This is a summary of links featured on Quantocracy on Sunday, 11/19/2017. To see our most recent links, visit the Quant Mashup. Read on readers!
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Transforming a QP Problem to an SOCP Problem [Numerical Method]A Quadratic Programming problem (QP) in the form of \begin{aligned} & \underset{x}{\text{minimize}} & & \frac{1}{2} x^T H x + p^T x \\ & \text{subject to} \\ & & & A_{eq} x = b_{eq} \\ & & & A x \geq b \end{aligned} where H \in \Re^{n \times n}, A \in \Re^{m \times n}, p, x \in \Re^n, b \in \Re^m, can be transformed to a Second-Order Cone Programming (SOCP)
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Recalibrating Expected Shortfall to Match Value-at-Risk for Discrete Distributions [Quant at Risk]By considering the same risk measure, , applied to two or more portfolios (credit loss distributions, profit-and-loss distributions, etc.) one desires to have a subadditivity property in place: (X1+X2)(X1)+(X2) i.e. meaning that two combined portfolios should never be more risky than the sum of the risk of two portfolios separately. Unfortunately, the Value-at-Risk risk measure does not
