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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.

Against the background of growing concern about climate change, this study attempts to examine the impact of US climate policy uncertainty on exchange rate volatility. Specifically, this paper explores whether climate policy changes and their extreme observations, as well as the short-term asymmetry of exchange rate volatility, can help predict future exchange rate volatilities. The in-sample estimation results show that the uncertainty of US climate policy significantly affects the volatility of the US exchange rate, and extreme positive shocks of climate policy uncertainty have a greater impact on the exchange rate than extreme negative shocks of the same magnitude. In addition, we find significant asymmetries in the short-term components of exchange rate volatility, especially in the USD-CNY exchange rate and the USD-EUR exchange rate. Multiple out-of-sample forecasting tests show that models incorporating extreme values of climate policy uncertainty exhibit superior forecasting performance. Notably, the predictive performance of these models tends to be stronger during periods of low volatility and relatively weak during periods of high volatility. This paper provides valuable insights for stakeholders to make informed decisions and optimize strategies in the face of uncertain climate policies. [ABSTRACT FROM AUTHOR]

This paper explores the forecasting power of some of the most informative indicators of economic uncertainty on carbon emission price volatility. We use one- and two-component GARCH-MIDAS models based on mixed frequency data and Model Confidence Set (MCS) testing with 'rolling scheme' forecast method to examine the forecasting performance of economic uncertainty indicators. We employ an economic policy uncertainty (EPU) indicator with China, US and global economic policy uncertainty constituents together with traditional uncertainty indicators, such as macroeconomic and financial market volatility. Our empirical findings show that generally economic policy uncertainty indicators contain more information of carbon emission price volatility than other indicators. Specifically, one-component GARCH-MIDAS model with the EPU indicator with China constituent and two-component model with EPU indicator have superior performance in forecasting carbon emission price volatility of the Guangdong pilot in China. Our study adds insights into factors that affect carbon emission price movements. [ABSTRACT FROM AUTHOR]

Lumber is one of the most essential forest products in the United States. During the first year of the COVID-19 pandemic, lumber prices almost quadrupled, and fluctuations reached record levels. Although market experts have pointed to various drivers of such high price volatility, no firm conclusions have been drawn yet. Using the generalized autoregressive conditional heteroskedasticity-mixed data sampling (GARCH-MIDAS) framework, this study assesses the potential drivers of lumber price volatility, with predictors including the Google Trends Web Search Index, housing starts, US lumber production quantity, and VIX index, representing public attention, housing demand, lumber supply, and macroeconomic concerns, respectively. We have found that housing demand is the key driver of lumber price volatility, followed by public attention. It is worth noting that US lumber supply and macroeconomic concerns have played a modest role in explaining lumber price volatility. Also, forecasting lumber price by using the housing demand variable substantially outperforms others. Market participants, including lumber mills, wholesalers, and home builders can get valuable information from the housing market to manage lumber price risk. [ABSTRACT FROM AUTHOR]

We assess the relationship between regime‐dependent volatility in S&P 500, economic policy uncertainty, the S&P 500 bull and bear sentiment spread (bb_sp), as well as the Chicago Board Options Exchange's VIX over the period 2000–2018. Our findings from two‐covariate GARCH–MIDAS (GM) methodology, regime switching Markov Chain, and quantile regressions suggest that the association of realized volatility and sentiment varies across high‐ and low‐volatility regimes and depends on investors' sensitivity toward incidents of market uncertainties under these regimes. The findings suggest that these indicators may not be useful in volatility forecasting, especially under high‐volatility regimes. [ABSTRACT FROM AUTHOR]

