Cross-Category, Trans-Pacific Spillovers of Policy Uncertainty and Financial Market Volatility


Using generalised variance decompositions from vector autoregressions, we analyse cross-country, cross-category spillovers of economic policy uncertainty (EPU) and financial market volatility between the US and Japan. Our model includes indices of monetary, fiscal and trade policy uncertainty for each country, as well as three measures of option-implied stock market and exchange rate volatility, respectively. We find that the financial market volatility indices are usually substantial net spillover transmitters towards the total group of EPU measures. However, the Japanese equity and especially the FX volatility index are typically more affected by EPU spillovers than the US VXO. Our results also reveal that, compared to within-country spillovers, cross-country spillovers of EPU are relatively small and less volatile. Finally, we show that the direction of net EPU spillovers between the US and Japan is both time- and category-dependent with different EPU categories acting as strong sources of uncertainty spillovers throughout the sample period.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9


  1. 1.

    Our choice of the US and Japan as objects of study is largely determined by the limited availability of category-specific EPU measures for other countries. However, as these are the world’s two largest developed economies, their relationship is also fundamentally of interest.

  2. 2.

    Adopting a relatively broad view of the concept, we define uncertainty as the (generally perceived) absence of information about the exact state of the economy in the future. We, therefore, do not distinguish between risk and uncertainty in the sense of Knight (1921), as both phenomena are synonymous with a lack of such information (cf. Bloom 2014; Thiem 2019).

  3. 3.

    Krol (2014) also highlights the need to control for general economic uncertainty when investigating the effects of EPU on other financial variables such as foreign-exchange market volatility, for instance.

  4. 4.

    Liow et al. (2018) also investigate the link between a cross-country EPU spillover index and a cross-financial market volatility spillover index they compute and find weak evidence for the former leading the latter. However, since their framework does not allow for direct EPU-volatility spillovers, they are only looking at third-round spillovers at best, and at generally biased results at worst.

  5. 5.

    Other examples of studies on the link between EPU and FX volatility are Krol (2014) and Kido (2016).

  6. 6.

    Since our volatility measures also capture many facets of uncertainty, there may be indeed various potential triggers and channels of transmission behind the observed cross-category, cross-type and cross-country spillovers (see also Thiem 2018). Political bargaining and conflict, the effects of households’ financial well-being on their voting behaviour, and the ability (or obligation) of policymakers to respond to economic or political developments across the Pacific are just a few of the examples that could be named in this context. While we provide explanations for most of the notable spillover effects found in the paper, we have to leave it to future research to more systematically analyse the processes by which uncertainty spillovers generally arise.

  7. 7.

    By including measures of MPU, FPU and TPU, we arguably cover the policy categories that are the most relevant for the two countries’ economies and their economic relationship. We decide against the inclusion of other EPU indices or sub-categories, as these variables are only available for one of the two countries, which raises potential concerns regarding the cross-country balance and comparability of our spillover indices. In some cases, there are also non-negligible technical barriers. Arbatli et al.’s (2017) exchange rate policy uncertainty index, for instance, shows many isolated jumps, making it difficult to reconcile the index with the DY framework’s assumptions – see the next section and JPN_FX in Appendix Figure 10.

  8. 8.

    The VIX index as the “most commonly used proxy for overall economic uncertainty” (Baker et al. 2016, p. 1620) is not computed before 1990. However, during the available period, the VXO and the VIX correlate at 0.99 on a monthly basis.

  9. 9.

    The monthly correlations of the computed realised volatilities and the option-implied indices are 0.83 for the Nikkei 225 stock market index and 0.81 for the Yen/USD exchange rate, respectively.

  10. 10.

    This notation implies that we condition on the information set Ωt – see also Thiem (2018).

  11. 11.

    Consequently, the sum of all bilateral (and multilateral) net uncertainty spillovers is equal to zero: \( {\sum}_{i,j=1,\kern0.5em i\ne j}^N{C}_{ij}={\sum}_{i=1}^N{C}_i=0 \).

  12. 12.

