Economic Policy Uncertainty and Emerging Stock Market Volatility

This research examines the effect of economic policy uncertainty (EPU) indices on Pakistan's stock market volatility. Particularly, we examine the impact of the economic policy uncertainty index for Pakistan and bilateral global trading partner countries, the US, China, and the UK. We employ the GARCH-MIDAS model and combination forecast approach to evaluate the performance of economic uncertainty indices. The empirical findings show that the US economic policy uncertainty index is a more powerful predictor of Pakistan stock market volatility. In addition, the EPU index for the UK also provides valuable information for equity market volatility prediction. Surprisingly, Pakistan and China EPU indices have no significant predictive information for volatility forecasting during the sample period. Lastly, we find evidence of all uncertainty indices during economic upheaval from the COVID-19 pandemic. We obtained identical results even during the Covid-19. Our findings are robust in various evaluation methods, like MCS tests and other forecasting windows.

Price Volatility Modeling for the Lumber Futures Market: A Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling Approach

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.Study Implications: The findings of this study can be used to improve hedging strategies, design option pricing formulas, and setting margin requirements. Critical information for price risk management on the lumber market can be gained by lumber market participants from the housing market. For forest management decisions by landowners, giving close attention to housing market would provide valuable information on the appropriate time for timber harvesting, because changes in the housing market affect lumber price that will indirectly affect the demand for timber, which is the most important factor of production for lumber mills.

The return volatility of cryptocurrencies during the COVID-19 pandemic: Assessing the news effect

In this paper, we test the role of news in the predictability of return volatility of digital currency market during the COVID-19 pandemic. We use hourly data for cryptocurrencies and daily data for the news indicator, thus, the GARCH MIDAS framework which allows for mixed data frequencies is adopted. We validate the presupposition that fear-induced news triggered by the COVID-19 pandemic increases the return volatilities of the cryptocurrencies compared with the period before the pandemic. We also establish that the predictive model that incorporates the news effects forecasts the return volatility better than the benchmark (historical average)model.

Why do households repay their debt in UK during the COVID-19 crisis?

Purpose: In this paper, the authors investigate whether coronavirus disease 2019 (COVID-19) impacts household finances, like household debt repayments in the UK. Design/methodology/approach: This paper employs a vector autoregressive (VAR) model that nests neural networks and uses Mixed Data Sampling (MIDAS) techniques. The authors use data information related to COVID-19, financial markets and household finances. Findings: The authors' results show that household debt repayments' response to the first principal component of COVID-19 shocks is negative, albeit of low magnitude. However, when the authors employ specific COVID-19-related data like vaccines and tests the responses are positive, insinuating the underlying dynamic complexities. Overall, confirmed deaths and hospitalisations negatively affect household debt repayments. The authors also report low persistence in household debt repayments. Generalised impulse response functions (IRFs) confirm the main results. As draconian measures, the lockdowns are eased and the COVID-19 shocks are diminishing, and household financial data converge to the levels prior to the pandemic albeit with some lags. Originality/value: To the best of the authors' knowledge, this is the first study that examines the impact of the pandemic on household debt repayments. The authors' findings show that policy response in the future should prioritise innovation of new vaccines and testing. © 2023, Emerald Publishing Limited.

Can multi-source heterogeneous data improve the forecasting performance of tourist arrivals amid COVID-19? Mixed-data sampling approach.

The coronavirus disease (COVID-19) pandemic has already caused enormous damage to the global economy and various industries worldwide, especially the tourism industry. In the post-pandemic era, accurate tourism demand recovery forecasting is a vital requirement for a thriving tourism industry. Therefore, this study mainly focuses on forecasting tourist arrivals from mainland China to Hong Kong. A new direction in tourism demand recovery forecasting employs multi-source heterogeneous data comprising economy-related variables, search query data, and online news data to motivate the tourism destination forecasting system. The experimental results confirm that incorporating multi-source heterogeneous data can substantially strengthen the forecasting accuracy. Specifically, mixed data sampling (MIDAS) models with different data frequencies outperformed the benchmark models.