Forecasting international stock market volatility using the oil volatility risk premium (OVRP) is important for asset allocation and financial risk management. In previous literature, the issue of biased OVRP has not been well addressed. The OVRP is defined as the difference between the ex ante risk‐neutral expectation of oil return variation and the oil realized variance. This is a biased estimator as argued by previous studies. In this paper, we propose a new measure of OVRP, namely, the uncertainty‐driven OVRP (UOVRP), in which the expected realized variation is the conditional variance of oil futures returns estimated by a typical GARCH‐MIDAS model, and the long‐term volatility component of conditional variance is directly determined by several uncertainty indices. Using heterogeneous autoregressive models (HAR), we find that UOVRPs significantly and positively predict most of the international stock market volatility in‐sample. Furthermore, it significantly outperforms the realized volatility, the "model‐free" OVRP, and recently proposed volatility predictors for most American, European, and Pacific markets out‐of‐sample. On average, UOVRPs driven by the world uncertainty index and the U.S. economic policy uncertainty perform the best among all UOVRPs. The predictive ability of UOVRPs is robust across a series of checks. [ABSTRACT FROM AUTHOR]

Modeling futures market risk simultaneously influenced by macro low-frequency information and daily risk factors is a valuable challenge. We propose a new general framework for it based on the flexible GARCH-MIDAS model. It uses a skewed t distribution to describe the asymmetry of long and short trading positions, allows for a different number of trading days per month, and can identify the optimal combination of risky factors. We also derive its impact response function on how low-frequency factors directly influence the high-frequency futures market risk. Through an exhaustive empirical analysis of the Chinese soybean futures market, we not only find its excellent out-of-sample market risk forecasting performance but also offer systematic recommendations for improving risk management.

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Introduction/main objectives: The aim of this research is to study the impact of macroeconomic variables on the Indonesian Islamic stock market's volatility. Background issues: To predict the stock market's volatility, daily or high-frequency data has been applied to the model's explanatory variables with the same data frequency. However, when it comes to the macroeconomic variables as volatility drivers, the data is low-frequency, such as weekly, monthly, or quarterly. The current study uses a model which treats the data equally. Novelty: This study employs the mixed data sampling (MIDAS) model, which allows data from multiple frequencies to be included in the same model. This model can combine daily stock returns' data with monthly or quarterly macro-economic data. Hence, this is the first paper to study the determinants of the volatility of Indonesia's Islamic stock index using GARCH-MIDAS. Research Methods: The Generalized Autoregressive Conditional Heteroscedasticity GARCH-MIDAS model captures the short-run from the long-term element of volatility; the findings show the asymmetry effect for the short-term element's result. Finding/Results: Inflation does not influence long-term market volatility. Moreover, after the 2008 crisis, the study shows that inflation and short-term interest rates positively influenced market volatility. Conclusion: The positive effect of inflation suggests that stocks can function as inflation hedges for stock investors in the long run. Further, the positive impact of interest rates implies that Muslim investors use the conventional short-term interest rates as a benchmark for investment in Shari'ah-compliant instruments. [ABSTRACT FROM AUTHOR]

China has been promoting the internationalisation of the RMB for two decades. As a result, its offshore foreign exchange market has been substantially revitalised, despite that China's capital market remains partially open. The movements of offshore exchange rates can provide crucial clues to understanding the offshore market structure and the effects of interventions by Chinese monetary authorities. Using the GARCH-MIDAS model, this paper examines how the degree of openness and economic fundamentals – both observed and unobserved – affect the long-term volatility of offshore exchange rates. We find that, first, trade openness attenuates the long-term volatility, while financial openness has no effect. Specifically, the use of bilateral currency swap agreements and the relaxation of capital control are associated with lower volatility, while stock market connect programs tend to increase the volatility. Second, observed fundamentals, including relative measures for growth, interest rate, and money supply, have significant negative effects on offshore volatility. Third, although economic policy uncertainty and market risk affect long-term volatility, liquidity does not appear to be a culprit of volatility. In terms of FX interventions, direct intervention decreases the volatility, while oral intervention increases volatility. [ABSTRACT FROM AUTHOR]

