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Financial vs. Policy Uncertainty in Emerging Market Economies

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Abstract

While the negative effect of uncertainty shocks on economic activity is well documented in many empirical studies, little is known about the extent to which the effect differs across various kinds of uncertainty, especially in the emerging market economy context. Using the newly available economic policy uncertainty index from six emerging market economies (Brazil, Chile, China, India, Korea, and Russia), we compare the impact of financial uncertainty shocks—measured by stock market volatility—and that of policy uncertainty shocks on the economy. We find that financial uncertainty shocks have much larger and more significant impact on output than policy uncertainty shocks, except for China where the government has direct controls over financial market activity. While our finding differs from the existing studies about advanced economies that find no smaller effects of policy uncertainty shocks on output than financial uncertainty shocks, it is consistent with the recent emphasis on financial frictions as a propagation mechanism of uncertainty shocks.

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Notes

  1. We do not intend to summarize the mounting literature about uncertainty and economic activity. See Bloom (2014) for a comprehensive review of the literature.

  2. For example, during the recent episodes of the U.K.’s referendum to leave the European Union and the U.S. presidential election, uncertainty regarding economic policy increased dramatically to the unprecedented level, whereas uncertainty about financial markets, measured by the VIX, remained at the low level.

  3. See, for example, the recent studies on the effect of uncertainty shocks on the exchange rates in small open economies (Bhattarai et al. 2017; Choi 2017, 2018). However, we did not include the exchange rate in the VARs in the earlier version of the paper and reached the same conclusion. To conserve space, these results are available upon request.

  4. For example, Choi (2017) shows that the effect of uncertainty shocks measured by implied volatility on output is near identical to that measured by realized volatility for the U.S. economy. We also find quantitatively similar results from the common subsamples.

  5. For example, in the case of Korea, they use six newspapers to construct the EPU index: Donga Ilbo, Kyunghyang Shinmun, Maeil Business Newspaper (from 1990), Hankyoresh Shinmun, Hankook Ilbo, and the Korea Economic Daily (from 1995). They calculate the number of news articles that considers the following terms relative to the entire news articles: uncertain or uncertainty; economic, economy or commerce; and one or more of the following policy-relevant terms: government, “Blue House”, congress, authorities, legislation, tax, regulation, “the Bank of Korea”, “central bank”, deficit, WTO, law/bill or “ministry of finance.” After the standardization of each paper’s EPU to unit standard deviation during the sample period, they average across the papers by month and then rescale the resulting series to a mean of 100. For further details about the construction of the EPU index of other countries, see Baker et al. (2016) and www.policyuncertainty.com.

  6. While we do not include a trend term in the baseline model, our results hardly change when including a linear trend term in the VARs. To save space, the results are available upon request.

  7. Using the log level of the uncertainty indices do not affect the results.

  8. Akaike Information Criterion (AIC) suggests four lags (Russia), three lags (Brazil and Korea), and two lags (Chile, China, India) while the Schwarz’ Bayesian Information Criterion (SBIC) suggests only one lag for the six EMEs.

  9. In this analysis, we do not include the financial uncertainty index for comparison with Baker et al. (2016). To recover orthogonal shocks, Baker et al. (2016) use a Cholesky decomposition with the following ordering: the EPU index, the log of the S&P500 index, the federal funds rate, log employment, and log industrial production using three lags.

  10. 90% confidence intervals are plotted using 200 bootstraps.

  11. See Shin et al. (2018) for a similar finding about the muted response of Korean output to policy uncertainty shocks.

  12. See Choi (2018) for the theoretical mechanism through which uncertainty shocks raise the short-term interest rate in EMEs where credit market imperfections are prevalent.

  13. By no means, we do not claim that policy uncertainty does not affect economic activity. Vast theoretical and empirical evidence found that policy uncertainty dampens economic activity (Aizenman and Marion 1993; Handley and Limao 2015). We simply find that the results from VARs using the Korean EPU index give little support to the claim that economic slowdowns in Korea are attributed to the heightened policy uncertainty.

  14. Reversing the ordering between the two uncertainty indices only strengthens our conclusion that financial uncertainty shocks are a far more important business cycle driver than policy uncertainty shocks.

  15. The nominal effective exchange rate is used here so that a decrease in the index denotes a nominal depreciation. The insignificant response of the Chinese exchange rate to both types of uncertainty shocks is expected because China maintained the fixed exchange rate regime for the most of the sample period. Even after China moved to the managed floating regime, its exchange rate is only allowed to float within a very narrow band.

  16. A seemingly strange response of Russian output to policy uncertainty shocks is likely due to the positive correlation (0.25) between oil prices and the Russian EPU index for the period 1994M1-2015M12 and the high reliance of the Russian economy on oil exports. See Antonakakis et al. (2014) for the spillovers between oil prices and economic policy uncertainty.

  17. Relaxing the small open economy assumption and letting the data free to speak regarding this assumption do not change the main results.

  18. The HP-filtering parameter is 129,600 in this case.

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Acknowledgments

The previous version of the paper was circulated under the title “Financial vs. Policy Uncertainty in Emerging Economies: Evidence from Korea and the BRICs.” We have greatly benefited from the extensive comments and suggestions by the editor and two anonymous referees. We would like to thank Hie Joo Ahn, Nina Biljanovska, Yan Carriè re-Swallow, Yongsung Chang, Minchul Shin, Taeyoon Sung, Ling Zhu, and seminar participants at Korean Econometric Society Summer Meeting for their helpful comments and suggestions. Youngkeun Choi provided excellent research assistance. This work was supported (in part) by the Yonsei University Future-leading Research Initiative of 2017 (2017-22-0152). All remaining errors are ours.

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Authors and Affiliations

  1. School of Economics, Yonsei University, Seoul, South Korea

    Sangyup Choi

  2. School of Economics, Sogang University, Seoul, South Korea

    Myungkyu Shim

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  1. Sangyup Choi
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Correspondence to Sangyup Choi.

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Choi, S., Shim, M. Financial vs. Policy Uncertainty in Emerging Market Economies. Open Econ Rev 30, 297–318 (2019). https://doi.org/10.1007/s11079-018-9509-9

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