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Economic Policy Uncertainty and Volatility Spillovers Among International Stock Market Indices During the COVID-19 Outbreak

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Abstract

Using a sample of 16 international stock market indices spanning the period of January 2015 to June 2022, we examine how global equity markets interact with respect to volatility spillover, with a special focus on types of investment horizons, and how the connectedness structure evolves during the COVID-19 outbreak. Empirical results suggest that there is strong evidence of volatility spillovers among global stock markets, and the COVID-19 pandemic further strengthens such volatility spillovers. However, the structure of the frequency connectedness changes gradually when compared to the full sample period. We further investigate if economic policy uncertainty (EPU) affects volatility spillovers among global stock markets. The results suggest that EPU significantly affects the connectedness among global stock markets, particularly during the COVID-19 pandemic period. Overall, the findings suggest that volatility spillovers across international stock markets vary with time horizons and market conditions, which contributes to the academic literature on modelling global volatility spillovers. Practically, the findings of the study contribute to investors and policymakers in adjusting trading strategies and monitoring market risks.

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Notes

  1. Several special issues on the economic and financial impacts of COVID-19 have been arranged and appeared in a large number of reputable economics and finance journals (i.e., Finance Research Letters, International Review of Financial Analysis, Journal of Economics and Business, etc.) over the past few years.

  2. In this study, we interchangeably use the term “spillover” and “connectedness”.

  3. In this paper, we adopt three measures appropriate to describe stock return variances, including realized variance, realized semi-variance, and bi-power variation to account for microstructure noise and jumps in stock price process, with the availability of tick-level data. Liu et al. (2015) show that the choice of 5-min sampling frequency can largely circumvent the impact of market microstructure noise. Therefore, all the realized volatility measures are calculated by rolling sampling of transaction price series at 5-min frequency (i.e., 5-min sub-sampled).

  4. For highly correlated financial variables such as stock market returns and volatilities we examine in this study, the variance–covariance matrix \(\Sigma\) is not necessarily a diagonal matrix, so that the shocks in \({\varepsilon }_{t}\) are allowed to be contemporaneously correlated. As the values of the entries in each row of the \({\Phi }_{H}\) matrix may not add up to one, each entry is then normalized by scaling the sum of the entries in each row.

  5. For more details about the frequency-domain modelling approach, please refer to Baruník and Krehlík (2018).

  6. In the robustness check, we also use weekly measure for volatility connectedness index with realized variance (RV), realized semi-variance (RSV) and bi-power variation (BV) in time–frequency domain.

  7. GEPU is a GDP-weighted average of national EPU indices for 20 countries: Australia, Brazil, Canada, Chile, China, France, Germany, Greece, India, Ireland, Italy, Japan, Mexico, the Netherlands, Russia, South Korea, Spain, Sweden, the United Kingdom, and the United States (Baker et al., 2016, 2022). The EPU index is provided at monthly frequency, while our spillover indices are calculated weekly. Therefore, we interpolate the monthly EPU index to weekly frequency by cubic spline.

  8. To measure volatility, we utilize realized variance (RV), realized semi-variance (RSV), and bi-power variation (BV), all calculated based on rolling sampling price series at 5-min intervals. However, due to space limitation, we omit the results for RSV and BV, which are available upon request by contacting the authors.

  9. Due to space limitation, we omit the results for RSV and BV, which are available upon request by contacting the authors.

  10. Although not reported for reasons of brevity, we perform several robustness tests to ensure the validity of our results. First, we perform pooled OLS regressions for all stock markets examined in this study, where we include the two main effects as well as the interaction term, and find qualitatively similar results. Second, we use the connectedness index with realized semi-variance (RSV) and bi-power variation (BV) as our dependent variables and obtain similar results for both the full sample and the subsample of the COVID-19 period. Third, we utilize the overall index as well as the frequency-specific connectedness indices as the dependent variables and draw similar conclusions.

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Acknowledgements

Fei Su acknowledges financial support from the National Natural Science Fund of China (Grant No. 71901087) and the Fundamental Research Funds for the Central Universities (No. NR2022004). Yahua Xu acknowledges financial support under the Program for Innovation Research at Central University of Finance and Economics. All errors are our own.

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Authors and Affiliations

  1. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Fei Su

  2. School of Finance, Shanghai University of Finance and Economics, Shanghai, 200433, China

    Feifan Wang

  3. China Economics and Management Academy, School of Innovation and Development, Central University of Finance and Economics, Beijing, 100081, China

    Yahua Xu

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  1. Fei Su
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  2. Feifan Wang
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  3. Yahua Xu
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Correspondence to Fei Su.

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Appendices

Appendix 1

See Appendix Tables 6, 7, 8, and 9. 

Table 6 List of stock market indices
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Table 7 Volatility spillover measures for RSV and BV—DY method
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Table 8 Spillover measures for volatility before and during the COVID-19 outbreak—RV
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Table 9 Volatility spillover measures for RV—LASSO method
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Appendix 2: code for LASSO regression

figure a

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Su, F., Wang, F. & Xu, Y. Economic Policy Uncertainty and Volatility Spillovers Among International Stock Market Indices During the COVID-19 Outbreak. Asia-Pac Financ Markets (2024). https://doi.org/10.1007/s10690-024-09452-z

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