Extreme shocks (e.g., wars and financial crises) cause violent fluctuations in crude oil volatility. In this paper, we first propose GARCH models in the framework of MIDAS augmented to include the impacts of extreme shocks on oil price volatility. In‐sample results show that extreme shocks can induce the additional volatility of crude oil. Further, the results from out‐of‐sample clearly indicate that the crude oil volatility is best fitted by the EGARCH‐MIDAS‐ES model, which incorporates asymmetric effects in the short‐term component and the significant effect of extreme shocks in the long‐term component. Additionally, robustness tests confirm that the augmented volatility models can produce better prediction results, both statistically and economically, than the conventional GARCH‐MIDAS model. Furthermore, we verify that negative extreme shocks can cause larger volatility, whereas positive extreme shocks of the same magnitude have smaller effects. Our contribution offers fresh insights into energy price volatility forecasting by considering extreme shocks. [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.

Introduction/main objectives: The aim of this research is to study the impact of macroeconomic variables on the Indonesian Islamic stock market’s volatility. Background issues: To predict the stock market’s volatility, daily or high-frequency data has been applied to the model’s explanatory variables with the same data frequency. However, when it comes to the macroeconomic variables as volatility drivers, the data is low-frequency, such as weekly, monthly, or quarterly. The current study uses a model which treats the data equally. Novelty: This study employs the mixed data sampling (MIDAS) model, which allows data from multiple frequencies to be included in the same model. This model can combine daily stock returns’ data with monthly or quarterly macro­economic data. Hence, this is the first paper to study the determinants of the volatility of Indonesia's Islamic stock index using GARCH-MIDAS. Research Methods: The Generalized Autoregressive Conditional Heteroscedasticity GARCH-MIDAS model captures the short-run from the long-term element of volatility; the findings show the asymmetry effect for the short-term element’s result. Finding/Results: Inflation does not influence long-term market volatility. Moreover, after the 2008 crisis, the study shows that inflation and short-term interest rates positively influenced market volatility. Conclusion: The positive effect of inflation suggests that stocks can function as inflation hedges for stock investors in the long run. Further, the positive impact of interest rates implies that Muslim investors use the conventional short-term interest rates as a benchmark for investment in Shari’ah-compliant instruments.

This paper establishes a predictive relationship between financial vulnerability and volatility in emerging stock markets. Focusing on China and India and utilizing GARCH-MIDAS models, we show that incorporating financial vulnerability can substantially improve the forecasting power of standard macroeconomic fundamentals (output growth, inflation and monetary policy interest rate) for stock market volatility. The findings have significant implications for investors to improve the accuracy of volatility forecasts. [ABSTRACT FROM AUTHOR]

Volatility is a key concept for understanding the dual relationships between the economic variables since it is inversely related to the stability of economies. Many models such as GARCH models have been constructed through time to understand which determinants and conditions can affect the volatility. These models mostly show the significant relationships between the volatilities generated by the low frequency macroeconomic activities and the high frequency financial variables in a stochastic way. However, it is required to check whether there exist deterministic effects of volatilities on high frequency economic variables. In order to reveal these deterministic effects, we developed a new component-wise model, namely GARCH-M MIDAS model. We formulate this model on stock prices and exchange rates, in which the long run volatility is driven by consumer price and industrial production indexes in a separate way. Hence, our empirical analyses support that both types of volatilities have statistically significant deterministic effects on the asset pricing of high frequency financial variables. We also find that macroeconomic activities have a significant role on the asset pricing in long horizons.

