Kernel Smoothing for Nested Estimation with Application to Portfolio Risk Measurement

@article{Hong2017KernelSF,
  title={Kernel Smoothing for Nested Estimation with Application to Portfolio Risk Measurement},
  author={L. Jeff Hong and Sandeep Juneja and Guangwu Liu},
  journal={Oper. Res.},
  year={2017},
  volume={65},
  pages={657-673}
}
Nested estimation involves estimating an expectation of a function of a conditional expectation via simulation. This problem has of late received increasing attention amongst researchers due to its broad applicability particularly in portfolio risk measurement and in pricing complex derivatives. In this paper, we study a kernel smoothing approach. We analyze its asymptotic properties, and present efficient algorithms for practical implementation. While asymptotic results suggest that the kernel… 
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