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 centuries 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 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.

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.

Over the period observed, the Geopolitical Risk Index (GPR) in Israel reached its highest point in ************. This was due to the outbreak of the Israel-Hamas war on October 7th that year. Another notable spike occurred in January 1991, during the Gulf War, at the start of Operation 'Desert Storm' and the Iraqi missile campaign against Israel.

Key information about Argentina Geopolitical Risk Index

• Argentina Geopolitical Risk Index was reported at 0.045 % in Feb 2025.
• This records an increase from the previous number of 0.031 % for Jan 2025.
• Argentina Geopolitical Risk Index data is updated monthly, averaging 0.025 % from Jan 1985 to Feb 2025, with 482 observations.
• The data reached an all-time high of 0.263 % in Nov 2018 and a record low of 0.000 % in Sep 2021.
• Argentina 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.

The Compass Series of Indexes is comprised of three unique and complementary Indexes that gauge the extent of global political, macroeconomic, and geopolitical risk: A Military Conflict Risk Index in five key geopolitical conflict regions, a Cold War Two Index in Russia, the US, and China, and a Polarization Risk Index in the G7 economies. Collectively, they provide investors, policymakers, and other decision makers with otherwise unavailable and comprehensive datafeeds that allow them to confirm and refute hypotheses and confidently navigate these risks.

The Cold War Index The Cold War II Index tracks – in Russia, the US, and China – six public sentiment indicators related to the geopolitical conflict and five current and future economic conditions indicators. The Index runs 24/7 and, unlike typical polls in these countries, draws on broad-based, anonymous, non-incented opinion.

The Military Conflict Risk Index The Military Conflict Risk Index measures, on a continuous, real-time basis, the perceptions of military conflict intensification from citizens in five major geopolitical conflicts: Russia-Ukraine, China-Taiwan, India-Pakistan, Iran-Israel, and South Korea-North Korea.

The Polarization Risk Index The Polarization Risk Index measures, on a quarterly basis, polarization within each G7 country as a key indicator of political stability. The Index uniquely draws on broad-based, anonymous opinion, minimizing biases associated with conventional polling.

As of Winter 2019 to 2020. the exchange transfer and trade sanction risk score of Iran was at ** ranking it as very high risk. The overall evaluation of the political risk situation of Iran was rated at ** on the PRI index, which is considered to be high.

Increasing geopolitical tensions and conflicts can cause disruptions for business operations. So, how do firms perceive geopolitical risk? The risk can differ between firms, but measurements focus primarily on the macro-level. Therefore, we introduce a new index to measure risk perceptions on the firm-level that we apply to Belgian public firms. To construct the index, we use a text-based content analysis method with a tailored dictionary. We use the index to examine whether geopolitical risk differs between firms and sectors, what the drivers of the risk are in high firms that experience high risk, and how the risk evolves over time. The study contributes to both the economic and security literature by introducing a new index and by creating and exploring new data on Belgian public firms. In addition, the index could become a helpful tool for policy makers that want to finetune their decisions on economic and security policy.

This data is mainly used to analyze the risk correlation among economic uncertainly,geopolitical risk and energy price,and can also be applied to the TVP-VAR model to analyze the correlation between different variables using the time-varying parameter model,which has great potential for reuse. The risk relationship between economic uncertainly.At the same time,since it is macroeconomic data,it does not involve any moral and ethical issues., , , # Economic uncertainty, geopolitical risk and U.S. energy price risk spillover: An empirical study based on the risk spillover model

Economic uncertainty, geopolitical risk and U.S. Energy Price risk spillover: An empirical study based on the Risk spillover model

Description of the data and file structure

The data set consists of the economic uncertainty index for the United States from 2009 to the end of 2023, the geopolitical risk index, and the time series data of major energy prices

Data potential This data is mainly used to analyze the risk correlation among economic uncertainty, geopolitical risk and energy price, and can also be applied to the TVP-VAR model to analyze the correlation between different variables using the time-varying parameter model, which has great potential for reuse. The risk relationship between economic uncertainty, geopolitical risk and energy price can be analyzed more accurately. At the same time, since it is macroeconomic data, it does not ...