In this paper, GARCH‐MIDAS model is used to study the influence of macro economy, including the levels and volatilities of macroeconomic variables, on the price fluctuations of crude oil market during the period from 1990 to 2018. The results indicate that, for the levels of macroeconomic variables, economic growth, inflation and total export–import volume have significant negative impacts on oil fluctuations. However, exchange rate has a significant positive impact on oil volatility. And short‐run interest rate does not have a significant effect on oil price volatility in most cases. In addition, the responses of oil price volatility to all five macroeconomic fluctuations are significant. More interestingly, the volatilities of economic growth and total import and export reduce oil price fluctuations during the pre‐2004 period, but they increase oil price fluctuations during the post‐2004 period. [ABSTRACT FROM AUTHOR]

This paper focuses on distinguishing the predictive power of five newly developed uncertainty indices, that is, global and US economic policy uncertainty (GEPU and US EPU), global geopolitical risk (GPR), US monetary policy uncertainty (MPU) and equity market volatility (EMV), on crude oil market volatility. We construct the extended GARCH‐MIDAS models to assess the forecasting ability of these indices and then compare them with four commonly used oil volatility drivers: oil supply, oil demand, oil speculation and interest rate. Firstly, the in‐sample analysis suggests that all uncertainty indices and traditional determinants have significant impacts on oil volatility. Secondly, the out‐of‐sample evaluations suggest that US MPU, EMV and EPU indices are more powerful than other predictors to improve the forecasting accuracy of crude oil volatility, and US EMV exhibits the best prediction ability. Then, we find that EPU and MPU indices are more effective in forecasting high volatility in crude oil market, while traditional determinants together with US EMV index are more helpful in predicting low oil volatility. Finally, the forecasts of bad oil volatility can be improved by all predictors except for GEPU index, while the forecasts of good oil volatility can only be enhanced by US MPU, EMV and EPU. [ABSTRACT FROM AUTHOR]

Abstract This study investigates the impact of economic policy uncertainty (EPU) on the volatility of European Union (EU) carbon futures prices and whether it has predictive power for the volatility of carbon futures prices. The GARCH-MIDAS model is applied for evaluating the impact of different EPU indexes on the price volatility of European Union Allowance (EUA) futures. We then compare the predictive power for the volatility of the two GARCH-MIDAS models based on different EPU indexes and six GARCH-type models. Our empirical results show that the GARCH-MIDAS models, which exhibit superior out-of-sample predictive ability, outperform GARCH-type models. The results also indicate that EPU has noticeable effect on the volatility of EUA futures. Specifically, the forecast accuracy of the EU EPU index is significantly higher than that of the global EPU index. Robustness checks further confirm that the EPU index (especially the EPU index of the EU) has strong predictive power for EUA futures prices. Additionally, using the volatility forecasting methods that GARCH-MIDAS models combine with the EPU index, investors can construct their portfolios to realize economic returns.

Despite the soaring popularity of Exchange Traded Funds (ETFs) in South Africa, country risk may have a minimal or no effect on ETFs because ETF investors can use a wide variety of market timing activities to minimize their exposure to country risks. This study investigated the effect of political, economic, and financial components of country risk on the volatility of the South African ETF market. A GARCH-MIDAS approach was employed to analyse a sample of South African ETFs from November 2000 to December 2019. The ETF market was segregated into a market of ETFs with domestic benchmarks and a market of ETFs with international benchmarks. The findings suggest that country risk components are significant sources of volatility in ETF markets except for financial risk which does not significantly impact ETFs with international benchmarks suggesting that these ETFs can be used to minimize an investor's exposure to financial risk. Overall, this study provides new insight into the use of ETFs to diversify an investor's exposure to different country risk components. [ABSTRACT FROM AUTHOR]

• 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]

We investigate the macroeconomic determinants of stock market volatility in China using the two-component GARCH-MIDAS model of Engle et al. (Rev Econ Stat 95:776–797, 2013). Our analysis shows that both current macroeconomic conditions and macroeconomic expectations impact the long-term component of stock volatility. Chinese macroeconomic data contain more information about Chinese stock market volatility than US macroeconomic data. We provide strong evidence that the long-term volatility is procyclical and increases with the growth rate of industrial production and retail sales. This finding can be explained by the fact that, in China, the volatility of macroeconomic fundamentals is itself procyclical. Finally, we find that specifications that include macroeconomic variables generate superior stock volatility predictions compared to alternative models that do not contain those variables. [ABSTRACT FROM AUTHOR]

