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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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  • Huthaifa Sameeh Alqaralleh

    (Mutah University)

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.

Suggested Citation

  • Huthaifa Sameeh Alqaralleh, 2023. "The extreme spillover from climate policy uncertainty to the Chinese sector stock market: wavelet time-varying approach," Letters in Spatial and Resource Sciences, Springer, vol. 16(1), pages 1-17, December.
    • Handle: RePEc:spr:lsprsc:v:16:y:2023:i:1:d:10.1007_s12076-023-00352-w
    DOI: 10.1007/s12076-023-00352-w
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    Cited by:

    1. Mo, Bin & Nie, He & Zhao, Rongjie, 2024. "Dynamic nonlinear effects of geopolitical risks on commodities: Fresh evidence from quantile methods," Energy, Elsevier, vol. 288(C).

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    More about this item

    Keywords

    Climate uncertainty; Quantile connectedness; W-Q-TVP-VAR;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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