Challenges for volatility forecasts of US fossil energy spot markets during the COVID-19 crisis

The outbreak of the COVID-19 pandemic led to a slowdown in the world's energy trade and changes in the use of energy resources. Meanwhile, global conditions are complex and can affect fossil energy spot markets, including crude oil, gasoline, heating oil, and natural gas. In this paper, we conduct comparative research to explore the impact of global conditions on fossil energy spot markets during the COVID-19 crisis based on the GARCH-MIDAS framework. We employ a 2010–2022 sample, which we cut off to investigate the differences before and after COVID-19. In-sample estimation shows that all global indicators are significant for forecasting the volatilities of these fossil energy spot prices. Out-sample forecasts reveal that GEPU and GECON outperform GPR and WIP for forecasting these four markets during the pre-COVID-19 period. After the crisis broke out, these global indicators can provide different forecasting information. Hence, this paper can be helpful for decision-makers to formulate and adjust pertinent policies and investments in the case of extreme emergencies in the future. © 2023

Forecasting the volatility of precious metals prices with global economic policy uncertainty in pre and during the COVID-19 period: Novel evidence from the GARCH-MIDAS approach

Economic policy is a major determinant of investment and financial decisions;Moreover, prices of precious metals are highly influenced by any uncertainty recorded in the global economic policy. Therefore, the prime consideration of the authors is to assess how global economic policy uncertainty influences the volatility of precious metals prices;particularly "gold, palladium, platinum, and silver” in the pre and during the COVID-19 pandemic. This research analyzed the full sample period (the 1997–2022), pre-COVID period (1997–2019), and during the COVID period (2020−2022) to evaluate the impact during different sample periods. Therefore, the GARCH-MIDAS approach is employed at the data set of different frequencies, i.e., monthly data of GEPU and daily data of precious metals. The results reveal a significant nexus between global GEPU and precious metals price volatility. The findings infer that any uncertainty recorded in global economic policy escalate the price volatility of gold, palladium, platinum, and silver prices. The present study increments the existing literature and provides insights for future scholars, investors, and policymakers. © 2023 Elsevier B.V.

Predicting volatility in natural gas under a cloud of uncertainties.

The COVID-19 pandemic has triggered an economic crisis and the ensuing global uncertainty. The current Russian-Ukrainian conflict has escalated tensions in various regions and increased various uncertainties in the financial and economic system. These uncertainties have had a significant impact on the development of the natural gas market during the current critical period of carbon neutrality and energy transition. This paper explores the impact of various uncertainties on price volatility in the U.S. natural gas futures market using the GARCH-MIDAS model. We considered eleven types of uncertainties, including four US economic policy uncertainties, four global uncertainty indicators, and oil supply-demand uncertainty closely related to the natural gas market. The in-sample empirical results find that various uncertainties can impact the natural gas market. However, through out-of-sample testing, we find that economic policy uncertainty has more predictive power than other indicators in predicting natural gas price fluctuations. Interestingly, oil supply-demand uncertainty surpasses global indicators and can provide forecasting information for natural gas markets. Therefore, in the current context of high uncertainty, our research may offer better decision-making opinions for market participants.

NOWCASTING REGIONAL ECONOMIC GROWTH IN INDONESIA

This study aims to nowcast gross regional domestic product at the provincial level for Indonesia. The dynamic factor model and mixed data sampling were applied to three sets of variables;namely, macroeconomic, financial, and Google Trends. We find that both methods captured several economic expansions and contractions, including the recent downturn during the COVID-19 pandemic. By including the pandemic period, accuracy across the same set of variables and provinces was slightly reduced. © 2022 The authors.

Challenges for volatility forecasts of US fossil energy spot markets during the COVID-19 crisis

The outbreak of the COVID-19 pandemic led to a slowdown in the world's energy trade and changes in the use of energy resources. Meanwhile, global conditions are complex and can affect fossil energy spot markets, including crude oil, gasoline, heating oil, and natural gas. In this paper, we conduct comparative research to explore the impact of global conditions on fossil energy spot markets during the COVID-19 crisis based on the GARCH-MIDAS framework. We employ a 2010–2022 sample, which we cut off to investigate the differences before and after COVID-19. In-sample estimation shows that all global indicators are significant for forecasting the volatilities of these fossil energy spot prices. Out-sample forecasts reveal that GEPU and GECON outperform GPR and WIP for forecasting these four markets during the pre-COVID-19 period. After the crisis broke out, these global indicators can provide different forecasting information. Hence, this paper can be helpful for decision-makers to formulate and adjust pertinent policies and investments in the case of extreme emergencies in the future.

