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Our paper has two main objectives. First, we aim to investigate the relationship between global economic contraction (GECON) and the return volatility of gold. Second, we examine the role of climate change as a mediator in this connection. To achieve this, we use the GARCH‐MIDAS model, which accommodates data in different frequencies in the same model. This prevents the loss of important information when aggregating high‐frequency data to lower‐frequency data. We also use alternative measures of GECON from Kilian and Zhou (Journal of International Money and Finance, 2018; 88, 54–78) and Baumeister et al. (Review of Economics and Statistics, 2020;104(4), 828–844) to ensure the robustness of our findings. Our results show that global economic contraction positively impacts the return volatility of gold. Additionally, our findings confirm that the increased uncertainty caused by climate change makes gold a safe haven for investors. This means that gold's return volatility is not negatively impacted by the rising level of uncertainty caused by climate‐induced contraction. Moreover, we note that the index of GECON that accommodates more dynamics can produce more accurate predictability for gold market volatility. Our analysis of stock market volatility further confirms that gold has a safe haven potential relative to the stock market. [ABSTRACT FROM AUTHOR]

In this paper, we employ the generalized autoregressive conditional heteroscedasticity-mixed data sampling (GARCH-MIDAS) framework to forecast the daily volatility of state-level stock returns in the United States (US) based on structurally decomposed four monthly oil shocks associated with oil supply, global economic activity, oil consumption and oil inventory. We find that over the daily period of (February) 1994 to (December) 2022 and various forecast horizons, in 46 out of the 50 states, the GARCH-MIDAS model with at least one oil shock can outperform the benchmark, i.e., the GARCH-MIDAS-Realized Volatility (RV), with 24 states depicting the importance of all the four shocks. In general, oil market-specific shocks, whether supply or demand, tend to matter more than a global economic impact driving the oil market in forecasting volatility of regional stock returns across with better forecasting performances related to states with higher CO2 emissions based on underlying energy consumption data. Our findings have important implications for investors and policymakers, with the observations for the former group depicted by an analysis of economic significance, i.e., utility gains. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to explore the intricate relationship between uncertainty indicators and volatility of commodity futures, with a specific focus on agriculture and energy sectors. Design/methodology/approach: The authors analyse the volatility of Indian agriculture and energy futures using the GARCH-MIDAS model, taking into account different types of uncertainty factors. The evaluation of out-sample predictive capability involves the application of out-sample R-squared test and computation of various loss functions. Findings: The research outcomes underscore the significant impact of diverse uncertainty factors such as domestic economic policy uncertainty (EPU), global EPU (GEPU), US EPU and geopolitical risk (GPR) on long-run volatility of Indian energy and agriculture (agri) futures. Additionally, the study demonstrates that GPR exhibits superior predictive capability for crude oil futures volatility, while domestic EPU stands out as an effective predictor for agri futures, particularly castor seed and guar gum. Practical implications: The study offers practical implications for market participants and policymakers to adopt a comprehensive perspective, incorporating diverse uncertainty factors, for informed decision-making and effective risk management in commodity markets. Originality/value: The research makes an inaugural attempt to examine the impact of domestic and global uncertainty indicators on modelling and predicting volatility in energy and agri futures. The distinctive feature of considering an emerging market also adds a novel dimension to the research landscape. [ABSTRACT FROM AUTHOR]

In recent years, monetary authorities have used unconventional monetary policy practices to stabilize economies. As a result, economic policy uncertainties have increased; subsequently, this has created fragilities in financial markets and exposed investors to greater levels of investment risk. However, recent literature suggests that volatility dynamics differ across industries, with some industries having hedging capabilities. On this basis, this study's objective is to explore the impact of economic policy uncertainty (EPU) on the volatility of different industries in South Africa. The GARCH-MIDAS approach was employed to achieve this objective, and nine industry-specific indices were evaluated from 3 January 2000 to 29 December 2023. The industry-specific analysis revealed that EPU has a negative relationship with the volatility in the following four industries: consumer discretionary, financials, health care, and technology. However, a positive relationship was found for the basic materials industry, while no significant effect was reported for consumer staples, energy, industrials, and telecommunications. Overall, these findings indicate that the EPU effects are asymmetric across industries and, therefore, it follows that the impact of EPU should be accounted for when making asset allocation choices.

