Since the monthly counting of the Geopolitical Risk Index (GPR) started in 1985, the index peaked in ************, immediately after the 9/11 terrorist attack on the World Trade Center and Pentagon in the United States. The attack is perceived to be the deadliest terrorist attack in the 20th and 21st century, and ultimately caused the start of the so-called war on terror, with U.S. invasions of Afghanistan (2001) and Iraq (2003) following in the aftermath. Russia-Ukraine war The GPR was also high in ********** following Russia's invasion of Ukraine at the end of February that year. The attack on an independent state meant that the relations between Russia and the West reached a new low after the collapse of the Soviet Union, and several sanctions were imposed on Russia. 1991: a turbulent year Apart from the 9/11 attacks in 2001, the index reached its highest level in ************. This was a result of the ongoing Gulf War following Iraq's invasion of Kuwait, but also Soviet troops storming the Lithuanian capital in order to stop the country's secession from the Soviet Union. Additionally, a massacre of Tutsi in Rwanda highlighted the growing tensions in the East African country, which ultimately resulted in the genocide in 1994.

OverviewThis archive contains the files to reproduce the results in "Measuring Geopolitical Risk" as well as any additional documentation referred in the paper. Each directory is self-contained. For each directory, download all the files in order to run the necessary scripts. Instructions are given in the README files.Updated data can be found on the geopolitical risk index webpage, which can be found at the following url: https://www.matteoiacoviello.com/gpr.htm For questions or comments, please contact iacoviel@gmail.comData Availability StatementAll the data used in this paper are provided in this repository, with the exception of the Compustat quarterly firm-level data, which can be downloaded from https://wrds-www.wharton.upenn.edu/pages/ with a registered account.Software used The codes here run and have been tested either on Stata/MP 16.0 (for *.do files), on Matlab R2019/A (for *.m files), on R Version 4.04 (for *.R files), and on Anaconda 3 (for *.py, *.ipynb files). Most codes run in seconds/minutes on a personal laptop with 16GB ram, with the exception of the R code to estimate disaster episodes, which takes about 2 days using the standard settings from the Nakamura et al (2013) paper (nIter = 50,000, nRuns = 40). Directory list and list of main input files - if any - in each directory1. Monthly Geopolitical Risk Data Used in the Paper (data_paper)See README.txt file in the directory for detailsdata_gpr_export.dta (Stata format)data_gpr_export.xls (Excel format)2. Replication of Section I: Tables 1-2, Figures 1-8, Appendix Tables A.3-A.6, and Appendix Figures A.1-A.4 and A.10-A.14 (figures_paper) (requires Stata)See README.txt in the directory for detailsinput file: run_figures_tables.do3. Replication of Section III : VAR Evidence - Figures 9-10 and Appendix Figures A.5-A.7 (var_results)(requires Matlab)See README.txt in the directory for detailsinput file: run_all.m4. Replication of Section IV : Country-Specific GPR and Disaster Probability and Quantile Regressions - Tables 3-4 (disaster_regressions)(requires Stata)See README.txt in the directory for detailsinput file: run_replication_country_gpr.do5. Replication of Section V : Firm-Specific Geopolitical Risk - Table 5, Figure 11, Appendix Table A.7, and Appendix Figure A.9 (firm_regressions)(requires Stata)See README.txt file in the directory for details.input file: run_replication_firm_shuffled.do(Note that replication of the results here requires downloading firm-level balance sheet data through Compustat/WRDS. See firm_documentation below for instructions on how to build the firm_level.dta file)6. Auxiliary Material (Section V): Construction of Industry-Specific Exposure to Geopolitical Risk - Appendix Figure A.8 (industry_regressions)(requires Stata)See README.txt file in the directory for details.input file: run_replication_industry.do7. Auxiliary Material: Documentation on how to Build the firm_level.dta file (firm_documentation)See README_BUILD.txt file in the directory for details.8. Auxiliary Material (Section II): Tabulations of Daily Narrative GPR Data from The New York Times (narrative_index)See README.txt file in the directory for details.9. Appendix: Details on the Construction of the Human GPR Index (human_index)See README.txt file in the directory for details.10. Appendix: Audit of Articles Belonging to the GPR Index Described in Appendix Table A.3 (audit_coded)See README.txt file in the directory for details.11. Appendix: Granger Causality Tests --- Appendix Table A.8 (granger_causality)(requires Stata)See README.txt file in the directory for details.input file: run_granger_test.do12. Appendix: Replication of Textual Analysis in Appendix Tables A.1 and A.2 (text_analysis)(requires Matlab, including text analytics toolbox, and Stata for generating the formatted tables in the appendix)See README.txt file in the directory for details.input files: run_find_grams_textanalytics.m and run_app_tables_1_2.do 13. Auxiliary Material: Estimation of the Country Disaster Events from 1900 through 2019 (disaster_estimation)(requires R)See README.txt file in the directory for details.14. Auxiliary Material: Stata File with Firm-Level Geopolitical Risk Data (firm_level_gpr)See README.txt file in the directory for details.15. Auxiliary Material: Search Queries for News-Based GPR Index (news_searches)See README.txt file in the directory

Key information about Australia Geopolitical Risk Index

• Australia Geopolitical Risk Index was reported at 0.165 % in Feb 2025.
• This records an increase from the previous number of 0.082 % for Jan 2025.
• Australia Geopolitical Risk Index data is updated monthly, averaging 0.069 % from Jan 1985 to Feb 2025, with 482 observations.
• The data reached an all-time high of 0.515 % in Mar 2022 and a record low of 0.005 % in Feb 1989.
• Australia Geopolitical Risk Index data remains active status in CEIC and is reported by Dario Caldara and Matteo Iacoviello.
• The data is categorized under World Trend Plus’s Geopolitical Risk Index – Table: Geopolitical Risk Index: By Country.

NARDL estimation results for the effect of domestic (country-specific) GPR and US GPR on stock market returns and volatility.

This study employs monthly data from January 2000 to October 2022. The estimation sample is dictated by the availability of energy-related uncertainty variables. The core variable of the model is a cryptocurrency environmental attention index (ICEA). This index is calculated by Wang et al. (2022).The index of global geopolitical risks (GPR), developed by Caldara and Iacoviello (2022), is another important variable in the model. Furthermore, the financial stress index (FSI), developed by Office of Financial Research, is utilized. Finally, the energy-related uncertainty index (EU), developed by Dang et al. (2023)

Graph and download economic data for Global Economic Policy Uncertainty Index: Current Price Adjusted GDP (GEPUCURRENT) from Jan 1997 to May 2025 about uncertainty, adjusted, GDP, indexes, and price.

Graph and download economic data for Economic Policy Uncertainty Index for United States (USEPUINDXD) from 1985-01-01 to 2025-07-16 about uncertainty, academic data, indexes, and USA.

We investigate the dynamic volatility connectedness of geopolitical risk, stocks, bonds, bitcoin, gold, and oil from January 2018 to April 2022 in this study. We look at connectivity during the Pre-COVID, COVID, and Russian-Ukraine war subsamples. During the COVID-19 and Russian-Ukraine war periods, we find that conventional, Islamic, and sustainable stock indices are net volatility transmitters, whereas gold, US bonds, GPR, oil, and bitcoin are net volatility receivers. During the Russian-Ukraine war, the commodity index (DJCI) shifted from being a net recipient of volatility to a net transmitter of volatility. Furthermore, we discover that bilateral intercorrelations are strong within stock indices (DJWI, DJIM, and DJSI) but weak across all other financial assets. Our study has important implications for policymakers, regulators, investors, and financial market participants who want to improve their existing strategies for avoiding financial losses.