Uncertainty Index Research Articles

The Effect of Economic Uncertainty on Access to Finance: An Empirical Examination for European SMEs

This study examines the effect of economic uncertainty on the probability of access to finance for the years between 2015 and 2020. Survey on Access to Finance of Enterprises (SAFE) dataset is used for measuring the level of access to finance and The IMF World Uncertainty Index (WUI) is considered as a proxy for uncertainty. According to probit regression model results, the uncertainty negatively affects the probability of firms’ access to bank loans while positively affecting the likelihood of access to trade credit. The results also reveal that some firm-level and macroeconomic indicators significantly impact access to finance.

The spillover effect of global uncertainty through input–output linkage

AbstractThis study examines the cross‐country spillover effects of global uncertainty on domestic value‐added by introducing indices for input‐ and output‐linked uncertainty. Employing a panel fixed‐effect regression model, the analysis covers 70 countries from 1995 to 2019, using the World Uncertainty Index and the OECD Inter‐Country Input–Output Table. Our findings reveal that input‐linked uncertainty from procuring foreign intermediate goods has the most significant negative impact on domestic value‐added. The effects of domestic uncertainty and output‐linked uncertainty from foreign product sales follow this. Additionally, these negative effects exhibit a dynamic cumulative impact over time, rather than being confined to a specific period. Considering both the magnitude and duration of these impacts, the primary transmission pathway of global uncertainty on a country's domestic production activity is through its input linkage. Furthermore, the study identifies country‐specific variations in the ripple effects of global uncertainty. Analysing specific cases such as Korea and China affirms the empirical relevance of our findings to their economic contexts.

Exploring the Relationship Between Technological Progress, Human Capital, Political Uncertain, Energy Consumption, and Economic Growth: Evidence from a Panel Data Analysis

AbstractThis study examines the determinants of economic growth by integrating technological progress, human capital, energy consumption, and monetary policy uncertainty in an extended theoretical model. The empirical investigation covers 18 economies from 2009 to 2019 and applies the Bias-Corrected Estimation (BC) and Generalized Method of Moments (GMM) techniques. The findings reveal that while gross capital formation is a significant growth driver, financial development has no meaningful impact. Notably, intermediate education contributes positively to growth, whereas advanced education surprisingly shows a negative effect. Additionally, the results indicate a direct relationship between renewable energy consumption and growth, with oil consumption having a negative impact. Political instability, measured by the Global Economic Policy Uncertainty (GEPU) index, also hinders growth.

Macroeconomic Uncertainty and Sectoral Output in Nigeria

The paper examined the impact of macroeconomic uncertainty on the ten largest subsectors of the Nigerian economy using quarterly data from Q1 1981 to Q4 2023. The rationale behind selecting the subsectors is that these sectors constitute about 89 percent of the entire productive activities in the economy. To achieve the objectives, the paper created an index for macroeconomic uncertainty using exchange rate uncertainty, interest rate uncertainty, inflation uncertainty, and real gross domestic product (GDP) uncertainty to create this index. Furthermore, the paper explored the impacts of macroeconomic uncertainty and these individual economic uncertainty indexes on sector output. The study employed the novel dynamic autoregressive distributed lag (novel dynamic ARDL) technique to estimate the results and used the canonical cointegrating regression (CCR) and fully modified ordinary least square (FMOLS) techniques as robustness on the main findings. The findings demonstrated that during periods of recession, macroeconomic uncertainty tends to heighten or reach its peak in Nigeria. Furthermore, the paper showed that the sectors react homogenously to macroeconomic uncertainty. In addition, the impulse response results from the novel dynamic ARDL estimation show that macroeconomic uncertainty can predict robust negative movements in sector output for Nigeria. Indeed, these findings are insightful as they show the importance of macroeconomic uncertainties as key drivers of sector output in Nigeria. The paper argues that the policy authorities should improve their efforts to reduce macroeconomic uncertainty and foster a stable real sector/sectoral output to enhance the macroeconomic environment for Nigeria to aim for higher levels of growth.

