We consider the problem of optimally deleveraging a high net-worth long-short portfolio in a short time period to position the fund favorably with respect to leverage and margin risks, in the face of an adverse outlook on future uncertainty. We develop a generalized mean-variance deleveraging optimization model that accounts for market impact costs in portfolio trading under market illiquidity. Due to asset price impact stemming from both volume and intensity of trading, the model has significant non-convexities. For portfolios with a large number of assets, the model is not solvable using standard software, and thus, we employ an efficient solution scheme based on dual optimization, along with a sequence of progressively-improving feasible portfolios computed under convex approximation. Our computational analysis using real data on ETF assets provides new insights on performance sensitivity. In particular, ignoring market impact severely downgrades portfolio performance depending on leverage and margin policies, as well as market liquidity conditions. Such insights can guide portfolio managers in setting deleveraging policy parameters ex-ante when faced with potential market turbulence. We also test the solution algorithm using random problem instances under thousands of assets to demonstrate the scalability and solvability of the deleveraging model.

考虑到对未来不确定性的不利展望，我们考虑在短期内最佳地去杠杆高净值多空组合的问题，以使该基金在杠杆和保证金风险方面处于有利位置。我们开发了一种广义均值-方差去杠杆优化模型，该模型考虑了在市场流动性不足的情况下证券交易中的市场影响成本。由于交易量和交易强度对资产价格的影响，该模型具有很大的非凸性。对于具有大量资产的投资组合，该模型无法使用标准软件求解，因此，我们采用了基于对偶优化的有效解决方案，以及在凸近似下计算出的逐步改进可行投资组合的序列。我们使用ETF资产的真实数据进行的计算分析提供了有关性能敏感性的新见解。特别是，忽略市场影响会严重降低投资组合的绩效，具体取决于杠杆率和保证金政策以及市场流动性状况。这些见解可以指导投资组合经理在面对潜在的市场动荡时事前设定去杠杆政策参数。我们还使用数千个资产下的随机问题实例测试了解决方案算法，以证明去杠杆模型的可伸缩性和可解性。这些见解可以指导投资组合经理在面对潜在的市场动荡时事前设定去杠杆政策参数。我们还使用数千个资产下的随机问题实例测试了解决方案算法，以证明去杠杆模型的可伸缩性和可解性。这些见解可以指导投资组合经理在面对潜在的市场动荡时事前设定去杠杆政策参数。我们还使用数千个资产下的随机问题实例测试了解决方案算法，以证明去杠杆模型的可伸缩性和可解性。