This survey reviews the empirical literature concerning the impacts of geopolitical uncertainty as expressed by the highly innovative Geopolitical Risk Index (GPR) by Cardara and Iacoviello (2019). Focus is made on the effects on cryptocurrencies, oil, gold and stock markets. Findings reveal that the GPR index is negatively influential on returns and volatility of oil prices while increase mostly the volatility of stock markets mainly at lower quantiles and weakens the linkage between oil and stock markets. Moreover, this index is a powerful predictor of Bitcoin returns and volatility and is of major importance for determining the diversifying or hedging character of Bitcoin and major cryptocurrencies in portfolios. This sheds light on yet weakly known aspects of geopolitical uncertainty on markets and enables investors to take decisions.
Cryptocurrencies constitute an innovative form of liquidity and investments that has been gaining increasing attention since the rally in their market values during 2017. This modern form of money is characterized by no necessity for intermediation when used in transactions, low transaction costs as well as the pseudonymous character of users (Böhme et al. 2015). Much interest has aroused though about whether cryptocurrencies can fulfill the functions of money. It is argued that while all digital currencies can play the role of a medium of exchange, their high levels of volatility prevent them from becoming a safe unit of account (Ammous 2018). Moreover, cryptocurrencies have been at the epicenter of criticism because they enable the conduct of illegal transactions and money laundering. On the other hand, the high levels of liquidity and accumulation of debt that cryptocurrencies permit render them a highly promising alternative source of funding (Dyhrberg et al. 2018).
There has been a proliferating bulk of high-quality academic research about macroeconomics but mainly focusing on financial markets that investigates whether geopolitical uncertainty is influential towards assets’ market values. In order to conduct accurate estimations there is the need to employ a measure that is largely representative of geopolitical risk in a worldwide level. For the purposes of this type of estimations, Cardara and Iacoviello (2019) have constructed the Geopolitical Risk Index (GPR) index. The latter reflects automated text-search results of the electronic archives of 11 national and international newspapers: The Boston Globe, Chicago Tribune, The Daily Telegraph, Financial Times, The Globe and Mail, The Guardian, Los Angeles Times, The New York Times, The Times, The Wall Street Journal, and The Washington Post (Caldara and Iacoviello 2019) Figs 1, 2, 3, 4, 5 and 6.
The calculation of values of this index takes place by counting the number of articles that present a linkage with geopolitical risk in each of these newspapers for each month and then normalizing. To be more precise, words linked with explicit mentions of geopolitical risk, military-related tensions involving large regions of the world and a U.S. involvement and terms directly connected to nuclear tensions are involved. Furthermore, words that are tied to war threats and terrorist threats are adopted. Moreover, terms representing press coverage of actual adverse geopolitical events (such as terrorist acts or the initiation of a war) are used.
There is an important number of studies that investigate how geopolitical risk influences financial markets. The focus of interest of academic research about the impacts of the GPR index is on commodities, stock markets and cryptocurrencies. To be more precise, the papers of Antonakakis et al. (2017), Plakandaras et al. (2019), Conado et al. (2019) and Aloui and Hamida (2019) look into the nexus between the GPR index and oil prices. Furthermore, Balcilar et al. (2018), Gkillas et al. (2018) and Baur and Smales (2018) conduct studies about how stock markets are affected by geopolitical uncertainty. Another strand of the relevant literature puts emphasis on the nexus between the GPR index and cryptocurrencies. Such academic work takes place by Aysan et al. (2019), Al-Yahyaee et al. (2019), Al Mamun et al. (2020) and Bouri et al. (2020).
This survey focuses on the impacts of geopolitical risk as captured by Cardara and Iacoviello (2019) on the real economy and especially on financial markets. The main axis of investigation in this paper is the impacts that the GPR index exerts on cryptocurrencies, which constitute highly innovative forms of liquidity and investments. These new form of money and investment assets have rendered increasingly popular since 2017 and have been a topic of heated debate among scholars, policymakers, investors and the financial press. The only integrated survey about digital currencies is that of Corbet et al. (2019). To the best of our knowledge, no integrated survey about the linkage between EPU and cryptocurrencies has taken place up to the present. Thereby, we contribute to existing research on financial markets by casting light on a highly modern aspect of the financial economics.
