Supply Bottlenecks and Machine Learning Forecasting of International Stock Market Volatility

The objective of this paper is to forecast volatilities of the stock returns of China, France, Germany, Italy, Spain, the United Kingdom (UK), and the United States (US) over the daily period of January 2010 to February 2025 by utilizing the information content of newspapers articles-based indexes of supply bottlenecks. We measure volatility by employing the interquantile range, estimated via an asymmetric slope autoregressive quantile regression fitted on stock returns to derive the conditional quantiles. In the process, we are also able to obtain estimates of skewness, kurtosis, lower- and upper-tail risks, and incorporate them into our linear predictive model, alongside leverage. Based on the shrinkage estimation using the Lasso estimator to control for overparameterization, we find that the model with moments outperform the benchmark model that includes both own- and cross-country volatilities, but the performance of the former, is improved further when we incorporate the role of the metrics of supply constraints for all the 7 countries simultaneously. These findings carry significant implications for investors.