The Protective Nature of Gold During Times of Oil Price Volatility: An Analysis of the COVID-19 Pandemic.

This research explores gold's safe-haven properties amid oil price instability, focusing on the COVID-19 pandemic. The study examines how gold hedges against oil price swings in the context of the pandemic's exceptional market circumstances. A VAR (Vector Autoregressive) model analyzes gold and oil prices from 2006 through 2021. The VAR model reflects the dynamic interactions and interdependencies between these two essential commodities in the context of oil price volatility and the COVID-19 pandemic. This analysis shows that gold protects against oil price volatility and the COVID-19 pandemic-gold buffers against oil price swings due to its strong inverse association with oil prices. Gold offers investors security and asset preservation during significant oil price volatility. In light of oil price volatility and the COVID-19 pandemic, the study helps explain gold's importance as a diversification tool and haven asset. Investors, policymakers, and market players should consider gold as a hedge against oil price volatility and economic instability.

Pass-through Effects of Oil Prices on LATAM Emerging Stocks before and during COVID-19: An Evidence from a Wavelet-VAR Analysis

Vector Auto regression model (VAR) a time-varying parameter is applied to study the effect of oil price shocks on the returns of stocks in the LATAM (Latin American) markets. Coherent Wavelet analysis highlights possibilities of connectedness of the oil price and LATAM stock markets through the presence of different patterns in a time series. The structural demand shocks standard deviations during the COVID-19 era remain high and the pass-through effects on stock returns due to oil prices differ for different time frames. The fundamental linkages are demonstrated due to oil market specific demand. The main motive of the research work is to identify the influence of oil price on stocks and identify the fundamental source of contagion.A random effects model is applied to the panel data of LATAM markets with the Global stock market index, MSCI (Morgan Stanley Capital International World Index), domestic money market rates and currency exchange rates during the period of study, 15 March 2019 to 31 July 2021 with 684 observations of controlled non-observed characteristics from individual country. The findings of this research recommend the pass-through effect of the oil prices on the stock market returns are based on time frequency. The contribution of this paper helps the policy makers to restore the confidence amongst the investors in the stock markets and strategies to be adopted by the investors to mitigate the risk by ideal portfolio management. © 2023, Econjournals. All rights reserved.

On the volatility of WTI crude oil prices: A time-varying approach with stochastic volatility

This study investigates the impacts of crude oil-market-specific fundamental factors and financial indicators on the realized volatility of West Texas Intermediate (WTI) crude oil price. A time-varying parameter vector autoregression model with stochastic volatility (TVP-VAR-SV) is applied to weekly data series spanning January 2008 to October 2021. It is found that the WTI oil price volatility responds positively to a shock in oil production, oil inventories, the US dollar index, and VIX but negatively to a shock in the US economic activity. The response to the EPU index was initially positive and then turned slightly negative before fading away. The VIX index has the most significant effect. Furthermore, the time-varying nature of the response of the WTI realized oil price volatility is evident. Extreme effects materialize during economic recessions and crises, especially during the COVID-19 pandemic. The findings can improve our understanding of the time-varying nature and determinants of WTI oil price volatility. © 2022

Do oil, gold and metallic price volatilities prove gold as a safe haven during COVID-19 pandemic? Novel evidence from COVID-19 data.

The spreading COVID-19 outbreak has wreaked havoc on the world's financial system that raises an urgent need for the re-evaluation of the gold as safe haven for their money because of the unprecedented challenges faced by markets during this period. Therefore, the current study investigates whether different asset class volatility indices affect desirability of gold as a safe-haven commodity during COVID-19 pandemic. Long run and the short run relationship of gold prices with gold price volatility, oil price volatility, silver price volatility and COVID-19 (measured by the number of deaths due to COVID) has been analyzed in the current study by applying ARDL Bound testing cointegration and non linear ARDL approach on daily time series data ranging from January 2020 to Dec 2021. Findings of the study suggest that in the long run, oil price volatility and gold price volatility positively affect the gold prices, whereas the effect of silver price volatility on gold prices is negative in the long run. However in the short run, all the three indices negatively impact the gold prices. In contrast, the impact of COVID-19 is positive both in the short run and in the long run that proves the validity of gold as safe haven asset in the time of the deadly pandemic. The findings of this study have significant implications and offer investors with some indications to hedge their investments by considering the gold's ability of safe haven during this era of pandemic.

The impacts of oil price volatility on financial stress: Is the COVID-19 period different?

This study analyses the effects of oil price volatility on financial stress with various measures for a large panel of countries. The study places a special focus on comparing the pattern of these effects during the Great Recession period and the COVID-19 recession period. Using the local projection approach, the paper finds that oil price volatility has a positive and persistent effect on financial stress. However, the magnitude and the degree of persistency of oil price volatility impacts on financial stress are much greater for the Great Recession period than for the COVID-19 recession period. A possible explanation for this result would be that COVID-19 is better thought of as a "natural disaster” in which companies under stress were not being mismanaged. Another explanation would be that active intervention by the government through monetary and fiscal channels reduces the sensitivity of financial instability to oil price volatility during the COVID-19 period.