Real-time Forecasts of State and Local Government Budgets with an Application to the COVID-19 Pandemic

Using a sample of the 48 contiguous US states, we consider the problem of forecasting state governments' revenues and expenditures in real time using models that feature mixed-frequency data. We find that mixed-data sampling (MIDAS) regressions that predict low-frequency fiscal outcomes using high-frequency macroeconomic and financial market data outperform traditional fiscal forecasting models in both a relative and an absolute sense. We also consider an application of forecasting fiscal outcomes in the face of the economic uncertainty induced by the coronavirus pandemic. Overall, we show that MIDAS regressions provide a simple tool for predicting fiscal outcomes in real time.

Forecasting GDP with many predictors in a small open economy: forecast or information pooling?

This study compares two distinct approaches, pooling forecasts from single indicator MIDAS models versus pooling information from indicators into factor MIDAS models, for short-term Singapore GDP growth forecasting with a large ragged-edge mixed frequency dataset. We consider various popular weighting schemes in the literature when conducting forecast pooling. As for factor extraction, both the conventional dynamic factor model and the three-pass regression filter approach are considered. We investigate the relative predictive performance of all methods in a pseudo-out-of-sample forecasting exercise from 2007Q4 to 2020Q3. In the stable growth non-crisis period, no substantial difference in predictive performance is found across forecast models. In comparison, we find information pooling tends to dominate both the quarterly autoregressive benchmark model and the forecast pooling strategy particularly during the Global Financial Crisis. Supplementary Information: The online version contains supplementary material available at 10.1007/s00181-022-02356-9.

The response of household debt to COVID-19 using a neural networks VAR in OECD.

This paper investigates responses of household debt to COVID-19-related data like confirmed cases and confirmed deaths within a neural networks panel VAR for OECD countries. Our model also includes a plethora of non-pharmaceutical and pharmaceutical interventions. We opt for a global neural networks panel VAR (GVAR) methodology that nests all OECD countries in the sample. Because linear factor models are unable to capture the variability in our data set, the use of an artificial neural network (ANN) method permits to capture this variability. The number of factors, as well as the number of intermediate layers, is determined using the marginal likelihood criterion and we estimate the GVAR with MCMC techniques. We also report δ-values that capture the dominance of each individual country in the network. In terms of dominant countries, the UK, the USA, and Japan dominate interconnections within the network, but also countries like Belgium, Netherlands, and Brazil. Results reveal that household debt positively responds to COVID-19 infections and deaths. Lockdown measures such as stay-at-home advice, and closing schools, all have a positive impact on household debt, though they are of transitory nature. However, vaccinations and testing appear to negatively affect household debt.

The influence of COVID-19 epidemic on BDI volatility: An evidence from GARCH-MIDAS model.

In this study, we use the sample data from Jan 22, 2020 to Jan 21, 2022 to investigate the impacts of added infection number on the volatility of BDI. Under this structure, the control variables (freight rate, Brent crude oil price, container idle rate, port congestion level, global port calls) are added to test whether the information contained in the added infection number is covered. In the GARCH-MIDAS model, we divide the volatility of BDI into the long-term and short-term components, then employ in the least squares regression to empirically test the influences of added infection number on the volatility. From the analysis, we find the added infection numbers effectively impact the BDI volatility. In addition, whether the freight rate, Brent crude oil price, container idle rate, port congestion level, global port calls and other variables are considered alone or at the same time, further the added infection number still significantly influences the volatility of BDI. By studying the ability of the confirmed number to explain the volatility of BDI, a new insight is provided for the trend prediction of BDI that the shipping industry can take the epidemic development of various countries as a reference to achieve the purpose of cost or risk control.

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.

