City Research Online

Abstract

We introduce a new approach for modeling the time varying behavior and time series evolution of asset returns co-movements. Here, the co-movement in each period is captured by a trajectory of returns correlation, then a sequence of this over time and the time series evolution are studied. We rely on functional principal components to achieve dimension reduction and to construct the dynamic space of interest, while introducing a new class of information criteria in order to identify the finite dimensionality of the curve time series. Our method is able to combine two of the most applied ideas in the literature, namely economics (or finance) based and time-series based time-varying correlation models. This offers a general specification that is able to model processes of time-varying time-series correlations generated under many existing models that have dominated the financial literature for several decades. To illustrate its empirical relevance, we apply our method to model the time varying co-movement of exchange rate returns for a group of small open economies with large financial sectors. Our empirical results indicate that concepts of time varying correlation enabled by existing methods are too restrictive to accommodate fully the time varying behavior and time series evolution of the returns correlation. On the other hand, our method gives a more complete picture and is able to provide more accurate correlation forecasts.

Publication Type:	Article
Additional Information:	© 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords:	Function data analysis, Functional time series, Dimension reduction, Eigenanalysis, Model selection
Subjects:	H Social Sciences > HB Economic Theory
Departments:	School of Policy & Global Affairs School of Policy & Global Affairs > Economics
SWORD Depositor:	Symplectic Administrator

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