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The extreme spillover from climate policy uncertainty to the Chinese sector stock market: wavelet time-varying approach

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Abstract

This study investigates the extreme return connectedness between five major Chinese stock prices and climate uncertainty between March 2010 and June 2022. A novel wavelet time-varying parameter quantile vector Autoregression is employed. The results show that climate uncertainty depresses investment predominantly in normal periods while altering the lead-lag direction among these sector classes during turmoil periods. The results provide significant implications for investors and policymakers concerned with stock prices.

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Notes

  1. Details can be found at: https://www.iea.org/reports/global-energy-review-2021.

  2. Due to space limitation, the mathematical steps for wavelets methodology are eschewed here, and only the concepts and definitions useful for our purposes are introduced. The reader interested in the theory and use of wavelets may refer to Percival and Walden (2000).

  3. Due to space limitation, only the results of the short run estimation are presented here. Other frequencies are available upon request.

  4. For robustness checks, we estimate the W-TVP-QVAR model with 5-, 15-, and 24-step-ahead GFEVD, and we conduct the above analyses using an alternative quantile. The results from the alternative inputs and model settings are qualitatively similar and available upon request.

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  1. Department of Economics, Mutah University, Karak, Jordan

    Huthaifa Sameeh Alqaralleh

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  1. Huthaifa Sameeh Alqaralleh
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Alqaralleh, H.S. The extreme spillover from climate policy uncertainty to the Chinese sector stock market: wavelet time-varying approach. Lett Spat Resour Sci 16, 31 (2023). https://doi.org/10.1007/s12076-023-00352-w

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