A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of West Texas Intermediate Oil Prices and the DOW JONES Index

David E Allen ,Michael Mcaleer

doi:10.2139/ssrn.3371704

Abstract

The paper features an examination of the link between the behaviour of oil prices and DowJones Index in a nonlinear autoregressive distributed lag NARDL framework. The attraction of NARDL is that it represents the simplest method available of modelling combined short- and long-run asymmetries. The bounds testing framework adopted means that it can be applied to stationary and non-stationary time series vectors, or combinations of both. The data comprise a monthly West Texas Intermediate (WTI) crude oil series from FRED, commencing in January 2000 and terminating in February 2019, and a corresponding monthly DOW JONES index adjusted-price series obtained from Yahoo Finance. Both series are adjusted for monthly USA CPI values to create real series. The results of the analysis suggest that movements in the lagged real levels of monthly WTI crude oil prices have very significant effects on the behaviour of the DOW JONES Index. They also suggest that negative movements have larger impacts than positive movements in WTI prices, and that long-term multiplier effects take about 9 to 12 months to take effect.

Highlights

The paper explores the link between oil prices and Dow Jones Index in a nonlinear autoregressive distributed lag (NARDL) framework
Shin et al [1] extend the work in this area, and provide a dynamic framework that is both simple and flexible, nonlinear, and capable of simultaneously and coherently modelling asymmetries. These are present in both the underlying long-run relationship and in dynamic adjustment. They derive the dynamic ECM associated with asymmetric long-run cointegrating regression to the nonlinear autoregressive distributed lag (NARDL)
In the process we provide a validation and application of the nonlinear autoregressive distributed lag NARDL framework as developed by Shin et al [1] in relation to this topic

Summary

Introduction

The paper explores the link between oil prices and Dow Jones Index in a nonlinear autoregressive distributed lag (NARDL) framework. Shin et al [1] extend the work in this area, and provide a dynamic framework that is both simple and flexible, nonlinear, and capable of simultaneously and coherently modelling asymmetries These are present in both the underlying long-run relationship and in dynamic adjustment. They derive the dynamic ECM associated with asymmetric long-run cointegrating regression to the nonlinear autoregressive distributed lag (NARDL) They follow Pesaran et al [21] and use a bounds testing approach to test for a stable long-run relationship. They derive asymmetric cumulative dynamic multipliers that permit the display of the asymmetric adjustment patterns following positive and negative shocks to the explanatory variables.

The Links between Oil Prices and Stock Markets

Econometric Model—The Nardl Approach

Preliminary Analysis

Nardl Analysis

Statistic

Conclusions