Kramerdalby0797

S. COVID-19 reported cases) on the connectedness across the markets, especially at the lower and middle-level quantiles. Overall, these findings prove that the pandemic has been largely responsible for risks transmission across various commodity and financial markets. This is because it has significantly raised investors' and policy uncertainties and immensely altered global financial cycle which in turn results in global flows of capital, and movements in the prices of assets across different financial markets.This paper assesses the role of gold as a safe haven or hedge against crude oil price risks. We employ the asymmetric VARMA-GARCH model, using daily data from January 2016 to August 2020. To account for the impact of COVID-19 pandemic, we partitioned the data into two to reflect the periods before and during the pandemic. Our empirical results find gold as a significant safe haven against oil price risks. The optimal portfolio and hedging analyses conducted also validate the hedging effectiveness of gold against risk associated with oil. The robustness of our results is further confirmed using three other prominent precious metals - silver, platinum, and palladium. In sum, our results are useful for investors and portfolio managers that are desirous of using gold and other precious metals as portfolio rebalancing tools to minimize or circumvent risks associated with volatile oil returns.As mining activity generally occurs far away from metropolitan areas, governments tend to forget the problems that communities in mining regions face. Centralized government systems and, more importantly, a lack of a robust understanding of the effects of mining impacts on communities and regions, explain in part the lag of development in mining regions and countries. This paper discusses these aspects and introduces eight different papers (comprising the Special Issue Territorial development and mining in Chile) discussing the impacts that mining activity brings to local economic activity and societal outcomes using the case of Chile, one of the most important mining countries in the world. Torin 1 We conclude this invitation to a territorial turn in the study of mining-based economic development with a set of key research topics we believe would benefit from further research to improve our understanding of the temporal and spatial dynamics between mining activity and development across territories.The expansion of trade has not only increased imports and exports, but also increased metal consumption embodied in them. Based on China's input-output tables from 1997 to 2017, this study uses structural decomposition analysis (SDA) to analyze China's consumption of embodied metal in imports and exports in each sector, and identify their driving factors at the holistic, industrial and sub-sectoral levels. The results show the following. 1) China is a net importer of embodied ferrous metal and a net exporter of embodied non-ferrous metal, and the change of the embodied metal consumption showed an inverted U-shape. 2) The scale effect was the main driver for these increases; the technology and intensity effect were the primary inhibitor of embodied ferrous and non-ferrous metal consumption, respectively; the structure effect increased metal consumption embodied in imports more than in export. 3) Industry contributed most to the consumption, and the factors were heterogeneous in different industrial sub-sectors the inhibitory effect of technology was more obvious in imports than in exports, and the structure effect promoted more embodied ferrous metal consumption import and more non-ferrous metal consumption export; the intensity effect was promoter before 2007 while its inhibitory effect became more obvious after 2012.4) China's technology level and metal utilization efficiency were still lower than those in foreign countries; the effect of technology to reduce embodied metal consumption was small but had potential impact. Based on these results, relative policy recommendations are proposed.This paper examines the predictive power of oil supply, demand and risk shocks over the realized volatility of intraday oil returns. Utilizing the heterogeneous autoregressive realized volatility (HAR-RV) framework, we show that all shock terms on their own, and particularly financial market driven risk shocks, significantly improve the forecasting performance of the benchmark HAR-RV model, both in- and out-of-sample. Incorporating all three shocks simultaneously in the HAR-RV model yields the largest forecasting gains compared to all other variants of the HAR-RV model, consistently at short-, medium-, and long forecasting horizons. The findings highlight the predictive information captured by disentangled oil price shocks in accurately forecasting oil market volatility, offering a valuable opening for investors and corporations to monitor oil market volatility using information on traded assets at high frequency.Oil and gas are the most important inputs that countries use in their production process. link2 For this reason, changes in oil-gas prices affect economic growth, which is the most important macroeconomic performance indicator. This study aims to investigate whether the relations between the oil-gas prices index and economic growth are permanent in Turkey, covering the period 1998Q1-2019Q4. For this purpose, the relationships between variables are first examined by Granger and Toda-Yamamoto causality tests with structural breaks. Then, we analyze whether the relationships between them are permanent using frequency domain causality tests based on these two tests. There is insignificant causality