论文标题
Bartlett的三角洲重新审视：lognormal SABR和粗糙的Bergomi模型中的方差 - 最佳对冲
Bartlett's Delta revisited: Variance-optimal hedging in the lognormal SABR and in the rough Bergomi model
论文作者
论文摘要
我们在对数正态SABR和粗糙的Bergomi模型中的方差 - 最佳对冲策略及其均方面的套索误差的分析表达式。在SABR模型中，我们表明，方差 - 最佳对冲策略与Bartlett的三角洲调整相吻合[Wilmott Magazine 4/6（2006）]。我们在数学上和模拟中均表明，方差 - 最佳策略的效率（与简单的Delta Hedging相比）在很大程度上取决于杠杆参数RHO，并且在较弱的意义上 - 还取决于模型的粗糙度参数H，并提供了对此依赖性的精确量化。
We derive analytic expressions for the variance-optimal hedging strategy and its mean-square hedging error in the lognormal SABR and in the rough Bergomi model. In the SABR model, we show that the variance-optimal hedging strategy coincides with the Delta adjustment of Bartlett [Wilmott magazine 4/6 (2006)]. We show both mathematically and in simulation that the efficiency of the variance-optimal strategy (in comparison to simple Delta hedging) depends strongly on the leverage parameter rho and - in a weaker sense - also on the roughness parameter H of the model, and give a precise quantification of this dependency.
