Dynamic distances between stock markets: use of uncertainty indices measures

Carlos A. Acuña; Catalina Bolancé Bolancé; Salvador Torra

doi:10.26360/2021_3

Authors

Carlos A. Acuña Universidad de Barcelona (España)
Catalina Bolancé Bolancé Universidad de Barcelona (España)
Salvador Torra Universidad de Barcelona (España)

DOI:

https://doi.org/10.26360/2021_3

Keywords:

markets distances, uncertainty index, financial crisis, spatial dependence, stock market

Abstract

In this study we consider how to more accurately identify the possible impact of systemic risk on spatial dependence related to the most significant financial crises over the last 17 years: the Lehman Brothers bankruptcy, the sub-prime mortgage crisis, the European debt crisis, Brexit and the COVID-19 pandemic which has also affected the financial markets. We analyse two new dynamic distances applied to stock markets based on exogenous criteria known as the World Uncertainty Index (WUI) and our proposed Google Trends Uncertainty Index (GTUI). We address the feasibility and benefits of these dynamic distances compared to an alternative criterion based on hours. Using the new proposed dynamic distance to obtain the Moran’s I statistic, we analyse the spatial dependence between the losses of 46 stock markets.

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References

Acuña, C. A., Bolancé, C., and S. Torra. (2018). Análisis de la dependencia espacial entre índices bursátiles. Anales del Instituto de Actuarios Españoles, 24, 79–97.

Ahir, H., Bloom, N., and D. Furceri. (2019). The World Uncertainty Index. Technical Report 19-027, SIEPR-Stanford Institute for Economic Policy Research.

Asgharian, H., Hess, W., and Liu. L. (2013). A spatial analysis of international stock market linkages. Journal of Banking & Finance, 37(12), 4738- 4754.

Baker, S. R., Bloom, N., and Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593-1636.

Bolancé, C., and Acuña, C.A. (2021). A new kernel estimator of copulas based on beta quantile transformations. Mathematics, 9, URL: https://www.mdpicom/2227-7390/9/10/1078.

Castelnuovo, E., and Tran, T. D. (2017). Google it up! a google trends-based uncertainty index for the United States and Australia. Economics Letters, 161, 149-153.

Flavin, T., Hurley, M. J., and Rousseau, F. (2002). Explaining stock market correlation: A gravity model approach. Manchester School, 70, 87-106.

Ghirelli, C., Pérez, J. J., and Urtasun, A. (2019). A new economic policy uncertainty index for Spain. Economics Letters, 182, 64-67.

Griffith, D. A. (2010). The Moran Coefficient for non-normal data. Journal of Statistical Planning and Inference, 140, 2980-2990.

McNeil, A. J., Frey, R., and Embrechts, P. (2015). Quantitative Risk Management: Concepts, Techniques and Tools. Revised edition. Economics Books, Princeton University Press, edition 2, number 10496.

Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37, 17-23.

Weinberg, L. (2020). The Google Trends Uncertainty (gtu) Index: A measure of economic policy uncertainty in the EUu using google trends. Undergraduate Economic Review, 17: Article 2.