The determinants of volatility connectedness of South African equity super sectors

The paper aims to explore the determinants of total volatility connectedness of nine super sectors on the Johannesburg Stock Exchange (JSE) market from 3rd January 2006 to 31st December 2021. These sectors are Automobile and Parts, Chemical, Telecommunication, Technology, Energy, Health, Finance, Insurance, and General Industrials. The paper applied Diebold and Yilmaz connectedness matrix and the time-varying parameter – vector autoregressive (TVP-VAR) model to determine the sectorial total volatility connectedness index (STVCI). After that, the nonlinear autoregressive distributed lag model (NARDL) was used to determine the asymmetric effects and the drivers of STVCI. It was found that the partial sum decomposition of the South African volatility index (SAVI) and Economic Policy Uncertainty Index (EPU) are the key determinants of the STVCI both in the long and short run. However, domestic market return (DMR) shows no significant asymmetric effect on STVCI. The study concluded that SAVI and EPU are the key determinants of volatility connectedness among the JSE super-sectors. The results unveil important implications for sectorial investors and policymakers on potential regulations and stability of the significant determinants of spillover risk.

Forecasting Equity Premium in the Face of Climate Policy Uncertainty

ABSTRACTThis study examines the role of the US climate policy uncertainty (CPU) index in forecasting the equity premium, employing shrinkage methods such as LASSO and elastic net (ENet) to dynamically select predictors from a dataset spanning April 1987 to December 2022. Alongside CPU, other uncertainty predictors like economic policy uncertainty (EPU), geopolitical risk (GPR), and the volatility index (VIX) are considered to assess their complementary roles in out‐of‐sample (OOS) equity premium forecasting. The results reveal that while CPU alone cannot consistently predict the equity premium, it provides crucial complementary information when combined with other predictors, leading to a statistically significant OOS of 1.231%. The relationship between CPU and the equity premium is time varying, with a stronger influence observed during periods of economic downturn or heightened uncertainty, as demonstrated by wavelet coherence analysis. This study also identifies CPU's significant impact on industry‐specific returns, particularly in climate‐sensitive sectors, offering valuable insights for investment strategies and risk management in an era of increasing CPU.

Strategy towards sustainable energy transition: The effect of policy uncertainty, environmental technology and natural resources rent in the OECD nations

The stupendous increase in environmental pollutants alongside climate alterations are calling for integrated tasks at the global level to meet the Paris Agreements. Energy conversion from fossil fuel to renewable energy sources is an important step to address the adverse effects of extreme climatic conditions. This study explores the key contributors of energy transition in the context of the OECD countries during the period 1991 to 2019. To this end the major determinants chosen include natural resources rents, policy uncertainty, environmental technologies, environmental policies, income and globalization. The quantile regression technique is used to explore heterogeneous role of the contributory factors of energy transition across its different levels. This study applies method of moments quantile regression (hereafter MM-QR) by Machado and Silva (2019) that provides evidence on how the covariates affect the integral conditional distributions of energy transition. The study documents interesting findings. (i) Natural resources rents reduce the pace of the progress of sustainable energy transition in OECD economies. (ii) An escalation in the world uncertainty index leads to an improvement in energy transition index. (iii) Environmental technologies support the transition concerning cleaner and more efficient energy sources. (iv) Finally, as natural resources, uncertainty, environmental technologies, and globalization impacts energy transition differently, their effects need to be rationalized with varying environmental policy instruments. The study concludes that energy transition process requires the task of upgrading the quality of implementation of policies related to energy technology.

Is the Influence of Oil Shocks on Economic Policy Uncertainty Fading?

