진학프로석·박사 채용 접수 플랫폼

This project aims to develop an advanced framework for identifying Climate Policy Uncertainty (CPU) shocks, leveraging high-frequency financial data and narrative-based approaches. While existing studies focus on specific sectors, this research examines macro-financial effects in the aggregate, assessing whether CPU differs from traditional uncertainty sources.

To achieve this, we plan to analyze movements around key policy announcements, modeling CPU through implied and realized volatility using daily data on RGGI (Regional Greenhouse Gas Initiative) and CCA (California Carbon Allowance) prices, as well as ICE Carbon Futures returns.

We will implement two alternative identification strategies. The first, following Badinger and Schiman (2023), adopts a narrative-based approach, linking CPU shocks to historical climate events. The second (as in Antolín-Díaz and Rubio-Ramírez, 2018), applies sign and narrative restrictions within a Bayesian SVAR framework. These complementary methodologies will ensure the robustness of results, making them less dependent on a specific identification strategy. A further contribution is the construction of a novel climate policy uncertainty index, which aims to provide a more granular and timely indicator of climate-related uncertainty.

Finally, we aim to assess how CPU shocks amplify financial stress, credit spreads, GDP, inflation, and interest rates, offering insights into climate policy risks and their transmission to the broader economy, contributing to the growing literature on climate-related risks.