What is it about?
Time Dependent Kernel Density Estimation (TDKDE) developed by Harvey and Oryshchenko (2009) requires two input parameters known as bandwidth and discount to perform. A Maximum Likelihood Estimation (MLE) procedure is commonly used to estimate these parameters in a set of data but this method has a weakness; it may not produce stable kernel estimates. We have developed a method to set these initial parameters and significantly improve the performance of the TDKDE.
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Why is it important?
A significant improvement in the kernel estimation performance is achieved by using the proposed method.
Perspectives
We applied the technique on NASDQ stock returns and the TDKDE performed flawlessly.
Dr. Abolfazl Saghafi
University of the Sciences in Philadelphia
Read the Original
This page is a summary of: Improved parameter estimation of Time Dependent Kernel Density by using Artificial Neural Networks, The Journal of Finance and Data Science, September 2018, Elsevier,
DOI: 10.1016/j.jfds.2018.04.002.
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