Abstract

This paper attempts to estimate the relationship between oil prices and financial stress using weekly data for the period December 31, 1993 to July 15, 2016. The analysis is carried out using the cointegration framework. Both the linear and non-linear models for cointegration and related error correction models are estimated. The paper finds the threshold cointegration model more suitable than the linear cointegration models. It finds evidence of asymmetry in the adjustment process to equilibrium. It also finds that regimes with negative (below the threshold) changes of deviations adjust much faster than regimes with positive (above the threshold) changes of deviations, especially during a crisis period. Also, bi-direction causality is reported between the two variables.

Highlights

  • In the literature, it is well recognized that oil price shocks have detrimental effect on economic activity in developed and developing countries (Cunado and de Gracia, 2003; Cunado and de Gracia, 2005; Hamilton, 2011), especially for oil-importers

  • Three observations can be made (i) Oil prices and STLFSI have an evident comovement in general, which reveals a high possibility of cointegration between these two series. (ii) oil prices and STLFSI move together most of the time during our sample period, they display divergent movement indicating possible nonlinear cointegration. (iii) The two series tend to move more closely during and after the crisis relative to the pre-crisis period

  • We investigated the dynamic relationship between oil prices and financial stress over the period from December 31, 1993 to July 15, 2016

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Summary

Introduction

It is well recognized that oil price shocks have detrimental effect on economic activity in developed and developing countries (Cunado and de Gracia, 2003; Cunado and de Gracia, 2005; Hamilton, 2011), especially for oil-importers. The effect of oil shocks on the macro-economy seems to have weakened through time, Kilian (2008) argued that this is partly due to increased demand for industrial output, which offsets the negative impact of an increase in oil price. Most studies use market data, but some of them use both mixed market and balance sheet data (Holló et al, 2012), while others consider only balance sheet data (Morales and Estrada, 2010)

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