Kerry Back
\[x_s = 1 - \sum_{i=1}^n w_i\]
\[x_sr_s + (1-x_s)\sum_{i=1}^n \hat{w}_i \bar{r}_i - r_s\]
variance is \((1-x_s)\hat{w}^\top C \hat{w}(1-x_s)\)
std dev is \((1-x_s)\sqrt{\hat{w}^\top C \hat{w}}\)
So, \(1-x_s\) is in both numerator and denominator of Sharpe ratio. Sharpe ratio doesn’t depend on \(x_s\)
\(r_s\) = 2%. Fully invested portfolio has \(\bar{r}\) = 10%, std dev = 15%.
Line is set of (std dev, mean) pairs generated by \(0 \le x_s \le 1\). Slope is Sharpe ratio.
\(r_b\) = 5%. Fully invested portfolio has \(\bar{r}\) = 10%, std dev = 15%.
Line is set of (std dev, mean) pairs generated by \(0 \le x_b \le 1\). Slope is Sharpe ratio at borrowing rate.
\(r_s\) = 2% and \(r_b\) = 5%. Fully invested portfolio has \(\bar{r}\) = 10%, std dev = 15%.
Opportunities for \(0\le x_s \le 1\) or \(0 \le x_b \le 1\).