Understanding Uncertainty Shocks in Uruguay Through VAR Modeling

This study introduces a first set of uncertainty indexes for Uruguay (a newspaper-based index and a composite index-based) to analyze how economic uncertainty impacts domestic variables in a small and open economy such as Uruguay, which is exposed to international, regional, and local uncertainty. The analysis covers approximately 15 years and uses the vector autoregressive methodological framework. The main findings suggest that economic uncertainty significantly impacts the real economy and does not impact the nominal variables. These findings which differentiate from other results found in the empirical literature, can be associated with the stability of the Uruguayan economy and the strong institutions, which may help mitigate external shocks. To assess the capability of the proposed uncertainty model to predict macroeconomic variables, we evaluate its predictive performance within the last major uncertainty shock due to the COVID-19 pandemic. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

The role of categorical EPU indices in predicting stock-market returns

This study investigates the predictive ability of categorical economic-policy uncertainty (EPU) indices for stock-market returns. The results indicate that some categorical EPU indices have superior predictive ability for stock returns and even achieve higher realized utility than the original EPU index and popular predictors. Furthermore, the diffusion indices based on EPU categories, especially those that use partial least squares (PLS) to extract the principal components, more effectively use the forecast information contained in categorical EPU indices, resulting in improved forecast performance, including reduced forecast errors and increased economic value for investors. In addition, the categorical EPU indices show superior forecasting performance during economic-expansion, the China-US trade-war, and COVID-19 pandemic periods.

Cross-country categorical economic policy uncertainty spillovers: evidence from a conditional connectedness TVP-VAR framework

Purpose: This study aims to investigate the dynamic interconnectedness of economic policy uncertainty (EPU), fiscal policy uncertainty (FPU) and monetary policy uncertainty (MPU) in four nations, the USA, Japan, Greece and South Korea, between 1998 and 2021. Design/methodology/approach: To comprehend the cross-category/cross-country evolution of uncertainty connectedness, the authors use the conditional connectedness approach. By using an inclusive network, this strategy lessens the bias caused by omitted variables. The TVP-VAR method is advantageous as it eliminates outliers that may potentially skew the results and reduces the bias caused by picking arbitrary rolling windows. Findings: Based on the findings, aggregate EPU is a net transmitter of policy uncertainties across all countries when conditional-country connectedness is used. MPU receives significantly more spillovers than FPU does across all countries, even though both are primarily recipients of uncertainties. The USA appears to be a transmitter of categorical spillovers before COVID-19, while Greece appears to be a net receiver of all category spillovers in terms of category-specific connectedness. The existence of extreme global events is also seen to cause an increase in category-specific and country-specific connectedness. Additionally, the authors report that conditional country-specific connectedness is greater than conditional category-specific connectedness. Originality/value: This study expands existing literature in several ways. Firstly, the authors use a novel conditional connectedness approach, which has not been used to untangle cross-category/cross-country policy uncertainty connectedness. Secondly, they use the TVP-VAR approach which does not depend on rolling windows to understand dynamic connectedness. Thirdly, they use an expanded number of countries in their analysis, a departure from existing studies that have in most cases used two countries to understand categorical EPU connectedness. © 2023, Kingstone Nyakurukwa and Yudhvir Seetharam.

Economic Policy Uncertainty, Energy and Sustainable Cryptocurrencies: Investigating Dynamic Connectedness during the COVID-19 Pandemic

The purpose of the research is to explore the dynamic multiscale linkage between economic policy uncertainty, equity market volatility, energy and sustainable cryptocurrencies during the COVID-19 period. We use a multiscale TVP-VAR model considering level (EPUs and IDEMV) and returns series (cryptocurrencies) from 1 December 2019 to 30 September 2022. The data are then decomposed into six wavelet components, based on the wavelet MODWT method. The TVP-VAR connectedness approach is used to uncover the dynamic connectedness among EPUs, energy and sustainable cryptocurrency returns. Our findings reveal that CNEPU (USEPU) is the strongest (weakest) NET volatility transmitter. IDEMV is the most consistent volatility NET transmitter among all uncertainty indices across the original returns and wavelet scales (D1~D6). Energy cryptocurrencies, i.e., GRID, POW and SNC, are more likely to receive volatility spillovers than sustainable cryptocurrencies during a turbulent period (COVID-19). XLM (XNO) is least (most) affected by volatility spillover in system-wide connectedness, and XLM (ADA and MIOTA) showed a consistent (heterogeneous) non-recipient behavior across the six wavelet (D1~D6) scales and original return series. This study uncovers the dynamic connectedness across multiscale, which will support investors considering different investment horizons (D1~D6). © 2023 by the authors.

