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A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of the FTSE and S&P500 Indexes
Open accessAbstract
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.
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“Allen and McAleer (2021) applied the NARDL model to show that negative economic shocks have more substantial and lasting effects on global financial indicators.”
From Passenger and Freight Demand under Macroeconomic Volatility: Evidence from South Korea and The United States (2026) · cites this paper as Allen and McAleer (2021)
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Cites
Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework
Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014) · Festschrift in Honor of Peter Schmidt · doi:10.1007/978-1-4899-8008-3_9 · Cited by 3,835
Research works printed but not linked: 33
- Allen, David E., and McAleer, Michael (2020). A Nonlinear Autoregressive Distributive Lag Analysis (NARDL) of West Texas Intermediate Oil Prices and the Dow Jones Index. Energies, 13. · DOI
- Balke, 1997, Threshold cointegration, International Economic Review, 38, 627, 10.2307/2527284 · DOI
- Banerjee, 1998, Error-correction mechanism tests for cointegration in a single-equation framework, Journal of Time Series Analysis, 19, 267, 10.1111/1467-9892.00091 · DOI
- Black, Fischer (1976). Studies of Stock Price Volatility Changes. Proceedings of the Business and Economics Section, American Statistical Association.
- Braun, 1995, Good news, bad news, volatility and betas, Journal of Finance, 50, 1575, 10.1111/j.1540-6261.1995.tb05189.x · DOI
- Chen, 2018, A dynamic analysis of S&P 500, FTSE 100 and EURO STOXX 50 indices under different exchange rates, PLoS ONE, 13, e0194067, 10.1371/journal.pone.0194067 · DOI
- Caporale, 2016, Linkages between the US and European stock markets: A fractional cointegration approach, International Journal of Finance and Economics, 21, 143, 10.1002/ijfe.1537 · DOI
- Christie, 1982, The stochastic behavior of common stock variances: Value, leverage and interest rate effects, Journal of Financial Economics, 3, 407, 10.1016/0304-405X(82)90018-6 · DOI
- Delatte, Anne-Laure, and López-Villavicencio, Antonia (2011). Asymmetric Exchange Rate Pass-Through: Evidence from Major Economies, Rouen University Business School. · DOI
- Dickey, 1979, Distribution of the estimators for autoregressive time series with a unit root, Journal of the American Statistical Association, 74, 427
- Engle, 1987, Co-integration and error correction: Representation, estimation and testing, Econometrica, 55, 251, 10.2307/1913236 · DOI · Paper page
- Fama, 1965, The behaviour of stock market prices, Journal of Business, 38, 34, 10.1086/294743 · DOI
- Fama, 1970, Efficient capital markets: A review of theory and empirical work, Journal of Finance, 25, 383, 10.2307/2325486 · DOI · Paper page
- Figlewski, Steven, and Wang, Xiaozu (2000). Is the “Leverage Effect” a Leverage Effect?, University and Fudan University. Unpublished Working Paper. · DOI
- Glosten, 1992, On the relation between the expected value and the volatility of the nominal excess return on stocks, Journal of Finance, 48, 1779, 10.1111/j.1540-6261.1993.tb05128.x · DOI
- Granger, Clive W. J., and Yoon, Gawon (2002). Hidden Cointegration, University of California San Diego. · DOI
- 2013, Common and idiosyncratic disturbances in developed small open economies, Journal of International Economics, 90, 33, 10.1016/j.jinteco.2012.10.002 · DOI
- Hansen, 1995, Rethinking the univariate approach to unit root tests: How to use covariates to increase power, Econometric Theory, 11, 1148, 10.1017/S0266466600009993 · DOI
- Hasanhodzic, Jasmina, and Lo, Andrew W. (2021, April 14). Black’s Leverage Effect Is Not Due to Leverage. Available online: https://ssrn.com/abstract=1762363.