    Mechanically, the SOI is defined as the ratio of the sum of θg’s off-diagonal elements to the sum of all its elements (cf. DY 2016): \( C=\left[\frac{\sum_{i,j=1,\kern0.5em i\ne j}^N{\overset{\sim }{\theta}}_{ij}^g(H)}{\sum_{i,j=1}^N{\overset{\sim }{\theta}}_{ij}^g(H)}\right]\cdotp 100 \).

  13. 13.

    However, as shown in Section 6, this has hardly any influence on our results.

  14. 14.

    See also Table 2 in the appendix for a numerical representation.

  15. 15.

    See also Table 2 in the appendix for a numerical representation.

  16. 16.

    When taken together, however, the EPU variables account for 21.8% of the VXO’s total variation over the forecast horizon.

  17. 17.

    As this trend appears to begin close to the most recent US presidential election in November 2016, our results are generally consistent with the view that the surprising election result injected further uncertainty into the global economy (see, e.g., Bown 2017, Thiem 2018, 2019). Other than that, however, the reactions of our computed spillover measures turn out to be relatively muted, suggesting that the Trump election did not cause any immediate Trans-Pacific uncertainty spillovers that were sufficiently large to affect the Japanese economy.

  18. 18.

    By July 2005 all three option-implied volatility indices had declined to multi-year lows.

  19. 19.

    Note that, while the impact on the JYVIX is strongest and most persistent, we also find increased net spillovers from Japanese FPU to the US and Japanese stock markets during the 1998–2000 period.

  20. 20.

    Halfway through the cycle (August 2003), the Within US connectedness index reaches a local peak of 10.6%.

  21. 21.

    By including an aggregate index for European EPU in the system, we rule out the possibility that this result is somehow driven by the outcome of the Brexit referendum, which sent large waves of uncertainty through the global economy in June/July 2016. See Section 6 for further details.

  22. 22.

    We use the one-month forward looking versions of the indices. The data is available from Sydney Ludvigson’s website:

  23. 23.

    To a certain extent, these results are counterintuitive, as the aim of the Jurado et al. indices is to capture a broader set of information than the conventional, stock-market based uncertainty measures and to use this information more efficiently. We would thus expect these variables to act as even stronger net sources of uncertainty spillovers than the US VXO. Since the opposite is the case, we suspect that their strictly model-based method may be too restrictive and filter out some of the relevant information. This would also explain why the net influence of the Japanese EPU measures appears larger after the VXO is replaced. Moreover, while the econometric merits of their approach may be debated, it is highly unlikely that economic agents, including policymakers, filter and weight the available information in the exact same manner that is assumed by Jurado et al.’s models (see, e.g., Shiller 2003). Unlike the VXO, which is derived from real market transactions, the researchers’ indices may thus be quite detached from economic agents’ actual perceptions of uncertainty.

  24. 24.

    Since category-specific EPU measures are not available for Europe, we use Baker et al.’s (2016) aggregate European EPU index. Apart from not providing categorical information, one drawback of this measure is that it is not based on the same group of countries over the entire sample period – see for further details. Regarding European stock market volatility, we use the VSTOXX for the period from 1999 onwards and the standardised realized volatility of the Eurostoxx 50 for the earlier part of the sample. We obtain data on the former from and data on the latter from Yahoo Finance.

  25. 25.

    See also Table 3 in the appendix.

  26. 26.

    We obtain similar results for our dynamic analysis: While the intensity of spillovers among the US and Japanese variables is generally weaker after including the European uncertainty measures, the direction and evolution of the time-varying spillover indices hardly changes.


  1. Amengual D, Xiu D (2018) Resolution of policy uncertainty and sudden declines in volatility. J Econ 203(2):297–315

    Article  Google Scholar 

  2. Arbatli, E., Davis, S., Ito, A., Miake, N. and Saito, I. (2017). Policy uncertainty in Japan. NBER working paper, no. 23411

  3. Azzimonti M (2017) Partisan conflict and private investment. J Monet Econ 93:114–131

    Article  Google Scholar 

  4. Baker S, Bloom N, Canes-Wrone B, Davis S, Rodden J (2014) Why has US policy uncertainty risen since 1960? American Economic Review: Papers & Proceedings 104(5):56–60