Anecdotal evidence indicates that the influence of crude oil shocks on US economic performance and, by extension, economic policy uncertainty is in decline. While existing studies present mixed evidence on the relationship between economic policy uncertainty and structural oil shocks, they fail to assess any time variation in this relationship. We examine the temporal nature of the influence of three structural oil shocks, oil supply shocks, global aggregate demand shocks, and shocks due to oil market-specific demand, on categorical economic policy uncertainty indexes from 1986 to 2023. We find that out of three oil shocks, only oil market-specific demand shocks negatively and significantly affect the US policy uncertainty over high-uncertainty regimes, with monetary policy uncertainty being the most influenced. At the same time, the impact of the other two shocks, oil supply shocks and aggregate demand shocks on EPU, is found to be fading. The oil shocks-EPU relationship exhibits regime dependencies with higher impact during the high volatile regime. Also, the role of partisan conflict in influencing the oil shocks-EPU relationship is established specifically in EPU subcategories relating to government expenditures. Hence, this paper uncovers interesting dynamics of the oil shocks-EPU relationship, such as regime dependencies, time-varying relationship, the importance of partisan conflict, and the fading influence of global oil supply shocks and oil shocks due to aggregate demand in the global economy.

Research on the Impact of Economic Policy Uncertainty and Investor Sentiment on the Growth Enterprise Market Return in China—An Empirical Study Based on TVP-SV-VAR Model

This study employs the economic policy uncertainty index to gauge the level of economic policy uncertainty in China. Utilizing textual data from the growth enterprise market internet community, we construct the growth enterprise market investor sentiment index by applying the deep learning ERNIE (Enhanced Representation through Knowledge Integration) model, thereby capturing investors’ sentiment within the growth enterprise market. The dynamic interplay between economic policy uncertainty, investor sentiment, and returns of the growth enterprise market is scrutinized via the TVP-SV-VAR (time-varying parameter stochastic volatility vector auto-regression) model, and the asymmetric response of different industries’ stock returns within the growth enterprise market to economic policy uncertainty and investor sentiment shock. The findings of this research are that economic policy uncertainty exerts a negative influence on both investor sentiment and returns of the growth enterprise market. While it may trigger a temporary decline in stock prices, the empirical evidence suggests that the impact is of short duration. The influence of investor sentiment on the growth enterprise market returns is characterized by a reversal effect, suggesting that improved sentiment may initially boost stock prices but could lead to a subsequent decline over the long term. The relationship between economic policy uncertainty, investor sentiment, and returns of the growth enterprise market is time-variant, with heightened sensitivity observed during bull markets. Lastly, the effects of economic policy uncertainty and investor sentiment on the returns of different industries within the growth enterprise market are found to be asymmetric. These conclusions contribute to the existing body of literature on the Chinese capital market, offering a deeper understanding of the complex dynamics and the factors influencing market behavior.

SOCIAL MEDIA SENTIMENT AND STOCK MARKET VOLATILITY: EVIDENCE FROM THE US HI-TECH COMPANIES

Purpose: This study aims to investigate the effects of social media sentiment, measured by Twitter-based uncertainty index, on the stock market volatility of the US technology companies. Design/methodology/approach: Methodologically, we employ the quantile regression model. Our findings reveal that the volatility of Amazon, Apple, Google and IBM stocks are sensitive to the variations in twitter-based economic and market uncertainties (i.e., TEU and TMU indexes). Findings: We observe significant influences at both lower and upper quantiles. Thus, for both high and low volatility regimes, the information on twitter-based uncertainty indexes can be used to predict the market volatility of these leading hi-tech companies. Moreover, TEU and TMU indexes exert positive effects on the stock price implied volatility implying that the variance of these technology firms experiences an upward trend as the social media uncertainty rises. Originality/value: While numerous studies have focused on the influence of social media (e.g., Facebook, twitter etc.) on investment strategies, the impact of twitter sentiments on the risk linked to hi-tech firms remains understudied. Hence, investors participating in the technology sectors could use our findings for managing portfolio risk.

