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

Abstract

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.

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Notes

  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: https://www.sydneyludvigson.com/data-and-appendixes

  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 www.policyuncertainty.com 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 www.stoxx.com 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.

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Correspondence to Christopher Thiem.

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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.

Appendix

Appendix

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
figure10

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

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Thiem, C. Cross-Category, Trans-Pacific Spillovers of Policy Uncertainty and Financial Market Volatility. Open Econ Rev 31, 317–342 (2020). https://doi.org/10.1007/s11079-019-09559-1

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Keywords

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

JEL Classification

  • C32
  • D80
  • F42
  • G18