relationship between the variables according to Granger and the Frequency Domain Causality Test results based on this test. However, according to the results of the Toda-Yamamoto causality test with a structural break, there is a causality relationship from oil-gas prices to economic growth. According to the results of the Frequency Domain Causality Test based on this test, the permanent effect of oil-gas prices on economic growth is approximately five years.Using high-frequency data of crude oil, gold, and silver exchange-traded funds (ETFs) and their related volatility indices, we analyse patterns of intraday return predictability, also called intraday momentum, in each market. We find that intraday return predictability exists in all the markets, but the patterns of predictability differ for each market, with different half-hour returns, not necessarily the first half-hour returns of the trading day, exhibiting significant predictability for their last half-hour counterparts, depending on the specific market. The intraday return predictability is stronger on days of higher volatility and larger jumps. Substantial economic value can be generated by a market timing strategy which is constructed upon the intraday momentum, in all the markets under study. Possible theoretical explanations for the intraday return predictability are infrequent portfolio rebalancing investors and late-informed investors.This paper examines the impacts of COVID-19 on the multifractality of gold and oil prices based on upward and downward trends. We apply the Asymmetric Multifractal Detrended Fluctuation Analysis (A-MF-DFA) approach to 15-min interval intraday data. The results show strong evidence of asymmetric multifractality that increases as the fractality scale increases. Moreover, multifractality is especially higher in the downside (upside) trend for Brent oil (gold), and this excess asymmetry has been more accentuated during the COVID-19 outbreak. Before the outbreak, the gold (oil) market was more inefficient during downward (upward) trends. During the COVID-19 outbreak period, we see that the results have changed. More precisely, we find that gold (oil) is more inefficient during upward (downward) trends. Gold and oil markets have been inefficient, particularly during the outbreak. The efficiency of gold and oil markets is sensitive to scales, market trends, and to the pandemic outbreak, highlighting the investor sentiment effect.This study investigates the short- and long-run determinants of gold price movements in financial markets by taking into account multiple structural breakpoints using an ARDL-based error correction approach. The study used daily time series data from December 19, 2018 to May 15, 2020. The key variables used include international stocks and bond funds that are frequently traded on stock exchanges around the world. The results, based on the fourth breakpoint regime, reveal a significant positive relationship between gold price movements and LSE, Nikkei stocks, T.Rowe global multi-sector bond funds, and CBOE volatility index; and a significant negative association with Gmo emerging country debt and Pimco emerging markets local currency bond funds both in the short- and long-run. Other stocks, like NASDAQ, DJI, S&P500, only revealed negative short-run relationships; except for NYSE that was found to have a positive short-run association with gold price movements. Conversely, Goldman Sachs bonds revealed a significant positive long-run relationship with gold price movements. These results have significant policy implications for gold producers and investors, as both stocks and bonds are an important source of information in the determination of gold price movements both in the short- and long-run.This paper explores the relationship between macro-factors and the realized volatility of commodity futures. Three main commodities-soybeans, gold and crude oil-are investigated using high-frequency data. For macro factors, we select six indicators including economic policy uncertainty (EPU), the economic surprise index (ESI), default spread (DEF), the investor sentiment index (SI), the volatility index (VIX), and the geopolitical risk index (GPR). These indicators represent three dimensions from macroeconomics and capital markets to a broader geopolitical dimension. Through establishing a dynamic connectedness network, we show how these macro factors contribute to the volatility fluctuations in commodity markets. The results demonstrate clearly distinctive features in the reaction to macro shocks across different commodities. Crude oil and gold, for example, are more reactive to market sentiment, whereas DEF contributes the most to the realized volatility of soybeans. link3 Macroeconomic factors and geopolitical risks are more relevant to crude oil volatilities compare to the other two. Our empirical results also reveal the fact that the macro influence on the realized volatility of commodities is time varying.A growing body of literature considers investor sentiment as the partial driver of change in commodity prices. In contrast with previous studies that have almost exclusively focused on linear relationship, this empirical paper investigates the entire dynamic dependence of the quantile of investor sentiment and that of ten important commodities. To do so, we use the novel quantile cross-spectral dependence approach of Baruník and Kley (2019) and the nonparametric causality-in-quantiles test proposed by Balcilar et al. (2017a) over the period 1998-2018. Overall, the results show that the inter-dependence between sentiment and commodity differs according to return quantile and time frequency.