- Johansen, 1988, Statistical analysis of cointegration vectors, Journal of Economic Dynamics and Control, 12, 231, 10.1016/0165-1889(88)90041-3 · DOI · Paper page
- Kapetanios, 2006, Testing for cointegration in nonlinear smooth transition error correction models, Econometric Theory, 22, 279, 10.1017/S0266466606060129 · DOI
- Kasa, 1992, Common stochastic trends in international stock markets, Journal of Monetary Economics, 29, 95, 10.1016/0304-3932(92)90025-W · DOI
- Konker, 2001, Quantile Regression, Journal of Economic Perspectives, 15, 143, 10.1257/jep.15.4.143 · DOI
- Kremers, 1992, The power of cointegration tests, Oxford Bulletin of Economics and Statistics, 54, 325, 10.1111/j.1468-0084.1992.tb00005.x · DOI
- Kwiatkowski, 1992, Testing the null hypothesis of stationarity against the alternative of a unit root, Journal of Econometrics, 54, 159, 10.1016/0304-4076(92)90104-Y · DOI · Paper page
- Natsiopoulos, Kleanthis, and Tzeremes, Nickolaos (2021, April 14). ARDL: ARDL, ECM and Bounds-Test for Cointegration. Available online: https://CRAN.R-project.org/package=ARDL. · DOI
- Nguyen, Viet Hoang, and Shin, Yongcheol (2010). Asymmetric Price Impacts of Order Flow on Exchange Rate Dynamics, Leeds University Business School. Mimeo. · DOI
- Pesaran, 2001, Bounds testing approaches to the analysis of level relationships, Journal of Applied Econometrics, 16, 289, 10.1002/jae.616 · DOI · Paper page
- Psaradakis, 2004, On Markov error-correction models with an application to stock prices and dividends, Journal of Applied Econometrics, 19, 69, 10.1002/jae.729 · DOI
- Phillips, 1990, Statistical inference in instrumental variables regression with I(1) processes, Review of Economic Studies, 57, 99, 10.2307/2297545 · DOI
- Schwert, 1989, Why Does Stock Market Volatility Change over Time?, Journal of Finance, 44, 1115, 10.1111/j.1540-6261.1989.tb02647.x · DOI
- Summers, 1986, Does the stock market rationally reflect fundamental values, Journal of Finance, 41, 591, 10.1111/j.1540-6261.1986.tb04519.x · DOI
- Taylor, 1989, The internationalisation of stock markets and the abolition of U.K. exchange control, The Review of Economics and Statistics, 71, 332, 10.2307/1926980 · DOI
Cited by
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Jung, N. (2026) · Transport and Telecommunication Journal · doi:10.2478/ttj-2026-0011 · Cited by 1
“Allen and McAleer (2021) applied the NARDL model to show that negative economic shocks have more substantial and lasting effects on global financial indicators.”
Metadata
- Authors
- Allen, D., & McAleer, M.
- Affiliations
- Edith Cowan University (AU)
- Universidad Complutense de Madrid (ES)
- Corresponding author
- Allen, David
- Venue
- Risks
- Publisher
- MDPI AG
- Published
- 2021-11-03
- Volume
- 9
- Issue
- 11
- Pages
- 195
- ISSN
- 2227-9091 (ISSN-L)
- OpenAlex
- W3209176190
- Cited by
- 35
- References
- 1
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 9 · 2023: 7 · 2022: 3
- FWCI
- 3.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Risks (publishedVersion, cc-by)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion, cc-by)
- Australasian Journal of Paramedicine (submittedVersion, cc-by)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Econstor (Econstor) (submittedVersion, cc-by)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Risk and Volatility Modeling
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Market Dynamics and Volatility
- Keywords
- FTSE 100, S&P 500, stock market linkages, nonlinear autoregressive distributed lag, asymmetric price effects, Brexit, COVID-19 market impact, weak-form market efficiency, quantile regression, daily stock prices
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