Market reaction to macroeconomic anouncements: green vs conventional bonds

There has been an increased interest in literature to examine the risk and returns between green and conventional bonds during the last decade. However, the existing literature is silent regarding investigating green bonds and their reactions to regional and global shocks. We attempt to close this gap by gathering green and conventional bonds data issued by the same firms. Using data from 262 firms that issue both sets of bonds and trade in the same market, we are able to control/eliminate for firm-specific factors that can impact the bond spreads. Upon introducing US and EU macroeconomic announcements and economic uncertainty, we find that the green bonds are more resilient from specific shocks, when compared to conventional bonds. We further expand our study to systematically assess the impact of the Covid-19 pandemic on both bonds. Our comprehensive findings suggest an increase in green bonds' uptake post-Covid-19 pandemic, and its significant evolvement compared to pre-Covid-19 proves green bonds popularity. Our result shows greater resilience of green bonds from specific Covid-19 shocks and uncertainties.

Do stock market fear and economic policy uncertainty co-move with COVID-19 fear? Evidence from the US and UK

Purpose: The purpose of the paper is to investigate co-movement of major implied volatility indices and economic policy uncertainty (EPU) indices with both the health-based fear index and market-based fear index of COVID-19 for the USA and the UK to help investors and portfolio managers in their informed investment decisions during times of infectious disease spread. Design/methodology/approach: This study uses wavelet coherence approach because it allows to observe lead–lag nonlinear relationship between two time-series variables and captures the heterogeneous perceptions of investors across time and frequency. The daily data used in this study about the USA and the UK covers major implied volatility indices, EPU, health-based fear index and market-based fear index of COVID-19 for both the first and second waves of COVID-19 pandemic over the period from March 3, 2020 to February 12, 2021. Findings: The results document a strong positive co-movement between implied volatility indices and two proxies of the COVID-19 fear. However, in all the cases, the infectious disease equity market volatility index (IDEMVI), the COVID-19 proxy, is more representative of the stock market and exhibits a stronger positive co-movement with volatility indices than the COVID-19 fear index (C19FI). This study also finds that the UK's implied volatility index weakly co-moves with the C19FI compared to the USA. The results show that EPU indices of both the USA and the UK exhibit a weak or no correlation with the C19FI. However, this study finds a significant and positive co-movement of EPU indices with IDEMVI over the short horizon and most of the sampling period with the leading effect of IDEMVI. This study's robustness analysis using partial wavelet coherence provides further strengths to the findings. Research limitations/implications: The investment decisions and risk management of investors and portfolio managers in financial markets are affected by the new information on volatility and EPU. The findings provide insights to equity investors and portfolio managers to improve their risk management practices by incorporating how health-related risks such as COVID-19 pandemic can contribute to the market volatility and economic risks. The results are beneficial for long-term equity investors, as their investments are affected by contributing factors to the volatility in US and UK's stock markets. Originality/value: This study adds following promising values to the existing literature. First, the results complement the existing literature (Rubbaniy et al., 2021c) in documenting that type of COVID-19 proxy matters in explaining the volatility (EPU) relationships in financial markets, where market perceived fear of COVID-19 is appeared to be more pronounced than health-based fear of COVID-19. Second, the use of wavelet coherence approach allows us to observe lead–lag relationship between the selected variables, which captures the heterogeneous perceptions of investors across time and frequency and have important insights for the investors and portfolio managers. Finally, this study uses the improved data of COVID-19, stock market volatility and EPU compared to the existing studies (Sharif et al., 2020), which are too early to capture the effects of exponential spread of COVID-19 in the USA and the UK after March 2020. © 2022, Emerald Publishing Limited.

