Empirical estimation of the ultimate forward rate using the Kalman filter and smoother

  • Ryan Tjin Ryan Tjin

Since the introduction of the Ultimate Forward Rate (UFR) there has been an ongoing debate amongst stakeholders in the pension and financial sector. The discussion mostly revolves around the chosen model and their input parameters. The proposed UFR method is the parametric Smith-Wilson extrapolation function with a fixed UFR value and Last Liquid Point (LLP). Therefore the question arises if there is an empirical estimation method for the UFR, which is objective, transparent and dynamic?. In this thesis a model is constructed which incorporates all market data and returns parameters from which an UFR value is computed. This is done by applying a Kalman Filter to the Vasicek short rate model. It was clearly shown that the Kalman Filter and Smoother can successfully filter noise from observed yields if the underlying state structure is known. Thus providing a possible solution for the LLP debate. However, the model coped with the known limitations of the Vasicek model and therefore needs to be researched further in order to actually serve as an alternative to the proposed solution by EIOPA.

Netspar, Network for Studies on Pensions, Aging and Retirement, is a thinktank and knowledge network. Netspar is dedicated to promoting a wider understanding of the economic and social implications of pensions, aging and retirement in the Netherlands and Europe.


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