Abstract:
In this paper, we examine the potential spillovers between returns, volatility, skewness and kurtosis of developed stock markets under the lenses of rare disaster events, proxied by climate risks. The aforementioned moments based on model-implied distributions of stock returns are derived from the quantile autoregressive distributed lag mixed-frequency data sampling (QADL-MIDAS) method, using a long span of data. The empirical findings are as follows: firstly, spillovers are significant within- and across stock markets for each of the four moments. Secondly, based on a nonparametric causality-in-quantiles approach, changes in temperature anomalies, have the predictive power to shape the entire conditional distribution of various metrics of spillover involving single- and multiple-layers of returns and risks layers. In sum, we show that the multi-layer approach offers a comprehensive and nuanced view of how stock market-related information is transmitted across the stock markets of advanced economies, carrying implications for investors and policymakers.
