CARDI – Carbon Risk Dynamics Indicator
CARDI resolves the carbon premium puzzle by tracking how climate policies and emission intensities drive systemic volatility in carbon contingent assets.
Spikes in CARDI precede reversals in return premia, revealing when regulatory uncertainty amplifies heavy-tailed losses in carbon-intensive sectors.
About
CARDI captures systemic, time-varying carbon risk by predicting relative tail-event measures between high- and low-carbon firms. As CARDI rises, it signals strengthening demand for green assets and erodes the traditional high-risk–high-return pattern observed in carbon-intensive sectors.
Using Chinese daily market data and 7 macroeconomic factors from 2014–2025, CARDI anticipates shifts in the low-carbon premium, offering investors and asset managers a practical tool to integrate climate-related financial risks into pricing and portfolio decisions.
CARDI𝜏=5%, SCARDI𝜏=5% (Smooth CARDI) and Climate-related policy announcement day
𝜏 = quantile level
Macro variable contribution to CARDI
Based on the Shapley values, the most important features that drive CARDI are carbon quota price volatility of the Guangdong and Shenzen markets and the TED spread (difference between the 3-month Shibor and the 3-month treasury rate)
References
Ideas, Papers, Theory & Code used in our project
Measuring Carbon Risk Dynamics, Working Paper (2025)
Wang R, Härdle WK, Zhang L
Financial Risk Meters in Taiwan’s High-Cap Sectors, Working Paper (2024)
Jheng SL, Teng HW, Härdle WK
Financial Risk Meter For The Romanian Stock Market (2023)
Romanian Journal of Economic Forecasting
Pele DT, Conda AL, Bag RC, Mazurencu-Marinescu-Pele M, Strat VA
Applied Economics
Nguyen PA, Nguyen NP, Le HMD
Quantitative Finance
Wang R, Potì V, Härdle WK
Financial Risk Meter FRM based on Expectiles (2022)
Journal of Multivariate Analysis
Ren R, Lu MJ, Li Y, Härdle WK
Financial Risk Meter for Cryptocurrencies and Tail-Risk Network Based Portfolio Construction (2022)
The Singapore Economic Review
Ren R, Althof M, Härdle WK
Financial Risk Meter for emerging markets (2022)
Research in International Business and Finance
Amor SB, Althof M, Härdle WK
LASSO-Driven Inference in Time and Space (2021)
The Annals of Statistics
Chernozhukov V, Härdle WK, Huang C, Wang W
FRM Financial Risk Meter (2020)
The Econometrics of Networks
Mihoci A, Althof M, Chen CYH, Härdle WK
An AI approach to Measuring Financial Risk (2020)
Singapore Economic Review
Yu L, Härdle WK, Borke L, Benschop T
TENET: Tail-Event driven NETwork risk (2016)
Journal of Econometrics
Wolfgang Karl Härdle, Weining Wang, Lining Yu