Word embeddings for topic modeling: An application to the estimation of the economic policy uncertainty index

The quantification of economic uncertainty is key to the prediction of macroeconomic variables, such as gross domestic product (GDP), and is particularly crucial in regard to real-time or short-time prediction methodologies, such as nowcasting, where a large amount of time series data is required. Most of the data comes from official agency statistics and non-public institutions, but these sources are susceptible to lack of information due to major disruptive events, such as the COVID-19 pandemic. Because of this, it is very common nowadays to use non-traditional data from different sources. The economic policy uncertainty (EPU) index is the indicator most frequently used to quantify uncertainty and is based on topic modeling of newspapers. In this paper, we propose a methodology to estimate the EPU index that incorporates a fast and efficient method for topic modeling of digital news based on semantic clustering with word embeddings, allowing us to update the index in real time, which is something that other studies have failed to manage. We show that our proposal enables us to update the index and significantly reduce the time required for new document assignation into topics. © 2022 Elsevier Ltd

Are categorical EPU indices predictable for carbon futures volatility? Evidence from the machine learning method

The purpose of this paper is to explore whether the categorical Economic Policy Uncertainty (EPU) indices are predictable for the volatility of carbon futures, in the mixed data sampling (MIDAS) regression framework. The prediction methods include the MIDAS-RV model, the MIDAS models extended by individual categorical EPU index, combination prediction approaches, the MIDAS models extended by dimensionality reduction techniques as well as the machine learning methods on the basis of MIDAS model and Markov regime switching method. We find firstly that categorical EPU indices are predictable for carbon futures volatility, but the predictive power of individual categorical EPU indices is not robust. Secondly, machine learning methods, especially the machine learning method considering the Markov regime switching structure, help to obtain valid information from multiple categorical EPU indices and produce robust and superior prediction accuracy for carbon futures volatility. The results of the extension analysis also found that machine learning methods, especially the machine learning method considering the Markov regime switching structure help to produce higher investment performance and more accurate long-term carbon futures volatility forecasts. Meanwhile, we also find the advantages of the MIDAS based machine learning methods over the traditional AR based machine learning methods. Finally, the forecasting performance of the machine learning method which considering Markov regime switching structure are superior during both the low and high volatility regimes and even during the COVID-19 pandemic. © 2022 Elsevier Inc.

Which is leading: Renewable or brown energy assets?

This study examines the relationship between crude oil, a proxy for brown energy, and several renewable energy stock sector indices (e.g., solar energy, wind energy, bioenergy, and geothermal energy) over various investment horizons. Using daily data from October 15, 2010, to February 23, 2022, we apply a combination of methods involving co-integration, wavelet coherency, and wavelet-based Granger causality. The results show that the relationship between crude oil and renewable energy indices is non-linear and somewhat multifaceted. Firstly, there are sectorial differences in the intensity of the relationships. Notably, the relationship intensity between the wind and crude oil is lower than that involving geothermal energy or bioenergy. Secondly, the relationship evolves with time. For example, the COVID-19 outbreak seems to have increased the relationship between crude oil and renewable energy markets, notably for solar, bioenergy, and geothermal. Thirdly, the relationship varies across scales. When controlling for the VIX (volatility index), a proxy of the sentiment of market participants, and EPU (economic policy uncertainty index), the relationship seems strong in the long term but weak in the short term. This result is confirmed using a Granger causality test on the wavelet-decomposed series. These findings have important implications for long-term investors, short-term speculators, and policymakers regarding the co-movement between brown and renewable energy markets. © 2022 Elsevier B.V.

Do stock market fear and economic policy uncertainty co-move with COVID-19 fear? Evidence from the US and UK

