Figure B.1. Climate risk and word selection robustness. The red markers report the mean estimate from using the main climate risk indexes and the baseline climate-augmented regression described in Section 3.2. The green markers report the mean estimate, in addition to the lower and upper 2.5 and 97.5 percentile, from over 30000 random leave-one-out simulations of the underlying climate risk indexes.
(a) Climate-augmented
(b) Thresholds
Figure B.2. Climate risk and smoothing robustness. For each country the figures report the estimated climate risk coefficients from (5) and (6) using different degrees of smoothing when constructing CR.
The main results are produced using seven-month moving average (Smoothing window: 7). The 95%
confidence intervals are computed using HAC-corrected standard errors.
Figure B.3. Climate risk and commodity price index robustness. For each country the figures report the estimated climate risk coefficients from (5) and (6) using three different definitions of the commodity price index provided by Gruss and Kebhaj (2019). The main results are produced using net-export shares and a rolling window for the weights (Net Export Index, Rolling). The 95% confidence intervals are computed using HAC-corrected standard errors.
Figure B.4. Common components. The figure reports the first principal component estimate from the cross-sectional residuals from (4) (black) and from the individual climate risk indexes (green). The common residual and climate risk components explain 28 and 42 percent of the variation in the data, respectively.
(a) Norway (b) Mexico
(c) Malaysia (d)Canada
(e) Australia (f ) South Africa
(g) Brazil (h) Russia
Figure B.5. Climate risk (green) and temperature anomalies (red). The dotted lines report the raw series. The solid lines report the data when a Hodrick–Prescott filter (Hodrick and Prescott(1997)), with a smoothing parameter set to 1600, is used to extract the low-frequency fluctuations from the series.
Figure B.6. Gas, oil, and coal production relative to GDP. For each country, the figure reports a standard box plot of the production shares for the period 2002 to 2019. The underlying data is sourced from British Petroleum Company(2020).
Figure B.7. Exchange rates, commodity prices and common and idiosyncratic climate risk estimates.
The figure reports the results from estimating (5) using both the common (CC CR) and idiosyncratic (CSC CR) climate risk variables. The 95% confidence intervals are computed using HAC-corrected standard errors.
(a)Norway (b) Mexico (c) Malaysia (d)Canada
(e) Australia (f ) South Africa (g) Brazil (h) Russia
Figure B.8. Climate Change Performance Indexes (CCPI). The CCPIs report each country’s rank and are produced by Germanwatch since 2005. The statistics are sampled on a yearly frequency, and we assume that the rank stays the same within each year.
Figure B.9. Exchange rates, commodity prices and climate risk estimates for alternative climate risk variables. For each country, the figure reports the results from estimating the threshold regression in (6), using one of the following: our proposed measure of climate risk (Climate risk), the CCPIs (CCPI), the climate risk measure suggested by Engle et al. (2020) (Engle et al.), or abnormal temperature changes (Temperature anomalies). The 95% confidence intervals are computed using HAC-corrected standard errors.
(a)Norway (b)Mexico (c) Malaysia (d)Canada
(e) Australia (f ) South Africa (g) Brazil (h)Russia
Figure B.10. VAR and climate risk responses. Each graph reports the CR response following a one standard deviation exogenous innovation to either the REER (solid line) or commodity prices (dotted line). The innovations are computed from a system where the climate risk variable is ordered first in the system. 95% confidence bands are constructed using a residual bootstrap.
(a)Climate risk shock (b)Temperature anomaly shock
Figure B.11. VAR and REER variance decompositions. Each graph reports how much of the variance in the REER is explained by a one standard deviation exogenous innovation to either the climate risk variable or the temperature anomaly series. The results are computed using a recursive ordering, where the temperature anomalies and climate risk variable are ordered first and second in the system, respectively.
(a) Norway (b) Mexico (c) Malaysia (d)Canada
(e) Australia (f ) South Africa (g) Brazil (h)Russia
Figure B.12. VAR and REER responses, controlling for the stock market. Each graph reports the REER response following a one standard deviation exogenous innovation to the climate risk variable.
The innovations are computed from the same VAR as used in Figure 8, with the climate risk variable ordered first in the system. The mean response paths are reported with a dotted line. 95% confidence bands are constructed using a residual bootstrap. For comparison the benchmark responses, from Figure 6, are reported with a solid line.
(a) Energy (b) Material (c) Industry (d)Cons. Disc. (e) Cons. Stapl.
(f ) Health (g)Finance (h) IT (i) Telecom. (j)Utilities
Figure B.13. Industry returns and factor loadings. The change in climate change transition risk ∆CRt
is related to the return (minus the risk free rate) of industry portfolioj by:
rtj =µj+β1jM Rt+β2jSM Bt+β3jHM Lt+β4jU M Dt+β5jLIQt+β6j∆ComXt+β7j∆CRt+utj where M Rt, SM Bt, HM Lt, M ONt, and LIQt are the traditional market (MR), size (SMB), book-to-market (HML), momentum (UMD), and liquidity (LIQ) risk factors (Fama and French(1993),Jegadeesh and Titman (1993), Carhart(1997), and Pastor and Stambaugh (2003). See Odegaard (2017) for the construction of these for the Norwegian market.) The equation is estimated using a 5-year rolling window.
I.e., the estimates reported in, e.g., 2007, reflect the average relationship in the period 2002 to 2007. The
(a) Climate-augmented
(b) Threshold
Figure B.14. Exchange rates, commodity prices and climate risk estimates with additional controls.
For each country, the figure reports the results from estimating the climate-augmented regressions in (5) or (6), augmented with either remaining reserves, shale-oil growth, or temperature anomalies. The 95%
confidence intervals are computed using HAC-corrected standard errors.
Figure B.15. Exchange rates, commodity prices and climate risk estimates. Baseline regression (equa-tion (5)) and double selec(equa-tion results. The 95% confidence intervals are computed using HAC-corrected standard errors. The double selection is computed as follows: First, the REER, CR, andComX×CR variables are regressed separately on all the variables in the augmented ˜Xct vector using the LASSO estimator (Tibshirani (1996)). 100 different penalization parameters together with the BIC are used to tune the amount of regularization. Next, after these three penalized regressions, the REERs are regressed on (using OLS)CR,ComX×CR, and the union of the control variables selected in step one.
(a) Norway (b) Mexico (c) Malaysia (d)Canada
(e) Australia (f ) South Africa (g)Brazil (h)Russia
Figure B.16. VAR estimates with extra control variables and REER responses. Each graph reports the REER response following a one standard deviation exogenous innovation to the climate risk variable.
The climate risk variable is ordered last in the system. The VAR includes the variables inytin addition to one of the following: remaining reserves, shale-oil production growth, (global) uncertainty measures denotedV IXComandV IER, country-specific and total OECD composite leading indicators. The dotted black line and 95% confidence bands represent the “benchmark” results presented in Figure6. The other lines represent the mean REER response path from each of the alternative model specifications.
Table B.1. Climate risk and commodity market correlations. The table reports the correlation between the common component of climate risk and relative prices of gas, oil, and coal, and the global consumption of the same commodities. The correlations between climate risk and prices are computed using monthly time series. The global consumption growth statistics are collected from British Petroleum Company (2020), and are sampled on a yearly frequency and measured in Exajoules. *, **, and *** denote the 10%, 5%, and 1% significance level, respectively.
Relative prices Relative global consumption Gas−Oil Gas−Coal Oil−Coal Gas−Oil Gas−Coal Oil−Coal
Common climate risk component -0.09 -0.13* -0.11 0.59*** 0.66*** -0.01
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