Being more and more popular in the past 10 years, Bitcoin has drawn extensive attention from the press, scholars, and practitioners. The aim of this paper is to investigate which predictor is more predictive for Bitcoin volatility from the aspects of in‐sample and out‐of‐sample in a high‐speed changing world. We utilise the GARCH‐MIDAS model to examine the predictive power of five crucial predictors, including VIX, GVZ, Google Trends, GEPU, and GPR. Our findings provide strong evidence that GVZ exhibits strongest predictability for Bitcoin volatility over other competing predictors. Other empirical results based on different out‐of‐sample forecasting periods, alternative loss functions and combination methods further ensure our major conclusions are robust. [ABSTRACT FROM AUTHOR]

We investigate the impact of the global economic policy uncertainty (GEPU) on stock volatility for nine emerging economies (Brazil, Russia, India, China, South Africa, Mexico, Indonesia, South Korea, and Turkey). We employ an expanded GARCH-MIDAS approach to connect low-frequency GEPU data and high-frequency stock data, assuming that GEPU affects stock volatility via the long-run component of total volatility. We not only use DM and SPA tests to statically evaluate the out-of-sample forecasting ability of the extended model, taking traditional GARCH model and GARCH-MIDAS model as benchmarks, but also use the Fluctuation test to examine the time-varyingly relative forecasting performance in the presence of potential instability. From the in-sample estimation results, we find that GEPU has empirically significant impact on stock volatility for the nine emerging economies. The out-of-sample forecasting results show that the GEPU-based model can improve forecasting performance of stock volatility for emerging markets, especially in unstable environments.

The paper employs a generalized autoregressive conditional heteroskedasticity-mixed data sampling (GARCH-MIDAS) model to examine the relationship between macroeconomic conditions and US stock market volatility at the industry level, with three main findings. First, some macroeconomic factors, such as the term spread, the housing starts, the National Activity Index, the change in unemployment rate, and the default rate, have noticeable effects on industry volatility. Second, out-of-sample test results show that the GARCH-MIDAS model with macroeconomic series helps improve forecasting performance mainly in the consumer staples sector, but its performance is comparable to that of the GJR-GARCH(1,1) model in other sectors. Finally, on average, the term spread contributes the most to fluctuations during expansions, whereas the default rate assumes the most significant role during recessions. As for quantitative easing, although it seems to reduce the scale of stock volatility and to cause the volatilities across sectors associated with macroeconomic series to reach the values observed in expansions, the volatility attributed to the default rate is approaching its average level in recession periods. [ABSTRACT FROM AUTHOR]

Gold as a vital hedging asset plays increasing critical roles in risk management during turmoil macroeconomic environments. For the massive and indistinct impactors of gold price volatility, this paper tries to investigate whether the short‐ and long‐term asymmetry, extreme observations, and jump components in past gold volatility help to obtain higher forecasting accuracy in future volatility from both in‐sample and out‐of‐sample perspectives. A variety of evaluation methods are utilized to compare the performances of GARCH‐MIDAS models incorporating these volatility components and the standard ones without them. The results of in‐sample estimation show first that all the short‐term and long‐term asymmetry, extreme observations, and jump components have significantly impact on gold volatility. The evaluation results of out‐of‐sample forecasts suggest that the forecasting accuracy of gold volatility can be significantly improved by most of the extended GARCH‐MIDAS models including asymmetry, extreme observations, and jump components. The model including short‐term jump intensity and the model with both long‐term asymmetry and long‐term leverage effects have better forecasting performances than other models for gold volatility, especially for regular volatility. Moreover, GARCH‐MIDAS models incorporating long‐term leverage and long‐term jump have better performances in forecasting accuracy of extreme gold volatility. [ABSTRACT FROM AUTHOR]

