Double Sampling Kalman Filter with Applications in Investment Fund Reconstruction Problems

Posted: 9 Dec 2018

See all articles by Zimeng Cheng

Zimeng Cheng

Stevens Institute of Technology - School of Business

Ou Hui

Stevens Institute of Technology - School of Business

Zi Lang Wong

Stevens Institute of Technology - School of Business

Mu Tian

Stevens Institute of Technology - School of Business

Ionut Florescu

Stevens Institute of Technology

Date Written: November 14, 2018

Abstract

The investment fund replication techniques and related financial product have been developed in recent years. It is understood that investing in the replicated fund can help investors gain similar returns without suffering drawbacks of the target fund. However, it is difficult to produce reliable replications using traditional quantitative techniques based on factor models with selective market assets that are correlated with the fund portfolio. The methodologies require specially tuned input parameters and/or time windows in order to output strategies with practical transactions, less long-term tracking errors and lower rebalancing costs. In this work, we propose to reconstruct the target fund with its actual assets and weights. It is done using an innovative non-parametric technique called Double Sampling Kalman Filter, derived from the ordinary Kalman Filter. The algorithm is designed to estimate optimal filter parameters from the data, as well as to identify the sudden changes (jumps) in the portfolio weight. The algorithm is discussed and compared with other types of Kalman Filters. It is later tested using generated portfolios with different scenarios. Finally, we demonstrate the reconstruction results of a real market portfolio.

Keywords: Kalman Filter, Filtering, Portfolio Replication, Portfolio Reconstruction, Jump Detection

JEL Classification: C13, C14

Suggested Citation

Cheng, Zimeng and Hui, Ou and Wong, Zi Lang and Tian, Mu and Florescu, Ionut, Double Sampling Kalman Filter with Applications in Investment Fund Reconstruction Problems (November 14, 2018). Available at SSRN: https://ssrn.com/abstract=3284772 or http://dx.doi.org/10.2139/ssrn.3284772

Zimeng Cheng (Contact Author)

Stevens Institute of Technology - School of Business ( email )

Hoboken, NJ 07030
United States

Ou Hui

Stevens Institute of Technology - School of Business ( email )

Hoboken, NJ 07030
United States

Zi Lang Wong

Stevens Institute of Technology - School of Business ( email )

Hoboken, NJ 07030
United States

Mu Tian

Stevens Institute of Technology - School of Business ( email )

Hoboken, NJ 07030
United States

Ionut Florescu

Stevens Institute of Technology ( email )

Castle Point on the Hudson
Hoboken, NJ 07030
United States

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