Climate policy uncertainty and its impact on the volatility, correlation, and hedging of sovereign and green bonds

This study develops monthly climate policy uncertainty (CPU) indexes for 21 economies and three global CPU indexes to evaluate their effects on the long-term sovereign and green bond volatilities and the long-term sovereign-green bond correlation. Findings show significant increases in the CPU indexes during key climate policy events. Using the extended GARCH-MIDAS-CPU and DCC-MIDAS-CPU models, it finds that CPU significantly affects sovereign bond volatility following the Paris Agreement. Green bonds are more effective as hedging tools in the sovereign bond markets of emerging markets and developing economies than in developed ones, especially post-Paris Agreement. Diversified portfolios including green bonds offer superior hedging effectiveness. This study highlights the pivotal role of green bonds in managing CPU and promoting sustainable development goals.

Dynamics of persistence in Brazilian economic uncertainty, expectation, and confidence indexes

This study examines the persistence dynamics in Brazilian economic uncertainty, confidence, and expectation indexes, in both business and consumer sides of the economy. Using rolling window estimations of the fractional integration parameter, we assess how economic turbulence influences the persistence of these indexes. We find that economic uncertainty in Brazil is non-stationary but mean-reverting, with its persistence only transitorily affected by economic and political crises. Interestingly, confidence and expectation indexes displayed hysteretic behavior, with multiple persistence regimes linked to crises. These results may be informative for policymakers about the perceptions of the public and businessmen to economic shocks and crises.

Uncertainties of Economic Policy and Government Management Stability Played Important Roles in Increasing Suicides in Japan from 2009 to 2023.

Standardized suicide mortality rates per 100,000 (SMRs) in Japan consistently decreased from 2009 to 2019 but increased from 2020. The causes of these temporal SMR fluctuations remain to be clarified. Therefore, this study was conducted to identify the causalities underlying the recently transformed fluctuations of suicide mortality in Japan. Monthly suicide numbers disaggregated by sex and social standing, and political uncertainty indices, such as economic policy uncertainty (EPU) and government management instability (AENROP), were obtained from Japanese government databases. Interrupted time-series analysis was performed to analyze temporal fluctuations of SMRs disaggregated by sex/social standing associated with the three General Principles of Suicide Prevention Policy (GPSPP) periods and the COVID-19 pandemic. Panel data and vector autoregressive analyses were conducted to investigate causalities from political uncertainties to SMRs. During the first and second GPSPPs (2009-2017), all SMRs disaggregated by sex and social standing decreased, whereas those of unemployed females did not change. During the third GPSPP (2017-2022), decreasing trends in all SMRs were attenuated compared to previous periods. All female SMRs, except unemployed females, showed sharp increases synchronized with the pandemic outbreak. No male SMRs showed sharply increasing at the pandemic outbreak. SMRs of unemployed males/females drastically increased in the later periods of the pandemic, while SMRs of employed and multiple-person/single-person household males did not increase during the pandemic. SMR of unemployed males was positively related to AENROP but not EPU. Other male SMRs were positively related to EPU/AENROP. On the contrary, not all female SMRs were related to EPU/AENROP. Increasing AENROP generally contributed to increasing male SMRs throughout the observation period; however, susceptibility to AENROP and/or political information might have unexpectedly contributed to suppressing the sharply increasing male SMRs induced by large-scale social shocks (the COVID-19 pandemic outbreak) in Japan.

Probabilistic Quantile Factor Analysis

This article extends quantile factor analysis to a probabilistic variant that incorporates regularization and computationally efficient variational approximations. We establish through synthetic and real data experiments that the proposed estimator can, in many cases, achieve better accuracy than a recently proposed loss-based estimator. We contribute to the factor analysis literature by extracting new indexes of low, medium, and high economic policy uncertainty, as well as loose, median, and tight financial conditions. We show that the high uncertainty and tight financial conditions indexes have superior predictive ability for various measures of economic activity. In a high-dimensional exercise involving about 1000 daily financial series, we find that quantile factors also provide superior out-of-sample information compared to mean or median factors.