The remainder of this paper is structured as follows: Sect. “Studies that examine the nexus between GPR and macroeconomic variables” provides the primary studies that investigate the linkage between the GPR index and macroeconomic variables. Section “Studies investigating the nexus between GPR and stock markets” displays academic work related to the connection between geopolitical uncertainty and oil prices while Sect. “Studies investigating the linkage between GPR and oil prices” presents papers that look into the relation between the GPR index and stock markets. Moreover, Sect. “Studies about the impacts of GPR on cryptocurrencies” investigates the nexus between geopolitical risk and cryptocurrencies based on primary studies. These findings are also provided in Table 1 in a compact presentation. Finally, Sect. “Conclusions” lays out the conclusions, analyzes the economic implications and suggests avenues for further research.
Studies that examine the nexus between GPR and macroeconomic variables
Cheng and Chin (2018) employ structural Vector Autoregressive (SVAR) methodologies for 38 emerging countries and support that shocks of global geopolitical risk are related with significant economic contractions. Approximately 13–22% of total output variation in these countries is explained by the GPR index. Whatsoever, the exact fraction and the impulse responses to a GPR shock vary largely across the countries under scrutiny. Moreover, these findings do not alter when the terms of trade, financial conditions in the US, the US Economic Policy Uncertainty index and the VIX index are taken into consideration.
Moreover, based on their seminal work, Cardano and Iacoviello (2019) construct a geopolitical risk indicator based on newspaper articles that cover geopolitical tensions and study its evolution and impacts since 1985. War and terrorist events of high importance are included. High levels of the GPR index result into lower GDP, lower stock returns and to flows of capital from emerging towards advanced markets. It is found that the GPR index is mostly influential because of the threat component of adverse geopolitical events.
Studies investigating the nexus between GPR and stock markets
Current relevant literature about the impacts of geopolitical uncertainty on financial markets breaks down into studies that examine the linkage between Bitcoin and three alternative types of financial assets. The first strand of literature concerning geopolitical risk and financial markets centers interest on the linkage of the GPR with stock markets. Relevant studies include: Balcilar et al. (2018) and Baur and Smales (2018).
Balcilar et al. (2018) employ non-parametric causality-in-quantiles tests for exploring the impact of geopolitical uncertainty on return and volatility dynamics in the BRICS stock markets. Evidence reveals that this effect is heterogeneous across the BRICS stock market; thereby information about geopolitical risk does not affect these markets in the same manner. Outcomes indicate that great influence takes place on volatilities rather than returns and that mainly the lower quantiles of returns are affected. India is found to be the most resilient country against such risks. In a similar vein, Gkillas et al. (2018) adopt a non-parametric causality-in-quantiles test in order to detect whether the GPR index can predict jumps in volatility of the Dow Jones Industrial Average (DJIA) during the 1999–2017 period. Findings indicate that the GPR index can predict volatility jumps of the DJIA over its entire conditional distribution. Moreover, the cross-quantilogram analysis reveals that higher values of GPR are more important than lower values for increases in volatility jumps.
Furthermore, Baur and Smales (2018) investigate change in both perceived and realized global political risk on alterations in the prices of gold, silver, palladium, platinum, copper, the S&P500 and Bitcoin. Ordinary Least Squares (OLS) regressions are adopted in order to conduct estimations. Findings show that only gold exhibits a positive nexus with the geopolitical risk. Therefore, it is only gold that presents safe haven capabilities against the GPR index. This finding is robust to different classifications of geopolitical risk and to the inclusion of a widely-used sentiment measure.
Studies investigating the linkage between GPR and oil prices
The second strand of relevant literature focuses on GPR effects on commodity prices and the great majority of studies investigate oil prices. Antonakakis et al. (2017), Plakandaras et al. (2019) and Cunado et al. (2019) ate the academic papers that focus on such effects.
To be more precise, Antonakakis et al. (2017) employ monthly oil and stock data that cover from 1899 to 2016 in order to investigate whether their returns and volatility are affected by geopolitical risk. For the purposes of their estimations they employ Baba-Engle–Kroner-Kraft Generalized Autoregressive Conditional Hetereroskedasticity (BEKK-GARCH) methodologies based on Engle and Kroner (1995). Econometric outcomes provide evidence that the GPR index causes a negative impact, primarily on oil returns and volatility as well as on the covariance between the two markets but in a lesser extent. Furthermore, Plakandaras et al. (2019) investigate the dynamic linkage between oil prices and the GPR index as well as a composite measure of the index concerning emerging economies. The methodology employed is the Dynamic Model Averaging (DMA). Moreover, a number of linear and non-linear probabilistic models are used. According to econometric results, the global GPRs that are linked with war are the most appropriate for the purposes of forecasting oil returns in the short-run. On the other hand, composite GPRs based on emerging markets provide better forecasts of oil returns at medium- to longer horizons. It is argued that there is a negative nexus between GPR indices and oil returns. Moreover, it is found that increases in GPRs from their initial lower values can predict the resulting increases in oil returns in a more accurate manner compared to the lower end of the conditional distribution.
