Multi-level risk-controlled sector optimization of domestic and international fixed-income portfolios including conditional VaR

R. D'Vari, J. C. Sosa, K. Yalamanchili
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Abstract

We have previously developed a fixed-income sector optimization methodology to facilitate tradeoffs between various sectors based on their contribution to the total portfolio return and risk. We maximize portfolio return subject to constraints including value-at-risk (VaR) and other downside risk measures, both absolute and relative to a benchmark (market and liability-based). Our method optimizes interest rate, curve, credit, and volatility exposures to achieve the highest expected return (view-oriented, historically based, or quantitatively forecast) within the allowed risk space defined by various specified risk constraints. This work advances the state-of-the-art in the risk-controlled optimization process for cases where there are a large number of subsector decision variables. These advances include: 1) introduction of a multi-level optimization process to avoid ill-conditioned joint risk characterization of a large number of subsectors, and to reduce required length of time histories, 2) refinement of our previous VaR and CVaR methodologies to add opportunistic nondollar bonds as well as high yield and emerging markets, and 3) ability to control risk at subsector levels as well as the total portfolio.
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含条件VaR的国内外固定收益投资组合多层次风控板块优化
我们之前已经开发了一种固定收益部门优化方法,以促进基于其对总投资组合回报和风险的贡献的不同部门之间的权衡。我们在包括风险价值(VaR)和其他下行风险指标在内的约束条件下最大化投资组合回报,包括绝对和相对于基准(基于市场和负债)。我们的方法优化了利率、曲线、信贷和波动性敞口,以在由各种特定风险约束定义的允许风险空间内实现最高的预期回报(以观点为导向、以历史为基础或定量预测)。本工作在存在大量子行业决策变量的情况下,推进了风险控制优化过程的最新进展。这些进步包括:1)引入多层次优化过程,以避免大量子行业的病态联合风险特征,并减少所需的时间历史长度;2)改进我们以前的VaR和CVaR方法,以增加机会主义的非美元债券以及高收益和新兴市场;3)在子行业水平以及总投资组合上控制风险的能力。
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