Purpose>The purpose of the paper is to investigate co-movement of major implied volatility indices and economic policy uncertainty (EPU) indices with both the health-based fear index and market-based fear index of COVID-19 for the USA and the UK to help investors and portfolio managers in their informed investment decisions during times of infectious disease spread.Design/methodology/approach>This study uses wavelet coherence approach because it allows to observe lead–lag nonlinear relationship between two time-series variables and captures the heterogeneous perceptions of investors across time and frequency. The daily data used in this study about the USA and the UK covers major implied volatility indices, EPU, health-based fear index and market-based fear index of COVID-19 for both the first and second waves of COVID-19 pandemic over the period from March 3, 2020 to February 12, 2021.Findings>The results document a strong positive co-movement between implied volatility indices and two proxies of the COVID-19 fear. However, in all the cases, the infectious disease equity market volatility index (IDEMVI), the COVID-19 proxy, is more representative of the stock market and exhibits a stronger positive co-movement with volatility indices than the COVID-19 fear index (C19FI). This study also finds that the UK's implied volatility index weakly co-moves with the C19FI compared to the USA. The results show that EPU indices of both the USA and the UK exhibit a weak or no correlation with the C19FI. However, this study finds a significant and positive co-movement of EPU indices with IDEMVI over the short horizon and most of the sampling period with the leading effect of IDEMVI. This study's robustness analysis using partial wavelet coherence provides further strengths to the findings.Research limitations/implications>The investment decisions and risk management of investors and portfolio managers in financial markets are affected by the new information on volatility and EPU. The findings provide insights to equity investors and portfolio managers to improve their risk management practices by incorporating how health-related risks such as COVID-19 pandemic can contribute to the market volatility and economic risks. The results are beneficial for long-term equity investors, as their investments are affected by contributing factors to the volatility in US and UK's stock markets.Originality/value>This study adds following promising values to the existing literature. First, the results complement the existing literature (Rubbaniy et al., 2021c) in documenting that type of COVID-19 proxy matters in explaining the volatility (EPU) relationships in financial markets, where market perceived fear of COVID-19 is appeared to be more pronounced than health-based fear of COVID-19. Second, the use of wavelet coherence approach allows us to observe lead–lag relationship between the selected variables, which captures the heterogeneous perceptions of investors across time and frequency and have important insights for the investors and portfolio managers. Finally, this study uses the improved data of COVID-19, stock market volatility and EPU compared to the existing studies (Sharif et al., 2020), which are too early to capture the effects of exponential spread of COVID-19 in the USA and the UK after March 2020.

Spillover Effect of Economic Policy Uncertainty on the Stock Market in the Post-Epidemic Era

In the post-epidemic era, global economic policies have been uncertain and the stock market has been volatile. It is crucial to investigate the spillover effect of economic policy uncertainty (EPU) on the stock market for accurately hedging risks and seizing recovery opportunities. This paper applies the DY spillover index and network analysis to study the spillover effect between the U.S. EPU and the U.S. and Asian stock markets. The empirical results show a significant spillover effect in both the U.S. and Asian stock markets, with EPU as the recipient of risk spillover and stock indices as the transmitters. The stock markets in Japan and South Korea react more strongly to shifts in the U.S. EPU. All transmitters attain their maximum values in both the TO and FROM directions in 2020. The from-direction spillover indices of the U.S. stock market are less volatile in 2020 than those of the Asian stock market, indicating that the outbreak of the COVID-19 epidemic has a greater impact on the Asian stock market than the U.S. stock market. These conclusions have substantial implications for asset management, investment diversification and aversion to unsystematic risk in major economic shocks.

Are categorical EPU indices predictable for carbon futures volatility? Evidence from the machine learning method

The purpose of this paper is to explore whether the categorical Economic Policy Uncertainty (EPU) indices are predictable for the volatility of carbon futures, in the mixed data sampling (MIDAS) regression framework. The prediction methods include the MIDAS-RV model, the MIDAS models extended by individual categorical EPU index, combination prediction approaches, the MIDAS models extended by dimensionality reduction techniques as well as the machine learning methods on the basis of MIDAS model and Markov regime switching method. We find firstly that categorical EPU indices are predictable for carbon futures volatility, but the predictive power of individual categorical EPU indices is not robust. Secondly, machine learning methods, especially the machine learning method considering the Markov regime switching structure, help to obtain valid information from multiple categorical EPU indices and produce robust and superior prediction accuracy for carbon futures volatility. The results of the extension analysis also found that machine learning methods, especially the machine learning method considering the Markov regime switching structure help to produce higher investment performance and more accurate long-term carbon futures volatility forecasts. Meanwhile, we also find the advantages of the MIDAS based machine learning methods over the traditional AR based machine learning methods. Finally, the forecasting performance of the machine learning method which considering Markov regime switching structure are superior during both the low and high volatility regimes and even during the COVID-19 pandemic.

