Let $U_1=(X_1,Y_1)^T,\dots,U=(X_n,Y_n)^T$ are i.i.d. copies of $U=(X,Y)^T\sim N_2(0,\Sigma)$ where $$ \Sigma= \begin{pmatrix} \sigma^2 & \rho\sigma\tau \\ \rho\sigma\tau & \tau^2 \end{pmatrix} $$ such that \begin{equation} \sum_{i=1}^n\min\{U_iU_i^T,c\} \end{equation} is invertible where $\min\{A,c\}$ represents the matrix with element $\min\{a_{ij},c\}$. Given $c>0$ and define \begin{equation} b=\frac{\sum_{i=1}^n\max\{-c,\min\{X_iY_i,c\}\}}{\left(\sum_{i=1}^n\min\{X_i^2,c\}\sum_{i=1}^n\min\{Y_i^2,c\}\right)^{1/2}}. \end{equation}
The problem is how to prove that \begin{equation} -E(\min\{\sigma\tau Z^2,c\})\leq b\leq E(\min\{\sigma\tau Z^2,c\}), \end{equation} where $Z$ is normal standard distribution. I don't know how can I start this problem. Any advice?