Volatility spillover between carbon market and related markets in time-frequency domain based on BEKK-GARCH and complex network analysis

With rising public attention to climate issues and sustainable development, the connection between carbon market and its related markets has become closer. Based on BEKK-GARCH method and complex network theory, this study tries to explore the volatility spillover effects and network topology among China's carbon market, non-renewable energy market, renewable energy market, high-tech market and climate policy uncertainty index in time-frequency domain. It is found that, firstly, there are significant asymmetric volatility spillover effects among the markets. Secondly, the spillover effects between markets are time-varying, especially during crisis periods in which they are significantly higher than that during the stable period. Thirdly, with time increasing, the closer the connection between markets. In the long run, the closeness of the connection is directly proportional to the spillover effect. Finally, climate policy uncertainty is the main risk source. For full sample, from perspective of frequency domain, non-renewable energy and carbon markets are the main risk recipients. Based on the findings, it should step up efforts on market monitoring and intervention during crisis periods, strengthen cross-market risk management, pay attention to climate uncertainty and time-frequency varying characteristics of volatility spillovers, so as to provide useful information for policy makers and investors.

Quantifying the spillover effects of U.S. economic policy uncertainty on emerging market economies using GMM-PVAR model

This paper quantifies the spillover effects of economic policy uncertainty (EPU) in the United States on emerging market economies (EMEs). Using a generalized method of moments (GMM) estimation of a panel vector autoregression (PVAR) model on a dataset­ of 39 EMEs from 2005 to 2019, we find that increased U.S. EPU significantly raises the consumer price index (CPI) and negatively impacts the real GDP of these economies. Additionally, heightened U.S. EPU leads to a depreciation of emerging market currencies and a reduction in short-term interest rates. We employ a news-based EPU index developed by Baker et al. (2016) and conduct robustness checks using forward orthogonal transformation, an alternative EPU index, and by addressing the potential endogeneity of the oil price uncertainty (OPU) index. Our findings highlight the adverse effects of U.S. economic policy uncertainty on key macroeconomic variables in emerging markets, underscoring the importance of stable economic policies and robust institutions to mitigate these impacts.

Dynamic spillovers of various uncertainties to Russian financial stress: Evidence from quantile dependency and frequency connectedness approaches

Being geopolitically exposed, the Russian financial sector is vulnerable to various ­uncertainties. The aim of the article is to examine the quantile movements and dynamic connectedness of uncertainty indices with the financial stress index of Russia employing the cross-quantilogram (CQ), recursive cross-quantilogram (R-CQ) and TVP‑VAR dynamic connectedness using monthly data from July 2011 to August 2023. It is found that for Tweeter-based Economic Uncertainty (TEU) and Global Economic Policy Uncertainty (GEPU), there is strong positive dependence on the Russian Financial Stress Index (RFSI) in the bearish states of market in the initial memory, and the strength of this positive spillover effect gradually wilts towards longer memory structures. Unlike the GEPU, Russian Economic Policy Uncertainty (REPU) has long-lasting heterogeneous spillover effects on RFSI. Though there are significant positive, as well as negative, spillover ­effects of Global Geopolitical Risk (GGPR) on RFSI in the initial memory, across the longer­ memory structures these entire heterogeneous effects wash out. However, Russian Geopolitical Risk (RGPR) have long-lasting heterogeneous spillover effects on RFSI, unlike GGPR. GEPU, GGPR and RGPR were the net transmitters while RFSI, TEU and REPU were net receivers of volatility shocks. Since RFSI shows resilience over the long-term horizon to global geopolitics and economic uncertainty, investors are advised to keep patience and hold their capital/investment up to a minimum of 1 year in Russian financial system in order to be rewarded with positive returns.

