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Assessing systemic risk and its determinants for advanced and major emerging economies: the case of ΔCoVaR

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Abstract

The paper examines conditional risk relationships among sovereign CDS prices and stock market indices for 11 economies with particular relevance for international portfolio investment holdings (Canada, China, Brazil, France, Germany, Italy, Japan, Russia, Spain, the USA, and the UK). The analysis is based on delta conditional value at risk (ΔCoVaR). The UK, France, and Italy significantly contribute to the overall systemic risk in both markets. The USA, the UK, and Russia appear to be important contributors to it in the stock market. In the meantime, the advanced economies exhibit much higher resilience to the systemic risk propagation in comparison with China, Brazil and Russia. Gross government debt to GDP, state fragility index, EU membership and world gross GDP share of a country in distress are key determinants of ΔCoVaRs for the sovereign CDS prices. Stock market total value traded to GDP and world gross GDP share of a country in distress drive ΔCoVaRs in the stock market. In both cases geographic distance tends to deter systemic risk propagation. Inflation, trade and financial openness as well as common language and time zone differences are less important predictors of bilateral ΔCoVaR exposures.

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Notes

  1. It is hard to assert that the systemic risk measures are spotlessly accurate. There is evidence that CoVaR as well as MES (marginal expected shortfall) and SRISK proposed by Acharya et al. (2010) and Brownlees and Engle (2015) are sometimes outperformed by the metrics directly related to firms’ balance sheets, e.g., Benoit et al. (2013); Benoit (2014), and Idier et al. (2014). Nonetheless, CoVaR appears to be ahead of other indicators, judging by the number of citations in Google Scholar (1040 citations for Adrian and Brunnermeier (2011) paper vs. 800 for Acharya et al. (2010) and 343 for Brownlees and Engle (2015) as of early May 2015).

  2. This is the baseline approach to CoVaR estimation as proposed by Adrian and Brunnermeier (2011). However, they also recognize that CoVaR exposures can be obtained from GARCH models, which, however, involves more intermediate computations, e.g., Girardi and Tolga Elgün (2013). The advantages of using quantile regressions lie in their relative simplicity and robustness in assessing relations between the variables at extremely high (low) percentiles rather than means. Besides, they are not demanding with respect to the distributional properties of data. The quantile regressions are not reported here but their outputs are available upon request.

  3. As for sovereign CDS prices, the indicator is positive unless there is a statistically significant negative correlation between the CDS series of country i and j at high quantiles. Conversely, when stock market prices are considered, ΔCoVaR iΙj q value should be negative by definition. In this case, its absolute value is referred to for the general rule to hold.

  4. All the data are retrieved from Bloomberg. 5-year sovereign CDS contracts tend to be more liquid compared to the contracts with other (e.g., 3- or 10-year) durations. The following stock market indices are used: BOVESPA (Brazil), S&P/TSX (Canada), SSE Composite (China), CAC 40 (France), DAX (Germany), FTSE MIB (Italy), Nikkei 225 (Japan), IBEX 35 (Spain), MICEX (Russia), FTSE (the UK), Dow Jones Industrial (the USA). ΔCoVaR exposures are estimated on weekly data to check the robustness of the baseline (daily) results.

  5. The Netherlands, Luxemburg and the Cayman Islands which were also listed among top-10 international portfolio investors/recipients in 2009–2012 are not considered due to their offshore status which makes net contribution to the global systemic risk hardly discernible. India as a major emerging market is not considered for data availability reasons as it has not issued international bonds denominated in foreign currencies and, hence, there are no sovereign CDS contracts for India. The CDS contracts for the State Bank of India which is the biggest state-owned financial institution are not an adequate proxy for the sovereign credit risk as their pricing may be heavily influenced by bank-specific issues, e.g., temporary liquidity shortages.

  6. Only statistically significant ΔCoVaRs will be regressed on country specific predictors.

  7. A higher level of trade openness may exacerbate systemic risk due to the balance of payment identities. A country deeply involved in trade should also experience significant capital flows. Consequently, if this country is in a financial distress, its impact on international financial markets is likely to be stronger.

  8. EU countries, Russia, China, and Japan incorporate information with one day lag not only from the US market but also from Canada and Brazil. Baumӧhl and Vẏrost (2010) also suggest that Japanese and Chinese markets should lag one calendar day behind EU counterparts to convey information flows correctly.

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Authors and Affiliations

  1. Department of Applied Economics, Moscow State Institute of International Relations (MGIMO–University), 76 Vernadskogo prospect, Moscow, 119454, Russia

    Mikhail Stolbov

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  1. Mikhail Stolbov
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Correspondence to Mikhail Stolbov.

Appendices

Appendix A

Table 7 ΔCoVaR for changes in sovereign CDS spreads (in basis points), daily, 01.01.2010–28.02.2014 (with asymmetric responses and asynchronous trading effect)
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Table 8 ΔCoVaR for changes in sovereign CDS spreads (in basis points), weekly, 01.01.2010–28.02.2014 (with asymmetric responses)
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Table 9 ΔCoVaR for changes in stock market indices, daily, 01.01.2010–28.02.2014 (with asymmetric responses and asynchronous trading effect)
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Table 10 ΔCoVaR for changes in stock market indices, weekly, 01.01.2010–28.02.2014 (with asymmetric responses)
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Appendix B

Table B1

Table 11 Determinants of ΔCoVaRs for changes in sovereign CDS spreads
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Table B2

Table 12 Determinants of ΔCoVaRs for changes in stock market indices
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Stolbov, M. Assessing systemic risk and its determinants for advanced and major emerging economies: the case of ΔCoVaR. Int Econ Econ Policy 14, 119–152 (2017). https://doi.org/10.1007/s10368-015-0330-2

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