Category-specific EPU indices, macroeconomic variables and stock market return predictability

This paper mainly investigates whether the category-specific EPU indices have predictability for stock market returns. Empirical results show that the content of category-specific EPU can significantly predict the stock market return, no matter the individual category-specific EPU index or the principal component of category -specific EPU indices. In addition, the information of category-specific EPU indices can also have higher eco-nomic gains than traditional macroeconomic variables, even considering the trading cost and different investor risk aversion coefficients. During different forecasting windows, multi-period forecast horizons and the COVID-19 pandemic, we find the information contained in category-specific EPU indices can have better performances than that of the macroeconomic variables. Our paper tries to provide new evidence for stock market returns based on category-specific EPU indices.

Which is leading: Renewable or brown energy assets?

This study examines the relationship between crude oil, a proxy for brown energy, and several renewable energy stock sector indices (e.g., solar energy, wind energy, bioenergy, and geothermal energy) over various investment horizons. Using daily data from October 15, 2010, to February 23, 2022, we apply a combination of methods involving co-integration, wavelet coherency, and wavelet-based Granger causality. The results show that the relationship between crude oil and renewable energy indices is non-linear and somewhat multifaceted. Firstly, there are sectorial differences in the intensity of the relationships. Notably, the relationship intensity between the wind and crude oil is lower than that involving geothermal energy or bioenergy. Secondly, the relationship evolves with time. For example, the COVID-19 outbreak seems to have increased the relationship between crude oil and renewable energy markets, notably for solar, bioenergy, and geothermal. Thirdly, the relationship varies across scales. When controlling for the VIX (volatility index), a proxy of the sentiment of market participants, and EPU (economic policy uncertainty index), the relationship seems strong in the long term but weak in the short term. This result is confirmed using a Granger causality test on the wavelet-decomposed series. These findings have important implications for long-term investors, short-term speculators, and policymakers regarding the co-movement between brown and renewable energy markets.

Does the Economic Policy Uncertainty in the USA and China Affect Indian Stock Markets Symmetrically and Asymmetrically? An Empirical Analysis of Pre- and Post-Coronavirus Disease 2019 Timelines

Globalization and economic interconnections have recently garnered a lot of attention, and scholars and policymakers are delving into the effects of policy uncertainty on macroeconomic fundamentals against this backdrop, the current study investigates the symmetric and asymmetric effects of economic policy uncertainty (EPU) in the USA and China on the Indian benchmark index. Furthermore, the study also analyses the variation in results due to the occurrence of uncertain events. For the empirical analysis, non-linear autoregressive distributed lag (NARDL) and autoregressive distributed lag (ARDL) techniques have been employed, and the findings substantiate an asymmetric relationship between the US EPU and the Indian stock market, while a symmetric relationship between the Chinese EPU and the Indian stock market. The study also reveals that in the short run, the Indian stock market is immune to the fluctuations in the Chinese stock market and policy uncertainty. Moreover, the outcomes further reveal that the US and Indian stock markets project a mirror performance. In addition, the study further reveals that, unlike the pre-Coronavirus Disease 2019 (COVID-19) timeline, during the COVID- 19, the short-run volatility was high between the explanatory and outcome variables. The study is an original work and offers several useful recommendations.

Equation Chapter 0 Section 1 category-specific EPU indices, macroeconomic variables and stock market return predictability

This paper mainly investigates whether the category-specific EPU indices have predictability for stock market returns. Empirical results show that the content of category-specific EPU can significantly predict the stock market return, no matter the individual category-specific EPU index or the principal component of category-specific EPU indices. In addition, the information of category-specific EPU indices can also have higher economic gains than traditional macroeconomic variables, even considering the trading cost and different investor risk aversion coefficients. During different forecasting windows, multi-period forecast horizons and the COVID-19 pandemic, we find the information contained in category-specific EPU indices can have better performances than that of the macroeconomic variables. Our paper tries to provide new evidence for stock market returns based on category-specific EPU indices.

