MAMATZAKIS, E and Tsionas, Mike and Ongena, Steven (2022): Why do households repay their debt in UK during the COVID-19 crisis?
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Abstract
This paper employs a vector autoregressive (VAR) model that nests neural networks and uses Mixed Data Sampling (MIDAS) techniques. We use data information related to COVID-19, financial markets, and household finances. In this paper, we investigate whether COVID-19 impacts household finances, like household debt repayments in the UK. Our results show that household debt repayments’ response to the first principal component of COVID-19 shocks is negative, albeit of low magnitude. However, when we employ specific COVID-19 related data like vaccines and tests the responses are positive, insinuating the underlying dynamic complexities. Overall, confirmed deaths and hospitalisations negatively affect household debt repayments. We also report low persistence in household debt repayments. Generalized impulse response functions confirm the main results. As draconian measures, the lockdowns are eased it appears that the COVID-19 shocks are diminishing, and household financial data converge to the levels prior to the pandemic albeit with some lags. To the best of our knowledge, this is the first study that examines the impact of the pandemic on household debt repayments. Our findings show that policy response in the future should prioritise innovation of new vaccines and testing.
Item Type: | MPRA Paper |
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Original Title: | Why do households repay their debt in UK during the COVID-19 crisis? |
English Title: | Why do households repay their debt in UK during the COVID-19 crisis? |
Language: | English |
Keywords: | COVID-19, household debt, ANN, VAR, MIDAS. |
Subjects: | G - Financial Economics > G0 - General G - Financial Economics > G0 - General > G00 - General G - Financial Economics > G1 - General Financial Markets I - Health, Education, and Welfare > I1 - Health |
Item ID: | 118785 |
Depositing User: | Professor Emmanuel Mamatzakis |
Date Deposited: | 12 Oct 2023 11:26 |
Last Modified: | 12 Oct 2023 11:27 |
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URI: | https://mpra.ub.uni-muenchen.de/id/eprint/118785 |