United States Retail Sales Nowcast: sa: YoY: Contribution: Business Cycle Indicators: Geopolitical Risk Index data was reported at 0.195 % in 12 May 2025. This stayed constant from the previous number of 0.195 % for 05 May 2025. United States Retail Sales Nowcast: sa: YoY: Contribution: Business Cycle Indicators: Geopolitical Risk Index data is updated weekly, averaging 0.015 % from Feb 2020 (Median) to 12 May 2025, with 274 observations. The data reached an all-time high of 5.703 % in 17 Apr 2023 and a record low of 0.000 % in 14 Apr 2025. United States Retail Sales Nowcast: sa: YoY: Contribution: Business Cycle Indicators: Geopolitical Risk Index data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s United States – Table US.CEIC.NC: CEIC Nowcast: Retail Sales.

Graph and download economic data for Economic Policy Uncertainty Index for United States (USEPUINDXD) from 1985-01-01 to 2025-09-25 about academic data, uncertainty, indexes, and USA.

United States Unemployment Rate Nowcast: sa: Contribution: Business Cycle Indicator: Geopolitical Risk Index data was reported at 0.000 % in 12 May 2025. This stayed constant from the previous number of 0.000 % for 05 May 2025. United States Unemployment Rate Nowcast: sa: Contribution: Business Cycle Indicator: Geopolitical Risk Index data is updated weekly, averaging 0.047 % from Jan 2020 (Median) to 12 May 2025, with 279 observations. The data reached an all-time high of 12.178 % in 06 Jan 2025 and a record low of 0.000 % in 12 May 2025. United States Unemployment Rate Nowcast: sa: Contribution: Business Cycle Indicator: Geopolitical Risk Index data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s United States – Table US.CEIC.NC: CEIC Nowcast: Unemployment Rate.

To facilitate a comprehensive analysis of the spatial settings of geopolitical risk, we have developed an evaluation index system for geopolitical risk, grounded in the multi-scale risk perception framework. Using this system of evaluation indicators, we calculated the geopolitical risk for nine countries in Southeast Asia for the period 2013-2021. The dataset contains sources and raw data for the relevant indicators.

Energy storage technology as a key support technology for China’s new energy development, the demand for critical metal minerals such as lithium, cobalt, and nickel is growing rapidly. However, these minerals have high external dependence and concentrated import sources, increasing the supply risk caused by geopolitics. It is necessary to evaluate the supply risks of critical metal minerals caused by geopolitics to provide a basis for the high-quality development of energy storage technology in China. Based on geopolitical data of eight countries from 2012 to 2020, the evaluation indicators such as geopolitical stability, supply concentration, bilateral institutional relationship, and country risk index were selected to analyze the supply risk of three critical metal minerals, and TOPSIS was applied to construct an evaluation model for the supply risk of critical metal minerals of lithium, cobalt, and nickel in China. The results show that from 2012 to 2017, the security index of cobalt and lithium resources is between .6 and .8, which is in a relatively safe state, while the security index of nickel resources is .2–.4, which is in an unsafe state. From 2017 to 2020, lithium resources remain relatively safe, and the security index of nickel has also risen to between .6 and .7, which is generally in a relatively safe state. However, the security index of cobalt has dropped to .2, which is in an unsafe or extremely unsafe state. Therefore, China needs to pay attention to the safe supply of cobalt resources and formulate relevant strategies to support the large-scale development of energy storage technology.

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

A list of the variables and their respective source of data collection.