By their own perspective, Cunado et al. (2019) employ a time-varying parameter structural Vector Autoregressive (TVP-VAR) methodology in order to investigate the GPR effects on real oil returns for the period 1974–2017. Empirical findings reveal that GPR exerts negative effects on oil returns. The main reason for that is the lower oil demand as reflected by global economic activity. These results reinforce the role of geopolitical risk as a determinant of oil prices. Aloui and Hamida (2019) employ wavelet coherence methodologies in order to investigate the effect of the GPR index on the dynamic linkage between oil and stock prices in Saudi Arabia. Econometric estimations provide evidence of variation of the GPR role in the oil-stock nexus through different time periods and investment horizons. It is found that the GPR index leads to a weaker linkage between oil and stock in the short-run and that such geopolitical events result into lower oil-stock magnitude and volatility correlation.
Studies about the impacts of GPR on cryptocurrencies
For the purposes of elaborating on the arguments put forward by authors that have looked into the impacts of geopolitical risk on cryptocurrencies, we dwell on the academic papers of Aysan et al. (2019), Al-Yahyaee et al. (2019), Al Mamun et al. (2020), Chibane and Janson (2020) and Bouri et al. (2020).
Aysan et al. (2019) investigate the predictive power of GPR on Bitcoin returns and volatility. The methodologies adopted are the Bayesian Graphical Structural Vector Autoregressive (BSGVAR) model, Ordinary Least Squares (OLS) and Quantile-in-Quantile (QQ) estimations. Econometric outcomes by BSGVAR methodology reveal that alterations in the global GPR index present a predictive power on Bitcoin returns and volatility. Furthermore, OLS estimations provide evidence that changes in the GPR exert negative effects on Bitcoin returns but positive ones on Bitcoin volatility. As concerns findings based on QQ techniques, they show that the geopolitical risk exercises positive and statistically significant impacts at upper quantiles of both the returns and volatility of Bitcoin. It is supported that Bitcoin can act as a hedger against geopolitical risk. Baur and Lucey (2010) support that financial assets act as hedgers when they exhibit no correlation or negative correlation with alternative assets. Moreover, when they exhibit hedging abilities during extreme economic conditions they are considered to be safe havens. By adopting alternative methodologies, Al-Yahyaee et al. (2019) employ the US Economic Policy Uncertainty index, the Crude Oil Volatility index and the Geopolitical Risk index. The methodologies they use are the Wavelet Coherence (WC), Cross Wavelet Transform (CWT), Power Wavelet Coherence (PWC) and the Multiple Wavelet Coherence (MWC) specifications. Econometric outcomes reveal that the linkage between Bitcoin and the VIX index is not steady over time and over frequencies. It should be noted that negative (out-of-phase) comovements are traced at both high and low frequencies and information about the VIX present predictive impacts on Bitcoin returns over different frequencies. Emphasis should be put in that the EPU is found to be determinant of the nexus between Bitcoin and the VIX under alternative frequencies. Moreover, there is no evidence that investment horizons are influential as regards correlations between Bitcoin and uncertainty indices.