Chinese corporate biodiversity exposure

We present time series indices of corporate biodiversity risk exposure for listed Chinese companies. Utilizing recent advancements in text-as-data techniques, we analyze the annual reports of more than 4000 companies spanning a 15-year period. Our study introduces two indices: an exposure index, inspired by the work of Giglio et al., 2023, and an extensiveness index, which draws from policy uncertainty indices like Lucey et al., 2022. Our findings reveal substantial exposure to biodiversity risks among Chinese companies, notably exceeding levels observed in the USA.Corporate-level data are available as an Internet appendix to this note.

Climate policy uncertainty influences carbon emissions in the semiconductor industry

Industry carbon emissions have been increasing, yet there remains a dearth of research on the impacts of climate policy uncertainty. This study first explored the effects of climate policy uncertainty on the carbon emissions of semiconductor enterprises. We employed the Bidirectional Encoder Representations from Transformers (BERT) model. We constructed a Chinese climate policy uncertainty index based on electronic news entries to match the enterprise panel data structure from 2011 to 2022. The results showed an increase in climate policy uncertainty, which helped to reduce semiconductor enterprises’ carbon emissions. This effect was primarily achieved via two pathways. First, climate policy uncertainty leads to companies facing stricter environmental requirements, and these companies will proactively increase their investment in environmental, social, and governance standards to cope with the potential risks. Second, climate policy uncertainty is often accompanied by shifts in government climate policy. Governments will provide green subsidies to enterprises to achieve their policy goals. Furthermore, the policy uncertainty for the semiconductor industry could amplify the reducing effect of climate policy uncertainty on the carbon emissions from semiconductor enterprises. Climate policy uncertainty has a greater impact on non-state-owned and smaller semiconductor enterprises. Our study provides a new way to measure climate policy uncertainty, finds a new perspective based on climate policy uncertainty for exploring the potential impacts of corporate carbon emission reductions, bridges the gap between previous studies on enterprise carbon reductions and climate policy uncertainty, and offers a new path for governments to manage industrial carbon emissions.

Multi-model transfer function approach tuned by PSO for predicting stock market implied volatility explained by uncertainty indexes

This paper studies the forecasting power of uncertainty emanating from the commodities market, energy market, economic policy, and geopolitical threats to the CBOE Volatility Index (VIX). In this study, the relationship between the various uncertainty metrics throughout the period 2012—2022, using a multi-model transfer function technique optimized by particle swarm optimization (PSO) is estimated. Furthermore, we utilize PSO for parameter optimization within the multi-model framework, improving model performance and convergence speed. According to empirical findings, the CBOE Volatility Index reacts nonlinearly to the uncertainty indices. Specifically, the conclusions of the performance metrics show that the OVX index (MAPE: 4.1559%; RMSE: 1.0476% and W: 96.74%) outperforms the geopolitical risk index, the Bloomberg energy index, and the economic policy uncertainty index in predicting the volatility of the US equities market. Although individual models have generated respectful performance, results from the aggregate simulation show that when all predictors are combined, they simultaneously provide better performance indicators (MAVE: 2.7511 %; RMSE: 0.7361%; R2: 98.93%) than when they are estimated separately. In addition, results provide evidence that, when considering non-linear patterns in the data, the multi-model transfer function technique calibrated using PSO demonstrates its outperformance over autoregressive baseline models, traditional econometric models, and deep learning techniques. The effectiveness and accuracy of the multi-model transfer function method tuned by PSO as a forecasting tool are confirmed by the convergence analysis of the cost function. Our methodology innovates by employing a multi-model transfer function technique, which captures the complex and nonlinear relationships between uncertainty indicators and the VIX more comprehensively than traditional single-model approaches. These results are important for traders in terms of hedging as well as portfolio diversification by investing in defensive equities and for policymakers in terms of reliability and preciseness of volatility forecasts.