英文标题：
《Mean-variance portfolio selection under Volterra Heston model》
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作者：
Bingyan Han and Hoi Ying Wong
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最新提交年份：
2020
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英文摘要：
Motivated by empirical evidence for rough volatility models, this paper investigates continuous-time mean-variance (MV) portfolio selection under the Volterra Heston model. Due to the non-Markovian and non-semimartingale nature of the model, classic stochastic optimal control frameworks are not directly applicable to the associated optimization problem. By constructing an auxiliary stochastic process, we obtain the optimal investment strategy, which depends on the solution to a Riccati-Volterra equation. The MV efficient frontier is shown to maintain a quadratic curve. Numerical studies show that both roughness and volatility of volatility materially affect the optimal strategy.
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中文摘要：
基于粗糙波动率模型的经验证据，本文研究了Volterra-Heston模型下的连续时间均值方差（MV）投资组合选择。由于模型的非马尔可夫和非半鞅性质，经典的随机最优控制框架不能直接应用于相关的优化问题。通过构造一个辅助随机过程，我们得到了最优投资策略，该策略依赖于Riccati-Volterra方程的解。MV有效前沿保持二次曲线。数值研究表明，波动率的粗糙度和波动率都会对最优策略产生重大影响。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Portfolio Management 项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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