This study investigates the impact of economic policy uncertainty (EPU) on the volatility of European Union (EU) carbon futures prices and whether it has predictive power for the volatility of carbon futures prices. The GARCH-MIDAS model is applied for evaluating the impact of different EPU indexes on the price volatility of European Union Allowance (EUA) futures. We then compare the predictive power for the volatility of the two GARCH-MIDAS models based on different EPU indexes and six GARCH-type models. Our empirical results show that the GARCH-MIDAS models, which exhibit superior out-of-sample predictive ability, outperform GARCH-type models. The results also indicate that EPU has noticeable effect on the volatility of EUA futures. Specifically, the forecast accuracy of the EU EPU index is significantly higher than that of the global EPU index. Robustness checks further confirm that the EPU index (especially the EPU index of the EU) has strong predictive power for EUA futures prices. Additionally, using the volatility forecasting methods that GARCH-MIDAS models combine with the EPU index, investors can construct their portfolios to realize economic returns. [ABSTRACT FROM AUTHOR]

This paper focuses on the role of the macroeconomic outlook in explaining the returns volatility from 1998 to 2018 in Morocco. Our findings show that the inclusion of low-frequency macroeconomic information can improve the explaining ability, particularly for the long-term variance component. Information in some indicators which represent forward-looking variables (i.e. international economic situation, interest rates, exchange rates and inflation) seem to take into account the current economic situation, and remain useful in the Moroccan stock market development. Nevertheless, the historical realized volatility exceeds all models with macro-finance data in terms of explaining the long-term volatility. Therefore, it is noteworthy that investors and decision makers are considered to be more affected by the stock results, namely the past returns performance and the historical volatility. In general, modeling the long-term volatility component has a great potential and is very useful for portfolio selection, hedging decisions and macroeconomic risk management. [ABSTRACT FROM AUTHOR]

This paper proposes a novel GARCH-MIDAS-SK (G-M-SK) model that improves the basic GARCH-MIDAS (G-M) model by incorporating time-varying skewness and kurtosis. We employ our model with data concerning macroeconomic fundamentals to investigate in-sample fit and out-of-sample prediction of volatility in crude oil prices. Empirical results suggest that G-M-SK models produce the better in-sample fit than basic G-M models do. This result is also robust for the subsample of the oil market. It is equally noteworthy that modelling low-frequency macroeconomic variables better reveals the long-term volatility from time-varying skewness and kurtosis. More importantly, G-M-SK model significantly and robustly improves accuracy in predicting oil volatility. In particular, we find that data regarding macroeconomic fundamentals contribute more to forecasting volatility in oil prices than their variance does. Finally, our G-M-SK model more precisely calculates the utility incident to minimizing risk and allocating portfolios. Its results are consistent with out-of-sample forecasting results. These results are beneficial to the decision-making of the crude oil investors and policymakers alike. [ABSTRACT FROM AUTHOR]

Effectively explaining and accurately forecasting industrial stock volatility can provide crucial references to develop investment strategies, prevent market risk and maintain the smooth running of national economy. This paper aims to discuss the roles of industry-level indicators in industrial stock volatility. Selecting Chinese manufacturing purchasing managers index (PMI) and its five component PMI as the proxies of industry-level indicators, we analyze the contributions of PMI on industrial stock volatility and further compare the volatility forecasting performances of PMI, macroeconomic fundamentals and economic policy uncertainty (EPU), by constructing the individual and combination GARCH-MIDAS models. The empirical results manifest that, first, most of the PMI has significant negative effects on industrial stock volatility. Second, PMI which focuses on the industrial sector itself is more helpful to forecast industrial stock volatility compared with the commonly used macroeconomic fundamentals and economic policy uncertainty. Finally, the combination GARCH-MIDAS approaches based on DMA technique demonstrate more excellent predictive abilities than the individual GARCH-MIDAS models. Our major conclusions are robust through various robustness checks. [ABSTRACT FROM AUTHOR]

