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Main author:	Qin, Duo
Other authors:	van Huellen, Sophie Wang, Qing Chao Moraitis, Thanos
Format:	Journal Article
Online access:	Click here to view record

id	eprints-37131
recordtype	eprints
institution	SOAS, University of London
collection	SOAS Research Online
language	English
language_search	English
description	Aggregate financial conditions indices (FCIs) are constructed to fulfil two aims: (i) The FCIs should resemble non-model-based composite indices in that their composition is adequately invariant for concatenation during regular updates; (ii) the concatenated FCIs should outperform financial variables conventionally used as leading indicators in macro models. Both aims are shown to be attainable once an algorithmic modelling route is adopted to combine leading indicator modelling with the principles of partial least-squares (PLS) modelling, supervised dimensionality reduction, and backward dynamic selection. Pilot results using US data confirm the traditional wisdom that financial imbalances are more likely to induce macro impacts than routine market volatilities. They also shed light on why the popular route of principal-component based factor analysis is ill-suited for the two aims.
format	Journal Article
author	Qin, Duo
author_facet	Qin, Duo van Huellen, Sophie Wang, Qing Chao Moraitis, Thanos
authorStr	Qin, Duo
author_letter	Qin, Duo
author2	van Huellen, Sophie Wang, Qing Chao Moraitis, Thanos
author2Str	van Huellen, Sophie Wang, Qing Chao Moraitis, Thanos
title	Algorithmic Modelling of Financial Conditions for Macro Predictive Purposes: Pilot Application to USA Data
publisher	MDPI
publishDate	2022
url	https://eprints.soas.ac.uk/37131/