A stochastic model for predicting the response time of green vs brown stocks to climate change news risk
构建了一个随机模型，将新闻事件持续时间视为逆高斯分布，用于预测绿色和棕色公司股票价格对气候变化新闻风险的响应时间，并验证了模型在日度和高频数据上的有效性，有助于提高风险管理指标的精度。
We model the dynamic evolution of the attention process over the duration of climate change news events as a Brownian motion with an absorbing barrier, where attention to the news event ceases. In this framework, the duration of the underlying news event is a random variable whose probability distribution is the Inverse Gaussian (IG). We show that the IG distribution of news duration can be used to predict the response time of asset prices to climate news risk. We test the empirical validity of our model by constructing two novel climate news duration data sets: a daily duration and an hour-by-hour intra-news duration. At the daily frequency, our model predicts the response time of green versus brown firms’ stock prices to climate news risk. We demonstrate how this response time can enhance the precision of conventional risk management statistics, e.g., Value at Risk and expected shortfall, and in consequence improves the efficiency of managing firms’ exposures to such risk. At the high frequency, we extend the autoregressive conditional duration (ACD) model and show that, in an IG-ACD-GARCH framework, climate change news arrivals contribute to the volatility of green (but not brown) firms’ returns. This finding is attributed to public and investors’ concerns about climate change or to their belief that climate transition policies are ineffective in combating climate change.