Stability improvement for index tracking during a healthcare crisis using a dual decomposition approach

This paper developed a factor-based robust approach to improve the tracking fund's stability. Similar to the financial crisis, the recent coronavirus pandemic amplify the global market volatility significantly, which suggests that healthcare-based factor can be used to hedge against the jump risk. The index tracking fund is constructed by a developed cardinality constrained conic programming. To overcome the large-scale computational challenge, we decompose the problem into two simplified cases and quickly calculate the tighter lower bound and its feasible upper bound. In addition, a subgradient-based inequalities are derived to exclude the suboptimal points that have been traveled in previous iterations. It turns out that the proposed model, along with the designed solving technique, can be used as an alternative to build reliable tracking portfolios. We demonstrate the effectiveness and robustness of the proposed method by testing different large real data sets. © 2022 Elsevier Ltd

Stability improvement for index tracking during a healthcare crisis using a dual decomposition approach

This paper developed a factor-based robust approach to improve the tracking fund’s stability. Similar to the financial crisis, the recent coronavirus pandemic amplify the global market volatility significantly, which suggests that healthcare-based factor can be used to hedge against the jump risk. The index tracking fund is constructed by a developed cardinality constrained conic programming. To overcome the large-scale computational challenge, we decompose the problem into two simplified cases and quickly calculate the tighter lower bound and its feasible upper bound. In addition, a subgradient-based inequalities are derived to exclude the suboptimal points that have been traveled in previous iterations. It turns out that the proposed model, along with the designed solving technique, can be used as an alternative to build reliable tracking portfolios. We demonstrate the effectiveness and robustness of the proposed method by testing different large real data sets.

Dynamic planning of a two-dose vaccination campaign with uncertain supplies.

The ongoing COVID-19 pandemic has led public health authorities to face the unprecedented challenge of planning a global vaccination campaign, which for most protocols entails the administration of two doses, separated by a bounded but flexible time interval. The partial immunity already offered by the first dose and the high levels of uncertainty in the vaccine supplies have been characteristic of most of the vaccination campaigns implemented worldwide and made the planning of such interventions extremely complex. Motivated by this compelling challenge, we propose a stochastic optimization framework for optimally scheduling a two-dose vaccination campaign in the presence of uncertain supplies, taking into account constraints on the interval between the two doses and on the capacity of the healthcare system. The proposed framework seeks to maximize the vaccination coverage, considering the different levels of immunization obtained with partial (one dose only) and complete vaccination (two doses). We cast the optimization problem as a convex second-order cone program, which can be efficiently solved through numerical techniques. We demonstrate the potential of our framework on a case study calibrated on the COVID-19 vaccination campaign in Italy. The proposed method shows good performance when unrolled in a sliding-horizon fashion, thereby offering a powerful tool to help public health authorities calibrate the vaccination campaign, pursuing a trade-off between efficacy and the risk associated with shortages in supply.