Kernel Smoothing for Nested Estimation with Application to Portfolio Risk Measurement

L. Jeff Hong (), Sandeep Juneja () and Guangwu Liu ()
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L. Jeff Hong: Department of Economics and Finance and Department of Management Sciences, College of Business, City University of Hong Kong, Kowloon, Hong Kong
Sandeep Juneja: School of Technology and Computer Science, Tata Institute of Fundamental Research, Mumbai 400005, India
Guangwu Liu: Department of Management Sciences, College of Business, City University of Hong Kong, Kowloon, Hong Kong

Operations Research, 2017, vol. 65, issue 3, 657-673

Abstract: 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 smoothing approach is preferable over nested simulation only for low-dimensional problems, we propose a decomposition technique for portfolio risk measurement, through which a high-dimensional problem may be decomposed into low-dimensional ones that allow an efficient use of the kernel smoothing approach. Numerical studies show that, with the decomposition technique, the kernel smoothing approach works well for a reasonably large portfolio with 200 risk factors. This suggests that the proposed methodology may serve as a viable tool for risk measurement practice.

Keywords: nested estimation; kernel estimation; portfolio risk measurement (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (19)

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