A Lead-Lag Relationship and Forecast Research between China's Crude Oil Futures and Spot Markets

The integration of the global economy has led to an increasingly strong connection between the futures and spot markets of commodities. First, based on one-minute high-frequency prices, this paper applies the thermal optimal path (TOP) method to examine the lead-lag relationship between Chinese crude oil futures and spot from March 2018 to December 2021. Second, we apply the Mixed Frequency Data Sampling Regression (MIDAS) model and indicators such as deviation degree to test the degree of prediction of high-frequency prices in the futures market to the spot market. The experimental results show that the futures markets lead the spot market most of the time, but the lead effect reverses when major events occur;60-minute futures high-frequency prices are the most predictive of daily spot data;crude oil futures' predictive power declined after the Covid-19 outbreak and is more predictive when night trading is available. This study has important implications, not only to guide investors but also to provide empirical evidence and valid information for policy makers.

When are the effects of economic policy uncertainty on oil–stock correlations larger? Evidence from a regime-switching analysis

Economic policy uncertainty (EPU) is an important driver of the correlation in the oil–stock nexus. However, whether the effect of EPU on oil–stock correlations across different market conditions is heterogeneous remains unclear. To fill this gap, we combine a dynamic conditional correlation with the mixed data sampling (DCC-MIDAS) model and the Markov regime-switching model to explore the market-state-dependent effects of EPU on oil–stock correlations under different regimes. Empirical results indicate that the impacts of EPU on oil–stock correlations are regime-dependent both at the aggregate and industry levels, with stronger effects in high-correlation regimes, and these effects are more significant in times of economic turmoil. Moreover, the impact of EPU on oil–stock correlations is larger during the COVID-19 pandemic than it was during the Global Financial Crisis. These findings highlight the need to consider the nonlinear impact of EPU under different market conditions.

Negative effects of distance learning accentuated by COVID-19 outbreak: a perspective of learners and teachers

Set in a French higher education context, this paper contributes to the knowledge management literature by arguing that the digital transformation of knowledge transfer via distance learning includes negative outcomes, in addition to many benefits. Based on quantitative and qualitative data, via an online survey from learners and instructors, our findings show that while online modes of delivery are convenient and cost-effective, they overlook many aspects that enable users to engage in knowledge transfer.

U.S. grain commodity futures price volatility: Does trade policy uncertainty matter?

The outbreak and continuation of the COVID-19 pandemic have affected the trade policies of various countries and influenced global food security. This paper aims to use U.S. major grain commodity futures price and trade policy uncertainty (TPU) index data to examine the impact of TPU on the volatility of U.S. grain futures prices under the GARCH-MIDAS framework. The in-sample estimates confirm the impact of TPU on the volatility of US grain commodity futures prices. Out-of-sample testing further reveals that considering TPU could improve predictions of future price fluctuations for different grain commodities. Finally, we also consider other uncertainty indices. Since the grain market is often used as a tool to hedge financial risks, this article can provide some advice for investors in times of policy instability and especially trade policy uncertainty.

Forecasting stock market volatility with a large number of predictors: New evidence from the MS-MIDAS-LASSO model.

This paper explores the effectiveness of predictors, including nine economic policy uncertainty indicators, four market sentiment indicators and two financial stress indices, in predicting the realized volatility of the S&P 500 index. We employ the MIDAS-RV framework and construct the MIDAS-LASSO model and its regime switching extension (namely, MS-MIDAS-LASSO). First, among all considered predictors, the economic policy uncertainty indices (especially the equity market volatility index) and the CBOE volatility index are the most noteworthy predictors. Although the CBOE volatility index has the best predictive ability for stock market volatility, its predictive ability has weakened during the COVID-19 epidemic, and the equity market volatility index is best during this period. Second, the MS-MIDAS-LASSO model has the best predictive performance compared to other competing models. The superior forecasting performance of this model is robust, even when distinguishing between high- and low-volatility periods. Finally, the prediction accuracy of the MS-MIDAS-LASSO model even outperforms the traditional LASSO strategy and its regime switching extension. Furthermore, the superior predictive performance of this model has not changed with the outbreak of the COVID-19 epidemic.