Modelling Interest Rate Dynamics: A Flexible and Efficient Nonparametric Likelihood Approach

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Interest rates are a part of everyday life. They are fundamental in the allocation of financial resources in the economy and hence have profound implications on almost every aspect of its operation, from consumer spending to firm production to financial investments, as well as on employment, inflation, and growth.Interest rates on debt instruments having maturities of less than one year are known as short term interest rates. In the context of financial markets, the dynamics of short term interest rates are central to the valuation of all economic and financial assets whose values crucially depend on them. Therefore, understanding short term interest rate dynamics is now a major concern of both academics and practitioners.The greatest challenge in modeling short term interest rates is the dilemma between the quest for flexibility in capturing complex features of interest rate dynamics and the desire for tractability and efficiency in the implementation of the model. Existing methods in the literature generally either fail to account for short rate dynamics adequately or lack efficiency in drawing inferences from data in a manner that is convenient and informative.Thus the overall objective of this project is to develop a flexible and efficient new likelihood based approach for modeling interest rate dynamics. Specifically, this project aims to propose a flexible nonparametric specification for a class of stochastic differential equations and develop likelihood based inferential procedures and investigate nonparametric efficiency in that context. It also intends to extend the approach to multivariate case and test the applicability and suitability of the new methodology in a range of substantive empirical problems in which interest rates feature.This project, with its range of innovative methodological, theoretical and empirical proposals, promises new advances on the interest rate modeling front. Crucially, the approaches advocated here for using nonparametric RSDEs are completely new, and fill a substantial gap in a literature still dominated by parametric approaches. The use of Empirical Likelihood in this context is also novel. The methodology developed in this proposal will also provide extensive scope for significant empirical contributions to the discipline, given the wide range of empirical problems in which interest rates are concerned.Although the proposed approach is developed in the context of interest rate modeling, it is general enough to have a methodological impact in the broad realm of stochastic analysis, with substantial applicability to non-economic disciplines such as statistics, physics, engineering, chemistry, medical science, and so on.