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On the relationship between Asian exchange rates and stock prices: a nonlinear analysis

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Abstract

This paper examines the relationship between stock prices and exchange rates for the ASEAN5 plus the Big3. While previous studies have assumed a symmetric relationship between stock prices and exchange rates, this paper introduces nonlinearity in the relationship between the two variables. The empirical model used is the nonlinear autoregressive distributed lag (NARDL) model, which introduces nonlinearity by differentiating between the increases and decreases in the independent variable. The results show that although both the linear ARDL model and the NARDL model suggest that the relationship between the variables is mainly short-term, the NARDL model produces relatively more evidence supporting asymmetric long-run relationship between stock prices and exchange rates.

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Notes

  1. In a study by Klitgaard (1999), looking at U.S imports from Japan during the 1990s, found that when the dollar depreciated by 10%, the price of Japanese imports rose by only 6%. This suggests that Japanese exporters take a 4% offsetting change in their profit margin on exports.

  2. From July 1997 to December 1997, the currencies of Indonesia, Japan, Korea, Malaysia, the Philippines, Singapore, and Thailand had depreciated against the U.S dollar by nearly 44%, 9%, 47%, 32%, 28%, 12%, and 32%, respectively. During the same time period, stock markets in these countries dropped by about 44%, 25%, 48%, 41%, 29%, 22%, and 44%, respectively. China is the exception during this period of time as the renminbi appreciated against the U.S dollar by 0.12%, and the stock market rose by about 0.36%. Source: authors’ own calculations.

  3. Source: the World Bank Development Indicators Database, https://databank.worldbank.org/source/world-development-indicators.

  4. Source: the Bank for International Settlements, https://stats.bis.org/statx/srs/table/d11.3.

  5. The U.S came in the first place accounting for 24.42 percent. Source: World Bank Data, https://databank.worldbank.org/data/download/GDP.pdf.

  6. Shin et al. (2013) argue that due to the dependence structure between the partial sums $${e}_{t}^{+}$$ and $${e}_{t}^{-}$$, the exact value of the number of lags $$(k)$$ is not clear. However, they argue that $${e}_{t}^{+}$$ and $${e}_{t}^{-}$$ should be treated as one variable.s.

  7. Differences between our results and those of previous studies could be due to a number of reasons. (1) Model specification (for example, linear versus nonlinear model): using different models is likely to produce different results. (2) Sample size and data frequency: using different sample sizes and/or data frequency (annual versus quarterly versus monthly data) can produce different results. (3) Lag selection method: using different information criteria (e.g., AIC versus SIC) to select the optimal number of lags can lead to different results when testing for unit root, cointegration, or estimating a VAR model. Thus, using different models, different sample size, or different information criteria to select the number lags, can lead to differences in results.

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Acknowledgements

We are deeply grateful to two anonymous referees for their valuable suggestions and comments. The usual disclaimer applies.

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Authors and Affiliations

  1. Department of Economics, Lazaridis School of Business and Economics, Wilfrid Laurier University, 64 University Avenue West, Waterloo, ON, N2L 3C7, Canada

    Salah A. Nusair

  2. Department of Economics and Finance, Gulf University for Science and Technology, P.O. Box 7207, 32093, Hawally, Kuwait

    Jamal A. Al-Khasawneh

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Appendix

Appendix

1.1 Data source

All data are monthly. Stock price indices are closing prices and are extracted from Thomson Reuters Datastream. Nominal effective exchange rates are extracted from the International Financial Statistics of the International Monetary Fund (https://data.imf.org/?sk=4C514D48-B6BA-49ED-8AB9-52B0C1A0179B&sId=1390030341854) and from the Bank of International Settlements (https://www.bis.org/statistics/eer.htm). The following table provides the range of the sample for each country and the name of the stock market price index:

Country

Sample

Stock market index name

China

01/1991 to 12/2019

SHANGHAI SE Composite

Indonesia

01/1994 to 12/2019

IDX Composite

Japan

06/1973 to 12/2019

NIKKEI 225

Korea

12/1974 to 12/2019

KOSPI Composite Index

Malaysia

01/1980 to 12/2019

FTES BURSA MALAYSIA KLCI

Philippines

01/1986 to 12/2019

PHILIPPINES SE (PSEi)

Singapore

03/1995 to 12/2019

STRAITS TIMES Index (STI)

Thailand

01/1994 to 12/2019

BANGKOK S.E.T

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Nusair, S.A., Al-Khasawneh, J.A. On the relationship between Asian exchange rates and stock prices: a nonlinear analysis. Econ Change Restruct 55, 361–400 (2022). https://doi.org/10.1007/s10644-021-09318-8

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