# penalizing negative skewness by linking $U(\mu)$ and $U(\Sigma)$

Consider $U_1(\mu,\Sigma)$ and $U_2(\mu,\Sigma)$, where $U_1(\mu, \cdot) = U_2(\mu, \cdot)$, $U_1(\cdot, \Sigma) = U_2(\cdot, \Sigma)$ such that

\begin{equation*} arg\inf\limits_{\mu \in U_1(\mu, \cdot), \Sigma \in U_1(\cdot, \Sigma)} \left(w^T \mu – \alpha w^T \Sigma w \right) \equiv arg\inf\limits_{\mu \in U_2(\mu, \cdot), \Sigma \in U_2(\cdot, \Sigma)} \left(w^T \mu – \alpha w^T \Sigma w\right) \end{equation*}

Solutions for inner problems are the same

$(\mu, \Sigma)$ from $U_1$ are positively related ($\Sigma \uparrow$ as $\mu \uparrow \Rightarrow$ positively skewed)

$(\mu, \Sigma)$ from $U_2$ are negatively related ($\Sigma \downarrow$ as $\mu \uparrow \Rightarrow$ negatively skewed)

Solutions for inner problem: low $\mu$, high $\Sigma$

$$arg\inf\limits_{(\mu, \Sigma) \in U_1(\mu, \Sigma)} \left(w^T \mu – \alpha w^T \Sigma w \right) \geq arg\inf\limits_{(\mu, \Sigma) \in U_2(\mu, \Sigma)} \left(w^T \mu – \alpha w^T \Sigma w \right) \quad (1)$$

$\Rightarrow$ negative skewness is penalized by linking $U(\mu)$ and $U(\Sigma)$.

\begin{align} \max\limits_w \left(w^T \mu – \alpha w^T \Sigma w\right) &\geq \max\limits_w\inf\limits_{(\mu, \Sigma) \in U(\mu, \Sigma)} \left(w^T \mu – \alpha w^T \Sigma w\right)\\ &\geq \max\limits_w\inf\limits_{\mu \in U(\mu), \Sigma \in U(\Sigma)} \left(w^T \mu – \alpha w^T \Sigma w\right)\quad (2) \end{align}

Can anyone help me in proving the equation 1 and 2? here the uncertain set for mu(mean) could be ellipsoid and will change the problem in second order conic problem ......and the uncertain set for the sigma(variance matrix) could be a rectangular box which will change the problem in semi definite programming problem.also we can choose some others as i mention in the comments.

• here U(μ) and U(Σ) are the uncertain sets of mean and covariance. Commented Apr 5, 2011 at 7:12
• please, type it using LaTeX - otherwise it's very hard to go through your formulas.
– SBF
Commented Apr 5, 2011 at 8:07
• It can be even edited without you - but "___" I cannot understand well.
– SBF
Commented Apr 5, 2011 at 11:32
• @amber -- It seems this was cut-and-pasted from some homework? Please check that I've typeset correctly. Without some more info, I can't make this out. Commented Apr 5, 2011 at 11:47
• Now I think that the previous time it was even better )
– SBF
Commented Apr 5, 2011 at 12:11

As a general hint: try to solve a small-scale case first. Pick a two- or better three-asset $(\mu,\Sigma)$ and construct the objectives. Construct the "skewness tensor" (it's not a matrix, you can think of it like a "cube" or something - consult this book on how you can actually represent it as a matrix, or Fabozzi et al's textbook for an accessible discussion to the whole idea of introducing skewness tensors).