Gauging the level of contemporaneous and lagged linkages between climate policy uncertainty and green asset: novel insights from deep learning for a time-varying VAR model
摘要
Climate change policy uncertainty can serve as a catalyst for policy transformation by intensifying the urgency of implementing climate policies and influencing green asset investment decisions. Our paper applies Deep Learning for a time-varying vector autoregression (VAR) model (DL-TVP-VAR) to prove interlinkages between climate policy uncertainty and green assets from September 2020 to September 2024. The results demonstrate that climate policy uncertainty consistently serves as a major net shock transmitter, exerting significant influence on climate bonds, the blue economy, and environmental services, especially in the periods before 2022 and after 2023. Meanwhile, environmental opportunities and green bonds primarily function as net shock receivers, with only brief instances of surpassing climate policy uncertainty. Additionally, climate bonds, low-carbon energy, and the blue economy remain steady net receivers of shocks throughout the analyzed timeframe. These findings highlight the crucial need to closely monitor climate policy uncertainty to strengthen stability and resilience across environmentally oriented investment sectors. This thorough analysis lays the groundwork for better informed investment choices and successful risk management techniques by offering insightful information about the green asset investments.