This article takes into account the form of mixed data as well as the peak and thick tail characteristics contained in the data characteristics, expands the GARCH-MIDAS (Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling) model, establishes a new GARCH-MIDAS model with the residual term of the skewed-t distribution, and analyzes the influence factors of crude oil futures price volatility, which can better explain the changing laws of crude oil price volatility. The results show the following: First, the low-frequency factors include crude oil production, consumption, inventory, and natural gas spot price, and the high-frequency factors include on-market trading volume and off-market spot price, which can significantly explain the volatility of oil price. Second, low-frequency factors include crude oil inventory, consumption, crude oil production, and speculative factors, and high-frequency factors include crude oil spot price and substitute prices. The increase in the volatility of trading volume is significantly positively correlated with oil price volatility, and the overall volatility model outperforms the horizontal effect model. Third, from the perspective of the combined effect of a single factor level and volatility, we find that supply and demand are the low-frequency factors; the trading volume of on-market factors, natural gas price, and crude oil spot price of off-market factors, among the high-frequency factors, are the most important factors affecting oil price volatility. Fourth, from the perspective of high-frequency and low-frequency effects combined, there is no significant difference between the various factor models, which shows that the mixed effect model of high and low frequency models has advantages in terms of the stability of the estimation results. [ABSTRACT FROM AUTHOR]

This paper introduces a novel generalized autoregressive conditional heteroskedasticity–mixed data sampling–extreme shocks (GARCH‐MIDAS‐ES) model for stock volatility to examine whether the importance of extreme shocks changes in different time ranges. Based on different combinations of the short‐ and long‐term effects caused by extreme events, we extend the standard GARCH‐MIDAS model to characterize the different responses of the stock market for short‐ and long‐term horizons, separately or in combination. The unique timespan of nearly 100 years of the Dow Jones Industrial Average (DJIA) daily returns allows us to understand the stock market volatility under extreme shocks from a historical perspective. The in‐sample empirical results clearly show that the DJIA stock volatility is best fitted to the GARCH‐MIDAS‐SLES model by including the short‐ and long‐term impacts of extreme shocks for all forecasting horizons. The out‐of‐sample results and robustness tests emphasize the significance of decomposing the effect of extreme shocks into short‐ and long‐term effects to improve the accuracy of the DJIA volatility forecasts. [ABSTRACT FROM AUTHOR]

We consider the problem of testing for an omitted multiplicative long-term component in GARCH-type models. Under the alternative, there is a two-component model with a short-term GARCH component that fluctuates around a smoothly time-varying long-term component which is driven by the dynamics of an explanatory variable. We suggest a Lagrange multiplier statistic for testing the null hypothesis that the variable has no explanatory power. We derive the asymptotic theory for our test statistic and investigate its finite sample properties by Monte Carlo simulation. Our test also covers the mixed-frequency case in which the returns are observed at a higher frequency than the explanatory variable. The usefulness of our procedure is illustrated by empirical applications to S&P 500 return data. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

The process of opening the Chinese stock market has sped up in the last two decades. This paper investigates the effects of the uncertainty in the US measured by news-implied volatility on the Chinese stock market volatility. Our empirical results for the full sample period (1997–2016) reveal that there is no clear effect of US uncertainty on fluctuations in China's stock market. However, based on the subsample analysis along the opening progress in the Chinese stock market, we observe that US uncertainty had a significant effect on Chinese stock market volatility after Qualified Foreign Institutional Investors (QFII) and Renminbi QFII were allowed to invest in the Chinese stock market. The results are the same after the global financial crisis. Our results imply that the US uncertainty should be considered by investors deciding on investment strategy. The government should also consider the US uncertainty when enacting the policy on opening policy the Chinese stock market. [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]