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A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of the FTSE and S&P500 Indexes

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  • David E. Allen

    (School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia
    Department of Finance, College of Management, Asia University, Taichung City 41354, Taiwan
    School of Business and Law, Edith Cowan University, Joondalup, WA 6027, Australia)

  • Michael McAleer

    (Department of Finance, College of Management, Asia University, Taichung City 41354, Taiwan
    Department of Bioinformatics and Medical Engineering, College of Information and Electrical Engineering, Asia University, Taichung City 41354, Taiwan
    Discipline of Business Analytics, University of Sydney Business School, Darlington, NSW 2006, Australia
    Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, 3062 PA Rotterdam, The Netherlands)

Abstract

The paper features an examination of the link between the behaviour of the FTSE 100 and S&P500 Indexes in both an autoregressive distributed lag ARDL, plus 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 daily FTSE adjusted price series, commencing in April 2009 and terminating in March 2021, and a corresponding daily S&P500 Index adjusted-price series obtained from Yahoo Finance. The data period includes all the gyrations caused by the Brexit vote in the UK, beginning with the vote to leave in 2016 and culminating in the actual agreement to withdraw in January 2020. It was then followed by the impact of the global spread of COVID-19 from the beginning of 2020. The results of the analysis suggest that movements in the contemporaneous levels of daily S&P500 Index levels have very significant effects on the behaviour of the levels of the daily FTSE 100 Index. They also suggest that negative movements have larger impacts than do positive movements in S&P500 levels, and that long-term multiplier impacts take about 10 days to take effect. These effects are supported by the results of quantile regression analysis. A key result is that weak form market efficiency does not apply in the second period.

Suggested Citation

  • David E. Allen & Michael McAleer, 2021. "A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of the FTSE and S&P500 Indexes," Risks, MDPI, vol. 9(11), pages 1-20, November.
    • Handle: RePEc:gam:jrisks:v:9:y:2021:i:11:p:195-:d:671113
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    References listed on IDEAS

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    1. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    2. Delatte, Anne-Laure & López-Villavicencio, Antonia, 2012. "Asymmetric exchange rate pass-through: Evidence from major countries," Journal of Macroeconomics, Elsevier, vol. 34(3), pages 833-844.
    3. David E. Allen & Michael McAleer, 2020. "A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of West Texas Intermediate Oil Prices and the DOW JONES Index," Energies, MDPI, vol. 13(15), pages 1-11, August.
    4. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
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    Cited by:

    1. Jiecheng Song & Merry Ma, 2023. "Climate Change: Linear and Nonlinear Causality Analysis," Stats, MDPI, vol. 6(2), pages 1-17, May.
    2. Youxue Jiang & Zakia Batool & Syed Muhammad Faraz Raza & Mohammad Haseeb & Sajjad Ali & Syed Zain Ul Abidin, 2022. "Analyzing the Asymmetric Effect of Renewable Energy Consumption on Environment in STIRPAT-Kaya-EKC Framework: A NARDL Approach for China," IJERPH, MDPI, vol. 19(12), pages 1-15, June.
    3. Koushik Mandal & Radhika Prosad Datta, 2024. "Oil Price Dynamics and Sectoral Indices in India – Pre, Post and during COVID Pandemic: A Comparative Evidence from Wavelet-based Causality and NARDL," International Journal of Economics and Financial Issues, Econjournals, vol. 14(4), pages 18-33, July.
    4. Kleanthis Natsiopoulos & Nickolaos G. Tzeremes, 2024. "ARDL: An R Package for ARDL Models and Cointegration," Computational Economics, Springer;Society for Computational Economics, vol. 64(3), pages 1757-1773, September.
    5. Deng, Xiang & Xu, Fang, 2024. "Asymmetric effects of international oil prices on China's PPI in different industries——Research based on NARDL model," Energy, Elsevier, vol. 290(C).
    6. Victoria Foye, 2022. "Climate Change and Macro Prices in Nigeria: A Nonlinear Analysis," Managing Global Transitions, University of Primorska, Faculty of Management Koper, vol. 20(2 (Summer), pages 167-203.

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    More about this item

    Keywords

    NARDL; bounds tests; ARDL; FTSE; asymmetries; multiplier effects; S&P500;
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