Measuring stock market volatility and its determinants is critical for stock market participants, as volatility spillover effects affect corporate performance. This study adopted a novel approach to analysing and implementing GARCH-MIDAS modelling methods. The classical GARCH as a benchmark and the univariate GARCHMIDAS framework are the GARCH family models whose forecasting outcomes are examined. The outcome of GARCH-MIDAS analyses suggests that inflation, interest rate, exchange rate, and oil price are significant determinants of the volatility of the Johannesburg Stock Market All Share Index. While for Nigeria, the volatility reacts significantly to the exchange rate and oil price. Furthermore, inflation, exchange rate, interest rate, and oil price significantly influence Ghanaian equity volatility, especially for the long-term volatility component. The significant shock of the oil price and exchange rate to volatility is present in all three markets using the generalized autoregressive conditional heteroscedastic-mixed data sampling (GARCH-MIDAS) framework. The GARCH-MIDAS, with a powerful fusion of the GARCH model’s volatility-capturing capabilities and the MIDAS approach’s ability to handle mixed-frequency data, predicts the volatility for all variables better than the traditional GARCH framework. Incorporating these two techniques provides an innovative and comprehensive approach to modelling stock returns, making it an extremely useful tool for researchers, financial analysts, and investors. [ABSTRACT FROM AUTHOR]

In recent years, monetary authorities have used unconventional monetary policy practices to stabilize economies. As a result, economic policy uncertainties have increased; subsequently, this has created fragilities in financial markets and exposed investors to greater levels of investment risk. However, recent literature suggests that volatility dynamics differ across industries, with some industries having hedging capabilities. On this basis, this study's objective is to explore the impact of economic policy uncertainty (EPU) on the volatility of different industries in South Africa. The GARCH-MIDAS approach was employed to achieve this objective, and nine industry-specific indices were evaluated from 3 January 2000 to 29 December 2023. The industry-specific analysis revealed that EPU has a negative relationship with the volatility in the following four industries: consumer discretionary, financials, health care, and technology. However, a positive relationship was found for the basic materials industry, while no significant effect was reported for consumer staples, energy, industrials, and telecommunications. Overall, these findings indicate that the EPU effects are asymmetric across industries and, therefore, it follows that the impact of EPU should be accounted for when making asset allocation choices. [ABSTRACT FROM AUTHOR]

This study explores the main factors influencing international oil price fluctuations, selecting five influential variables: the consumer price index (CPI), industrial production index (IPI), global rig count (ADU), economic policy uncertainty index (EPU), and geopolitical risk index (GRI) based on previous literature. Employing the GARCH-MIDAS model, this research analyzes comparative effects on WTI international oil prices. Our findings highlight the varying degrees of influence, with IPI showing a stronger impact and EPU indicating broader economic implications. The GRI index responds primarily to specific geopolitical events with delayed fluctuations. Our study's novelty lies in the empirical investigation using the GARCH-MIDAS model, offering valuable insights for policymakers to manage oil price volatility effectively, particularly by addressing economic policy uncertainty as a critical factor. [ABSTRACT FROM AUTHOR]

The introduction of Bitcoin as a distributed peer-to-peer digital cash in 2008 and its first recorded real transaction in 2010 served the function of a medium of exchange, transforming the financial landscape by offering a decentralized, peer-to-peer alternative to conventional monetary systems. This study investigates the intricate relationship between cryptocurrencies and monetary policy, with a particular focus on their long-term volatility dynamics. We enhance the GARCH-MIDAS (Mixed Data Sampling) through the adoption of the SB-GARCH-MIDAS (Structural Break Mixed Data Sampling) to analyze the daily returns of three prominent cryptocurrencies (Bitcoin, Binance Coin, and XRP) alongside monthly monetary policy data from the USA and South Africa with respect to potential presence of a structural break in the monetary policy, which provided us with two GARCH-MIDAS models. As of 30 June 2022, the most recent data observation for all samples are noted, although it is essential to acknowledge that the data sample time range varies due to differences in cryptocurrency data accessibility. Our research incorporates model confidence set (MCS) procedures and assesses model performance using various metrics, including AIC, BIC, MSE, and QLIKE, supplemented by comprehensive residual diagnostics. Notably, our analysis reveals that the SB-GARCH-MIDAS model outperforms others in forecasting cryptocurrency volatility. Furthermore, we uncover that, in contrast to their younger counterparts, the long-term volatility of older cryptocurrencies is sensitive to structural breaks in exogenous variables. Our study sheds light on the diversification within the cryptocurrency space, shaped by technological characteristics and temporal considerations, and provides practical insights, emphasizing the importance of incorporating monetary policy in assessing cryptocurrency volatility. The implications of our study extend to portfolio management with dynamic consideration, offering valuable insights for investors and decision-makers, which underscores the significance of considering both cryptocurrency types and the economic context of host countries. [ABSTRACT FROM AUTHOR]