    Article  Google Scholar 

  5. Baker S, Bloom N, Davis S (2016) Measuring economic policy uncertainty. Q J Econ 131(4):1593–1636

    Article  Google Scholar 

  6. Balli F, Uddin G, Mudassar H, Yoon S (2017) Cross-country determinants of economic policy uncertainty spillovers. Econ Lett 156:179–183

    Article  Google Scholar 

  7. Beckmann J, Czudaj R (2017) Exchange rate expectations and economic policy uncertainty. Eur J Polit Econ 47:148–152

    Article  Google Scholar 

  8. Belke A, Osowski T (2019) International effects of euro area versus U.S. policy uncertainty: a FAVAR approach. Econ Inq 57(1):453–481

    Article  Google Scholar 

  9. Belke A, Dubova I, Osowski T (2018) Policy uncertainty and international financial markets: the case of Brexit. Appl Econ 50(34–35):3752–3770

    Article  Google Scholar 

  10. Bloom N (2009) The impact of uncertainty shocks. Econometrica 77(3):623–685

    Article  Google Scholar 

  11. Bloom N (2014) Fluctuations in uncertainty. J Econ Perspect 28(2):153–176

    Article  Google Scholar 

  12. Bown C (2017) Introduction. In: Bown C (ed) Economics and policy in the age of trump. CEPR Press, London, UK, pp 9–20

    Google Scholar 

  13. Brogaard J, Detzel A (2015) The asset-pricing implications of government economic policy uncertainty. Manag Sci 61(1):3–18

    Article  Google Scholar 

  14. Clausen V, Schlösser A, Thiem C (Forthcoming) Economic policy uncertainty in the euro area: cross-country spillovers and macroeconomic impact. Journal of Economics and Statistics

  15. Colombo V (2013) Economic policy uncertainty in the US: does it matter for the euro area? Econ Lett 121:39–42

    Article  Google Scholar 

  16. Davis, S. J. (2017). Regulatory complexity and policy uncertainty: headwinds of our own making. Becker Friedman Institute for Research in economics working paper, no. 2723980, 29 April

  17. Diebold FX, Yilmaz K (2009) Measuring financial asset return and volatility spillovers, with application to global equity markets. Econ J 119(534):158–171

    Article  Google Scholar 

  18. Diebold FX, Yilmaz K (2012) Better to give than to receive: predictive directional measurement of volatility spillovers. Int J Forecast 28(1):57–66

    Article  Google Scholar 

  19. Diebold FX, Yilmaz K (2014) On the network topology of variance decompositions: measuring the connectedness of financial firms. J Econ 182(1):119–134

    Article  Google Scholar 

  20. Diebold FX, Yilmaz K (2015) Financial and macroeconomic connectedness: a network approach to measurement and monitoring. Oxford University Press, New York, US

    Google Scholar 

  21. Diebold FX, Yilmaz K (2016) Trans-Atlantic equity volatility connectedness: U.S. and European financial institutions, 2004-2014. Journal of Financial Econometrics 14(1):81–127

    Google Scholar 

  22. Diebold, F. X., Liu, L. and Yilmaz, K. (2017). Commodity connectedness. NBER working paper, no. 23685

  23. Funke M, Schularick M, Trebesch C (2016) Going to extremes: politics after financial crises, 1870-2014. Eur Econ Rev 88:227–260

    Article  Google Scholar 

  24. Husted L, Rogers J, Sun B (2016) Measuring cross country monetary policy uncertainty. Federal Reserve Board IFDP note, pp 2016–11-23

  25. Iwaisako T (2014) Comparing fiscal problems in Japan and the United States. In: Cline, W., Fukao, K., Iwaisako, T., Kuttner, N., Posen, A. and Schott, J. (eds.). Lessons from decades lost: economic challenges and opportunities facing Japan and the United States, pp. 43–55. Peterson Institute for International Economics, briefing, no. Washington D.C, US, pp 14–14

    Google Scholar 

  26. Jacomy M, Venturini T, Heymann S, Bastian M (2014) ForceAtlas2, a continuous graph layout algorithm for Handy network visualization designed for the Gephi software. PLoS One 9(6):Article Number e98679

