Abstract
Economic globalization promotes closer economic ties among countries, and may also lead to a wider spread of economic risks. It is of great significance to study the transmission mechanism of financial risk. In this paper, the multi-scale tail risk transmission mechanism of Chinese and Russian stock market based on spatiotemporal Kriging model is studied. In this paper, the stock market of China and Russia is taken as the research object. Based on the Kriging model of time and space, the variance decomposition is studied. In this paper, we find that the maximum value of Ru share is 2.035% in the results of variance decomposition of SH series and 0.861% in the results of variance decomposition of Ru series. The multi-scale tail risk of Russian stock market has a relatively significant impact on Chinese market, while the multi-scale tail risk of Chinese stock market has a relatively low impact on Russian market.
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References
Jang, J., Lee, J.M., Cho, S.G., et al.: Space-time Kriging surrogate model to consider uncertainty of time interval of torque curve for electric power steering motor. IEEE Trans. Magn. 54(3), 1–4 (2018)
Sahoo, M., Das, T., Kumari, K., et al.: Space-time forecasting of groundwater level using a hybrid soft computing model. Int. Assoc. Sci. Hydrol. Bull. 62(4), 561–574 (2017)
Du, Z., Wu, S., Kwan, M.P., et al.: A spatiotemporal regression-Kriging model for space-time interpolation: a case study of chlorophyll-a prediction in the coastal areas of Zhejiang, China. Int. J. Geogr. Inf. Sci. 32(9–10), 1927–1947 (2018)
Cui, J.: The space–time distribution of soil water and temperature of a desert ecosystem using spatio-temporal Kriging and PCA analysis. J. Indian Soc. Remote Sens. 48(2), 271–286 (2020)
Yueheng, Z., Xinqi, Z., Zhenhua, W., et al.: Implementation of a parallel GPU-based space-time Kriging framework. ISPRS Int. J. Geo Inf. 7(5), 193 (2018)
Wang, F.Y., Xu, Y.L., Zhan, S.: Multi-scale model updating of a transmission tower structure using Kriging meta-method. Struct. Control Health Monit. 24(8), e1952.1–e1952.16 (2017)
Dorratoltaj, N., Nikin-Beers, R., Ciupe, S.M., et al.: Multi-scale immunoepidemiological modeling of within-host and between-host HIV dynamics: systematic review of mathematical models. PeerJ 5(5758), e3877 (2017)
Corzo, A., Gamboa, N.: Environmental impact of mining liabilities in water resources of Parac micro-watershed, San Mateo Huanchor district, Peru. Environ. Dev. Sustain. 20(2), 939–961 (2018)
Nisani, D.: Portfolio selection using the Riskiness Index. Stud. Econ. Finance 35(2), 330–339 (2018)
Wang, F.Y., Xu, Y.L., Qu, W.L.: Multi-scale failure analysis of transmission towers under downburst loading. Int. J. Struct. Stab. Dyn. 18(8), 1850029 (2017)
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Xiao, C., Xia, W., Jiang, J. (2021). Multi-scale Tail Risk Transmission Mechanism of Chinese and Russian Stock Market Based on Spatiotemporal Kriging Model. In: Atiquzzaman, M., Yen, N., Xu, Z. (eds) Big Data Analytics for Cyber-Physical System in Smart City. BDCPS 2020. Advances in Intelligent Systems and Computing, vol 1303. Springer, Singapore. https://doi.org/10.1007/978-981-33-4572-0_153
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DOI: https://doi.org/10.1007/978-981-33-4572-0_153
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