We employ the GARCH-MIDAS model to examine whether low-frequency macroeconomic variables help to explain the high-frequency volatility of the foreign investors' net purchase in Korean stock market. The estimation results show that business cycle expansion along with high production growth, high inflation and low unemployment rate predicts high volatility in the near future. Higher interest rate and lower money growth are also likely to lead to higher volatility of foreign investors' net purchase. We also find that domestic macroeconomic variables, relative to the US variables, have a stronger correlation with the future volatility of foreign investors' net purchase. [ABSTRACT FROM AUTHOR]

• War attention is an important driver of stock volatility. • The war attention index uses NLP and dimensionality reduction. • New GARCH-MIDAS models, adding war-attention extreme effects, are used. • War attention index enhances S&P500 volatility predictability. This paper aims to explore the impact of war attention on stock volatility predictability by constructing a new war attention index and employing an extended GARCH-MIDAS-ES model. The war attention index is developed by incorporating the Google search volume data for 56 war-related keywords using natural language processing methods and dimensionality reduction techniques. Since war attention is considered an exogenous shock, we modify the new extended MIDAS model by incorporating the extreme effects caused by war attention into the GARCH-MIDAS-ES framework. Compelling evidence demonstrates that our proposed war attention index is a statistically significant driver of S&P 500 volatility, and our extended model exhibits higher out-of-sample predictive accuracy as it captures both the normal and extreme effects of war attention on stock volatility within the MIDAS framework. By examining how war attention affects stock volatility predictability during the ongoing Russia–Ukraine war, we observe that the extended model's forecasting performance deteriorates as the forecasting horizon increases to a relatively large extent, which is in line with the findings of Andrei and Hasler (2015). [ABSTRACT FROM AUTHOR]

This paper uses the generalized autoregressive conditional heteroscedasticity mixing data sampling (GARCH‐MIDAS) model to construct three types of extended models. Geopolitical risk uncertainty is included in the study as an introduced variable, and its impact on the Shanghai Stock Exchange (SSE) 50 index volatility is analyzed. The empirical analysis shows that the GARCH‐MIDAS‐RV‐EPU model with China's EPU is the best in predicting the volatility of China's stock market when the information of economic policy uncertainty (EPU) and geopolitical risk uncertainty (GPR) of other countries are included. When the common information model composed of China's economic policy uncertainty index and geopolitical uncertainty index is used to predict the volatility of the SSE, the model's prediction is better. Finally, when the model confidence set (MCS) and the interval length index that changes the forecast outside the sample are used to retest each conclusion, the results are very robust. [ABSTRACT FROM AUTHOR]

This study explores the main factors influencing international oil price fluctuations, selecting five influential variables: the consumer price index (CPI), industrial production index (IPI), global rig count (ADU), economic policy uncertainty index (EPU), and geopolitical risk index (GRI) based on previous literature. Employing the GARCH-MIDAS model, this research analyzes comparative effects on WTI international oil prices. Our findings highlight the varying degrees of influence, with IPI showing a stronger impact and EPU indicating broader economic implications. The GRI index responds primarily to specific geopolitical events with delayed fluctuations. Our study’s novelty lies in the empirical investigation using the GARCH-MIDAS model, offering valuable insights for policymakers to manage oil price volatility effectively, particularly by addressing economic policy uncertainty as a critical factor.

The introduction of Bitcoin as a distributed peer-to-peer digital cash in 2008 and its first recorded real transaction in 2010 served the function of a medium of exchange, transforming the financial landscape by offering a decentralized, peer-to-peer alternative to conventional monetary systems. This study investigates the intricate relationship between cryptocurrencies and monetary policy, with a particular focus on their long-term volatility dynamics. We enhance the GARCH-MIDAS (Mixed Data Sampling) through the adoption of the SB-GARCH-MIDAS (Structural Break Mixed Data Sampling) to analyze the daily returns of three prominent cryptocurrencies (Bitcoin, Binance Coin, and XRP) alongside monthly monetary policy data from the USA and South Africa with respect to potential presence of a structural break in the monetary policy, which provided us with two GARCH-MIDAS models. As of 30 June 2022, the most recent data observation for all samples are noted, although it is essential to acknowledge that the data sample time range varies due to differences in cryptocurrency data accessibility. Our research incorporates model confidence set (MCS) procedures and assesses model performance using various metrics, including AIC, BIC, MSE, and QLIKE, supplemented by comprehensive residual diagnostics. Notably, our analysis reveals that the SB-GARCH-MIDAS model outperforms others in forecasting cryptocurrency volatility. Furthermore, we uncover that, in contrast to their younger counterparts, the long-term volatility of older cryptocurrencies is sensitive to structural breaks in exogenous variables. Our study sheds light on the diversification within the cryptocurrency space, shaped by technological characteristics and temporal considerations, and provides practical insights, emphasizing the importance of incorporating monetary policy in assessing cryptocurrency volatility. The implications of our study extend to portfolio management with dynamic consideration, offering valuable insights for investors and decision-makers, which underscores the significance of considering both cryptocurrency types and the economic context of host countries.