Word embeddings for topic modeling: An application to the estimation of the economic policy uncertainty index

The quantification of economic uncertainty is key to the prediction of macroeconomic variables, such as gross domestic product (GDP), and is particularly crucial in regard to real-time or short-time prediction methodologies, such as nowcasting, where a large amount of time series data is required. Most of the data comes from official agency statistics and non-public institutions, but these sources are susceptible to lack of information due to major disruptive events, such as the COVID-19 pandemic. Because of this, it is very common nowadays to use non-traditional data from different sources. The economic policy uncertainty (EPU) index is the indicator most frequently used to quantify uncertainty and is based on topic modeling of newspapers. In this paper, we propose a methodology to estimate the EPU index that incorporates a fast and efficient method for topic modeling of digital news based on semantic clustering with word embeddings, allowing us to update the index in real time, which is something that other studies have failed to manage. We show that our proposal enables us to update the index and significantly reduce the time required for new document assignation into topics.

Do gold, oil, equities, and currencies hedge economic policy uncertainty and geopolitical risks during covid crisis?

Recent literature extensively studies the safe-haven properties of different asset classes in crisis periods. The magnitude of the economic policy uncertainty index (EPU) and the geopolitical risk (GPR) increases significantly during extreme crisis periods such as covid crisis, but the earlier literature ignores how both risk measures impact on different asset classes during severe economic downturns. In this paper, we contribute by examining the hedging and safe-haven properties of gold, oil, equities, and foreign exchange rates against the United States (US) EPU and GPR by utilizing OLS regression, quantile regression and the quantile connectedness approach for pre-covid (October 1, 2013–March 10, 2020) and post-covid data (March 11, 2020–August 27, 2021). OLS results suggest that only the stock market has positive risk premium for both uncertainty measures. With quantile regression analysis for the pre-covid period, we find that asset returns provide no hedge (hedge) across bearish (bullish) market conditions. Importantly, safe-haven properties suggest that gold is a safe-haven asset at the extreme stress condition (at higher level of USEPU shocks). Other assets also exhibit safe-haven characteristics during extreme uncertain periods with heterogeneity in safe-haven effectiveness across bearish to bullish markets. With the post-covid data, we show that S&P500 stocks and EURO hedge EPU and GPR in bullish market condition, while Oil, S&P500, Great Britain Pound, EURO, Japanese Yen display safe-haven properties at the 99% quantile of USEPU. Specifically, gold lost its safe-haven features during covid. Interestingly, results from quantile connectedness suggest that selected asset returns have the potential to diversify against uncertainty measures considering low volatility transmissions between them across the lower and higher quantiles. Our findings are important for investors and asset managers who aim to hedge EPU and GPR during the stress period.

Market reaction to macroeconomic anouncements: green vs conventional bonds

There has been an increased interest in literature to examine the risk and returns between green and conventional bonds during the last decade. However, the existing literature is silent regarding investigating green bonds and their reactions to regional and global shocks. We attempt to close this gap by gathering green and conventional bonds data issued by the same firms. Using data from 262 firms that issue both sets of bonds and trade in the same market, we are able to control/eliminate for firm-specific factors that can impact the bond spreads. Upon introducing US and EU macroeconomic announcements and economic uncertainty, we find that the green bonds are more resilient from specific shocks, when compared to conventional bonds. We further expand our study to systematically assess the impact of the Covid-19 pandemic on both bonds. Our comprehensive findings suggest an increase in green bonds’ uptake post-Covid-19 pandemic, and its significant evolvement compared to pre-Covid-19 proves green bonds popularity. Our result shows greater resilience of green bonds from specific Covid-19 shocks and uncertainties. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

Asymmetric causality of economic policy uncertainty and oil volatility index on time-varying nexus of the clean energy, carbon and green bond

Vigorously developing the clean energy industry, improving the carbon allowance trading scheme, and issuing green bonds can effectively reduce emissions. To this end, this study aims to investigate the time-varying connections among clean energy, carbon, and green bonds through the DCC-MIDAS model, thus providing a bird's-eye view of their dynamic nexus. A non-parametric causality-in-quantile method is also employed to adequately capture the asymmetric causation of economic policy uncertainty (EPU) and the oil volatility index (OVX) on cross-asset correlations under different market conditions. The primary results imply complicated links among these three assets, with alternating positive and negative trends throughout the sample period. Notably, turbulence in financial markets can exacerbate network connectivity, particularly during the COVID-19 pandemic. Moreover, EPU and OVX can serve as strong predictors across various distributions of cross-market connections, which indicates that co-movement between assets is vulnerable to exogenous risks, especially under normal market conditions. Our findings have broader implications for market participants and policymakers.