肥尾分布与最优负债驱动投资组合
Fat Tails and Optimal Liability Driven Portfolios
AI总结：
本研究针对肥尾和极值风险测度下的负债驱动投资组合，提出极值偏差（XD）风险测度，优化每单位XD的收益，并分析配置的定性与定量特性。
摘要：
我们研究了在肥尾和极值风险测度族中的最优负债驱动投资组合，特别是在养老金基金和保险固定现金流负债概况的背景下，但也包括在随机波动率存在下产生的衍生品账簿中的那些，如德尔塔一账簿或期权账簿。在极值极限下，我们得到一个新的尾部风险测度，即极值偏差（XD），一种对极值收益的敏感性显著高于CVaR的极值风险测度。由此产生的最优投资组合优化每单位XD的收益，其投资组合权重由负债对冲贡献和寻求产生正风险调整收益的风险贡献组成。所得配置在多种不同极限下进行了定性和定量分析。
英文摘要：
We look at optimal liability-driven portfolios in a family of fat-tailed and extremal risk measures, especially in the context of pension fund and insurance fixed cashflow liability profiles, but also those arising in derivatives books such as delta one books or options books in the presence of stochastic volatilities. In the extremal limit, we recover a new tail risk measure, Extreme Deviation (XD), an extremal risk measure significantly more sensitive to extremal returns than CVaR. Resulting optimal portfolios optimize the return per unit of XD, with portfolio weights consisting of a liability hedging contribution, and a risk contribution seeking to generate positive risk-adjusted return. The resulting allocations are analyzed qualitatively and quantitatively in a number of different limits.
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