• The daily volatility of 19 dollar-based exchange rate returns is forecasted using GARCH-MIDAS model. • Oil price uncertainty and global and country-specific energy market-related uncertainty indexes are considered. • The global EUIs tend to perform better than the OPU. • The country specific EUIs outperform the benchmark in a statistically significant manner. In this paper, we employ the generalized autoregressive conditional heteroscedasticity-mixed data sampling (GARCH-MIDAS) framework to forecast the daily volatility of 19 dollar-based exchange rate returns based on monthly metrics of oil price uncertainty (OPU), and relatively broader global and country-specific energy market-related uncertainty indexes (EUI). We find that the global EUIs tend to perform better than the OPU, highlighting the need to look beyond the oil market to capture energy related uncertainties. The country-specific EUIs outperform the benchmark in a statistically significant manner for at least 14 currencies across the short-, medium-, and long-term forecasting horizons. [ABSTRACT FROM AUTHOR]

The present study examines the impact of uncertainties around climate policy on the stock returns of eight US airlines between 2007 and 2023. To examine how climate policies impact daily airline stock volatility through the long-run component of total volatility, the monthly climate policy uncertainty index is utilized. Using full-sample and out-of-sample estimations, we investigate the problem using the Generalized Autoregressive Conditional Heteroscedasticity-Mixed Data Sampling model. To further assess forecasting accuracy, the Diebold and Mariano as well as Superior Predictive Ability methodologies are applied. According to the full-sample estimation results, just two airlines showed a significant relationship with climate policy uncertainty. Meanwhile, six airlines including three of the "big four" airlines were significantly affected by the former, according to the out-of-sample data. Forecasting results indicate that the climate policy uncertainty-based model outperforms the other models in projecting airline returns. The results have significant theoretical and applied ramifications for comprehending sectoral asset valuations in the context of uncertain climate policy. • We investigate the stock market responses of U.S. airlines to climate policy uncertainty. • GARCH-MIDAS is utilized to analyze airline stock market reactions. • There is a reduced response of airline stocks to climate policy uncertainty, as estimated by the full sample. • The majority of stock prices respond to climate policy uncertainty on the basis of out-of-sample estimates. • The performance of climate policy uncertainty-based model forecasts surpasses that of alternative GARCH models. [ABSTRACT FROM AUTHOR]

• Based on the GARCH-MIDAS model, this paper explores the forecasting power of four transportation sub-sectors. • The empirical results show that the freight volume and passenger traffic of air aviation outperform other models during MCS and DoC tests. • This illustrates the important role of air aviation in the operation of a country's economy. Based on the GARCH-MIDAS model, this paper explores the forecasting power of four transportation sub-sectors. The empirical results show that the freight volume and passenger traffic of air aviation outperform other models during MCS and DoC tests. This illustrates the important role of air aviation in the operation of a country's economy. The findings of this paper could help the government to formulate infrastructure planning and provide insights into the allocation of different resources to different transportation sectors. [ABSTRACT FROM AUTHOR]

• There are information spillovers from energy-related uncertainty to the oil market. • Overall and asymmetric changes in the EUI can affect crude oil market volatility. • The positive/negative behaviors of the EUI offer more valuable information for model. • The GARCH-MIDAS model with double asymmetric produces optimal predictive efficacy. Recently, numerous events, such as trade conflicts, the United Kingdom's exit from the European Union, the Russian-Ukrainian war, and the Palestinian-Israeli conflict, have led to increased risks of uncertainty in global economic, political, and energy markets. However, despite the essential role of crude oil markets in national security and economic development, existing studies have paid little attention to how country-level uncertainty affects oil price volatility. Therefore, in this paper, we investigate whether the energy-related uncertainty index (EUI) proposed by Dang et al. (2023) affects oil price volatility through the GARCH-MIDAS framework. We first analyze whether the EUI can play a role in the oil market through parameter estimation of the model and find evidence of information spillovers from the EUI to the oil market. We then use tests to examine the accuracy of the econometric models used in this paper for WTI volatility forecasting. The test results show that the Double Asymmetric GARCH-MIDAS-EUI model has excellent forecasting performance. Therefore, this work is valuable for governments and corporations in formulating energy policies and business strategies to better cope with the risk of market uncertainty. [ABSTRACT FROM AUTHOR]