides of ${\bf{\Pi }} = \delta \times {\bf{\Sigma }} \times {\bf{w}}_M^{}$ with ${{\bf{w}}_M^T}$, to output the following relation:

$$ \delta = \frac{{Sharp \ Ratio}}{{\sqrt {{\bf{w}}_M^T{\bf{\Sigma w}}_M^{}} }}$$

As we know

$$Sharp \ Ratio= \frac{{\bf{\Pi }}}{{\sqrt {{\bf{w}}_M^T{\bf{\Sigma w}}_M^{}} }} = \frac{{\mu _M^{} - {r_f}}}{{\sigma _M^{}}}$$

However, I do not know how we can reach from ${\bf{w}}_M^T{\bf{\Pi }} = \delta {\bf{w}}_M^T{\bf{\Sigma w}}_M^{}$ to the relation above.

Reference: https://www.mathworks.com/help/finance/black-litterman-portfolio-optimization.html

## Answer by Pleb (score 2, accepted)

https://quant.stackexchange.com/a/66065

### Solving it algebraically:

As seen in the above provided reference (just above " 1) "), the general formulation for the unconstrained Markowitz portfolio optimization scheme, is given by:

\begin{align} &\text{arg}\max_{w} \; w^T\mu-\frac{\delta}{2} w^T\Sigma w.\\ \end{align} In absence of any constraints, the above optimization scheme have the closed-form solution:

$w = \frac{1}{\delta} \Sigma^{-1}\mu$.

Now, solving for the expected excess returns $\mu$ and we see something recognisable, $\mu = \delta \Sigma w$. In essence, if the weights equal the market weights, $w=w_m$, then the implied excess equilibrium returns equals the expected excess returns, $\Pi = \mu$ (this is also written here on p. 5).

#### The derivations:

Now, let $\Pi = \delta \Sigma w_m$ be the implied excess equilibrium returns, then multiplying both sides with $w^T_m$ we get:

\begin{equation} w^T_m \Pi = \delta w^T_m\Sigma w_m \qquad \iff \qquad \delta = \frac{w^T_m \Pi}{ w^T_m\Sigma w_m} \end{equation}

Since we are working with the market weights, it implies that $\Pi = \mu$ and thus we can do the following algebraic calculations:

\begin{align*} \delta &= \frac{w^T_m \Pi}{ w^T_m\Sigma w_m} \\ &= \frac{w^T_m \mu}{ w^T_m\Sigma w_m}\\ &= \frac{\frac{w^T_m \mu}{\sqrt{w^T_m\Sigma w_m}}}{ \sqrt{w^T_m\Sigma w_m}}\\ &=\frac{\text{Sharpe}}{\sigma_m}, \end{align*}

which is the same expression as stated in your reference. I hope this provide some help.

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.