    Article  Google Scholar 

  27. Kido Y (2016) On the link between the US economic policy uncertainty and exchange rates. Econ Lett 144:49–52

    Article  Google Scholar 

  28. Klößner S, Sekkel R (2014) International spillovers of policy uncertainty. Econ Lett 124(3):508–512

    Article  Google Scholar 

  29. Knight F (1921) Risk, uncertainty and profit. Reprint: Augustus M. Kelly. US, New York, p 1964

    Google Scholar 

  30. Koop G, Pesaran H, Potter S (1996) Impulse response analysis in nonlinear multivariate models. J Econ 74:119–147

    Article  Google Scholar 

  31. Krol R (2014) Economic policy uncertainty and exchange rate volatility. International Finance 17(2):241–255

    Article  Google Scholar 

  32. Kuttner K (2014) Monetary policy during Japan’s great recession: from self-induced paralysis to Rooseveltian resolve. In: Cline, W., Fukao, K., Iwaisako, T., Kuttner, N., Posen, A. and Schott, J. (eds.). Lessons from decades lost: economic challenges and opportunities facing Japan and the United States, pp. 66–80. Peterson Institute for International Economics, briefing, no. Washington D.C, US, pp 14–14

    Google Scholar 

  33. Kuttner K, Posen A (2002) Fiscal policy effectiveness in Japan. Journal of the Japanese and International Economies 16:536–558

    Article  Google Scholar 

  34. Lanne M, Nyberg H (2016) Generalized forecast error variance decomposition for linear and nonlinear multivariate models. Oxf Bull Econ Stat 78(4):595–603

    Article  Google Scholar 

  35. Lawrence R (2002) In: Frankel J, Orszag P (eds) American Economic Policy in the 1990sInternational trade policy in the 1990s. MIT Press, Cambridge, US, pp 277–327

    Google Scholar 

  36. Liow K, Liao W-C, Huang Y (2018) Dynamics of international spillovers and interaction: evidence from financial market stress and economic policy uncertainty. Econ Model 68:96–116

    Article  Google Scholar 

  37. Mikitani, R. and Kuwayama, P. (1998). Japan’s new central banking law: a critical view. Center on Japanese economy and business, Columbia business school, working paper, no. 145

  38. Nodari G (2014) Financial regulation policy uncertainty and credit spreads in the US. J Macroecon 41:122–132

    Article  Google Scholar 

  39. Pastor L, Veronesi P (2013) Policy uncertainty and risk premia. J Financ Econ 110:520–545

    Article  Google Scholar 

  40. Pesaran H, Shin Y (1998) Generalized impulse response analysis in linear multivariate models. Econ Lett 58:17–29

    Article  Google Scholar 

  41. Shiller R (2003) From efficient markets theory to behavioral finance. J Econ Perspect 17(1):83–104

    Article  Google Scholar 

  42. Thiem C (2018) Cross-category spillovers of economic policy uncertainty. Ruhr Economic Papers:744

  43. Thiem C (2019) In: University of Duisburg-Essen (ed) Empirical essays on the influence of uncertainty in the world economy. Unpublished Doctoral Dissertation, Essen, Germany

  44. Yin L, Han L (2014) Spillovers of macroeconomic uncertainty among major economies. Appl Econ Lett 21(13):938–944

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Christopher Thiem.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Thanks for valuable comments are due to the journal’s editors and anonymous reviewers as well as the participants of the 17th Annual EEFS Conference, June 2018, London, UK.



Table 1 Index construction & data sources
Table 2 Full-sample uncertainty spillover table
Table 3 Full-sample uncertainty spillover table including European Indices
Fig. 10

Distributions of the EPU and volatility indices before and after the data transformation

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Thiem, C. Cross-Category, Trans-Pacific Spillovers of Policy Uncertainty and Financial Market Volatility. Open Econ Rev 31, 317–342 (2020).

Download citation


  • Economic policy uncertainty
  • Exchange rate volatility
  • Japan
  • Spillovers
  • Stock market volatility
  • United States
  • Vector autoregression

JEL Classification

  • C32
  • D80
  • F42
  • G18