Dataset Description: 34-year Daily Stock Data (1990-2024)

Context and Inspiration

This dataset captures historical financial market data and macroeconomic indicators spanning over three decades, from 1990 onwards. It is designed for financial analysis, time series forecasting, and exploring relationships between market volatility, stock indices, and macroeconomic factors. This dataset is particularly relevant for researchers, data scientists, and enthusiasts interested in studying: - Volatility forecasting (VIX) - Stock market trends (S&P 500, DJIA, HSI) - Macroeconomic influences on markets (joblessness, interest rates, etc.) - The effect of geopolitical and economic uncertainty (EPU, GPRD)

Sources

The data has been aggregated from a mix of historical financial records and publicly available macroeconomic datasets: - VIX (Volatility Index): Chicago Board Options Exchange (CBOE). - Stock Indices (S&P 500, DJIA, HSI): Yahoo Finance and historical financial databases. - Volume Data: Extracted from official exchange reports. - Macroeconomic Indicators: Bureau of Economic Analysis (BEA), Federal Reserve, and other public records. - Uncertainty Metrics (EPU, GPRD): Economic Policy Uncertainty Index and Global Policy Uncertainty Database.

Columns

1. dt: Date of observation in YYYY-MM-DD format.
2. vix: VIX (Volatility Index), a measure of expected market volatility.
3. sp500: S&P 500 index value, a benchmark of the U.S. stock market.
4. sp500_volume: Daily trading volume for the S&P 500.
5. djia: Dow Jones Industrial Average (DJIA), another key U.S. market index.
6. djia_volume: Daily trading volume for the DJIA.
7. hsi: Hang Seng Index, representing the Hong Kong stock market.
8. ads: Aruoba-Diebold-Scotti (ADS) Business Conditions Index, reflecting U.S. economic activity.
9. us3m: U.S. Treasury 3-month bond yield, a short-term interest rate proxy.
10. joblessness: U.S. unemployment rate, reported as quartiles (1 represents lowest quartile and so on).
11. epu: Economic Policy Uncertainty Index, quantifying policy-related economic uncertainty.
12. GPRD: Geopolitical Risk Index (Daily), measuring geopolitical risk levels.
13. prev_day: Previous day’s S&P 500 closing value, added for lag-based time series analysis.

Key Features

• Cross-Market Analysis: Compare U.S. markets (S&P 500, DJIA) with international benchmarks like HSI.
• Macroeconomic Insights: Assess how external factors like joblessness, interest rates, and economic uncertainty affect markets.
• Temporal Scope: Longitudinal data facilitates trend analysis and machine learning model training.

Potential Use Cases

• Forecasting market indices using machine learning or statistical models.
• Building volatility trading strategies with VIX Futures.
• Economic research on relationships between policy uncertainty and market behavior.
• Educational material for financial data visualization and analysis tutorials.

Feel free to use this dataset for academic, research, or personal projects.

The dataset comprises monthly time series for exchange rates among the United States, Japan, Canada, the United Kingdom, France, Germany, and Italy. Explanatory variables include the output; the 3-month interest rates, the CPI, economic policy uncertainty indices, financial risk indicators such as implied equity market volatility (VIX), and geopolitical risk indicator, the U.S. monetary policy uncertainty, the U.S. trade policy uncertainty, the U.S. monetary policy surprise, term spread, and dividend yields. Macroeconomic series are drawn from the Federal Reserve Bank of St. Louis (FRED), OECD Main Economic Indicators, IMF International Financial Statistics, and national statistical agencies. Economic policy uncertainty and geopolitical risk indices come from policyuncertainty.com and the Caldara–Iacoviello dataset. Quarterly GDP data are interpolated to monthly frequency using the Chow–Lin method to match the frequency of other series. Monthly GDP data are obtained by interpolation. The EPU data are smoothed by a local level model. The explanatory data are transformed by natural logarithms.

The sample spans January 1999 to March 2025, subject to data availability.

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)

This study examines the association between home countries’ economic policy uncertainty (EPU) and foreign direct investment (FDI) inflows into Vietnam. Using data from 12 home countries from 2011 to 2022, we find that higher EPU significantly leads to lower FDI inflows into Vietnam. Also, we investigate how social connections between home countries and Vietnam, measured by the Social Connectedness Index, moderate the EPU - FDI relationship. Our findings show that social connectedness mitigate the negative impact of EPU on FDI by reducing information friction and enhancing trust in uncertain policy environments. These results are robust for home countries that experience periods of high global uncertainty and geopolitical risk, and are members of APEC.

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