Among most recent relevant studies can be included the work of Al Mamun et al. (2020). They examine the nexus of geopolitical risk and economic policy uncertainty in the US and in a worldwide level with Bitcoin’s correlation with a range of financial assets. The Dynamic Conditional Correlations Glosten-Jagannathan-Runkle GARCH (DCC-GJR-GARCH) methodology is employed for the purposes of estimations. According to findings, geopolitical risk is the most important positive determinant of Bitcoin volatility and risk premia. Moreover, global EPU and is more influential than US policy uncertainty. Overall, evidence reveals that geopolitical risk, global and US economic policy uncertainty appear to be much more important regarding Bitcoin volatility during stressed economic conditions. By their point of view, Chibane and Janson (2020) investigate whether the levels of Geopolitical Risk Index influence the correlations of Bitcoin with other assets. Findings indicate that there is a strong nexus between Bitcoin and the global geopolitical risk. To be more precise, the GPR determines whether Bitcoin acts as a safe haven, as a risky investment or as a normal investment. When GPR is high, then Bitcoin is strongly linked with gold, US Treasury yields and negatively connected with the EUR/USD exchange rate. Moreover, the appearance of Bitcoin price bubbles is more likely to take place. It is argued that these findings can be extended regarding Ethereum or Ripple. Moreover, Bouri et al. (2020) apply the semi-parametric methodology of Laurent et al. (2016) and logistic regressions in order to examine the jump incidence of daily returns for major cryptocurrencies and study the co-jumps, respectively. Econometric evidence supports that jumps exist in the GPR index as well as the returns of Bitcoin, Ethereum, Ripple, Litecoin and Stellar. Moreover, Bitcoin is the only cryptocurrency that presents jumps positively dependent on jumps in the level of geopolitical risk. It is argued that Bitcoin can act as a hedge against geopolitical uncertainty.
This study constitutes an integrated survey on the impacts of global geopolitical risk on financial markets. Emphasis is put on the effects on oil prices, stock markets and especially on cryptocurrencies. A highly proliferating bulk of academic research has been devoted to investigating how commodities, stocks and digital currencies are influenced by geopolitical uncertainty and this study undertakes the task of casting light on these aspects of financial markets. Overall, fourteen primary studies are under scrutiny in this integrated review. It should be emphasized that a highly innovative perspective of the economics and finance field comes better into light by our survey.
As concerns estimations about GPR effects on stock indices, findings by primary studies indicate that geopolitical risk is rather influential on volatility than on returns of stock indices and mainly at lower quantiles. Nevertheless, there is also some evidence that stock markets are not influenced by the GPR index while gold receives much larger impacts. When it comes to findings about the linkage of geopolitical uncertainty with oil prices, it is revealed that the GPR index exerts a negative effect on oil prices and volatility and that the global GPR or composite indices of emerging markets are more representative. Moreover, it is argued that higher geopolitical uncertainty leads to a weaker and less stable connection between oil and the stock markets. Econometric outcomes about the GPR impacts on cryptocurrencies also bring to the surface a number of interesting findings. There is evidence that geopolitical risk displays powerful predictive powers as concerns the returns and volatility of Bitcoin and other major digital currencies in a positive direction. Moreover, the GPR is found to be a strong determinant of whether Bitcoin acts as a diversifier, hedge or safe haven in portfolios with other assets. Additionally, it is revealed that Bitcoin could act as a hedge against geopolitical uncertainty.
The main methodologies employed for the purposes of estimations in primary studies are Ordinary Least Squares (OLS), BEKK-GARCH methodologies, Dynamic Conditional Correlations Glosten-Jagannathan-Runkle GARCH (DCC-GJR-GARCH) models, logistic regressions, non-parametric causality-in-quantiles tests, Dynamic Model Averaging (DMA) are used for the purposes of estimations of GPR effects on financial assets. Moreover, Time-Varying Parameter Vector Autoregressive (TVP-VAR) and Bayesian Graphical Structural Vector Autoregressive (BSGVAR) schemes are adopted. Additionally, models based on econophysics such as Wavelet Coherence (WC), Cross Wavelet Transform (CWT), Power Wavelet Coherence (PWC) and the Multiple Wavelet Coherence (MWC) specifications are employed.
The basic aim of this survey is to provide a bird’s-eye view on the impacts that uncertainty stemming from geopolitical risk as measured by Cardara and Iacoviello (2019) can exert on financial markets. This integrated overview could constitute a roadmap for investors in commodities such as oil or gold, stock markets in advanced as well as emerging economies and cryptocurrencies, which comprise the most modern form of liquidity and investments. Thereby, we cast light on a newly-developed but very interesting aspect of financial economics. Avenues for future investigation could include the examination of the influence of other indices that lead to uncertainty such as the index of trade policy uncertainty. Moreover, as research on the GPR effects advances fruitful findings about other conventional or sophisticated macroeconomic or financial variables could emerge and be worth examining.
Data availability statement
No data have been used.
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Kyriazis, Ν.A. The effects of geopolitical uncertainty on cryptocurrencies and other financial assets. SN Bus Econ 1, 5 (2021). https://doi.org/10.1007/s43546-020-00007-8
- Geopolitical risk index