Assessing oil price volatility co-movement with stock market volatility through quantile regression approach

In times of financial crisis as well as during the COVID-19 era, gold and crude oil are the two commodities that have the most influence on global stock markets and the real economy. But research has mostly focused on the effects of these commodities' prices on their own, and the volatility of these commodities' prices as a whole hasn't gotten much attention. This research paper evaluates the impacts of crude oil prices volatility on shares marketplaces. This research examines the impact of crude oil uncertainty on the overall market returns in several economic sectors (China, Japan, the USA, France, and Germany) between 2000 and 2020 using the importance of the crude oil prices volatility index by applying a Quantile-on-Quantile regression(Q-Q), including dynamic copula with Markov-Switching. The results depict that because the effect of the OVX changes crosswise every single quantile level of stock return, it is cumbersome to ascertain the changes within the adverse impacts under varied stock market circumstances. Equally, to derive a comprehensive understanding of the correlation between crude costs volatility and stock returns, we utilize twofold quantile regression and quantile-quantile regression methods. We interpret these different features' impacts by applying the quantile regression approximates. Our experiential findings show that crude costs uncertainty has lop-sided impacts on stock returns;more so, these disproportionate performances alternate based not merely on the level of shares returns nonetheless equally crude market circumstances. More so, greater values match a robust risk decrease. Further, we observed heterogenous hedging effectiveness among the varied United States stock sectors. The findings demonstrate that growing crude prices volatility obtains an adverse impact on stock returns when the dual crude costs volatility and stock returns are minimal. Nevertheless, when shares returns are greater plus crude prices, volatility is minimal;growing crude volatility increases stock returns.

On the volatility of WTI crude oil prices: A time-varying approach with stochastic volatility

This study investigates the impacts of crude oil-market-specific fundamental factors and financial indicators on the realized volatility of West Texas Intermediate (WTI) crude oil price. A time-varying parameter vector autoregression model with stochastic volatility (TVP-VAR-SV) is applied to weekly data series spanning January 2008 to October 2021. It is found that the WTI oil price volatility responds positively to a shock in oil production, oil inventories, the US dollar index, and VIX but negatively to a shock in the US economic activity. The response to the EPU index was initially positive and then turned slightly negative before fading away. The VIX index has the most significant effect. Furthermore, the time-varying nature of the response of the WTI realized oil price volatility is evident. Extreme effects materialize during economic recessions and crises, especially during the COVID-19 pandemic. The findings can improve our understanding of the time-varying nature and determinants of WTI oil price volatility.

Role of oil price volatility, energy efficiency, and financial stability on sustainable energy production

In the COVID-19 crisis, many economies suffered from sustainable energy production. The emergence of the COVID-19 crises, extreme volatility in oil prices, limited energy efficiency in energy systems, and weak form of financial stability were the key reasons for it. However, considering these issues, a recent study aims to analyze them. ASEAN countries' energy efficiency and crude oil price volatility are examined as a solution to how financial conditions might be utilized to handle energy efficiency issues and crude oil price volatility. Extending it, the study aims to identify the influence of financial stability on crude oil price volatility and energy efficiency issues. To do this, GMM is used. According to the study's findings, environmental mitigation was determined to be important at 18%, and financial stability and carbon risk significant at 21%. Global warming concerns have been raised due to the ASEAN nations' 19.5% link between financial stability and emissions drift. A country's financial stability is necessary for implementing green economic recovery strategies, among the most widely accepted measures to reduce energy efficiency and guarantee long-term financial potential on the national scale. The study on green economic growth also provides the associated stakeholders with sensible policy consequences on this importance.

Testing the fluctuations of oil resource price volatility: A hurdle for economic recovery

The influence of oil price volatility on significant international macroeconomic indicators is examined empirically. The vector auto-regression (VAR) system is used to examine the influence of oil price volatility. According to the Granger causality test, impulse response functions, and variance decomposition, economic recovery and investment have been significantly affected by oil price volatility from 2000Q1 to 2020Q4. According to this research, business investment and oil prices have shown great power throughout the international economic meltdown. Volatility in economic activity and oil prices are expected during this crisis, according to the recent COVID-19 outbreak. Furthermore, in the international financial crisis and COVID-19 crises, oil prices and economic growth are strongly linked. We propose that the COVID-19 epidemic and the global financial problems have major effects on economic activity when oil prices fall. The COVID-19 epidemic had the greatest total connectedness between oil prices and economic activities, which suggests that the speed of information propagation between the oil market and financial initiatives was greater during the COVID-19 outbreak than during past global financial crises. There are important consequences for policymakers based on the findings of this research.

Oil tail-risk forecasts: from financial crisis to COVID-19

The coronavirus outbreak has caused unprecedented volatility in oil prices. This paper extends previous studies on oil Value-at-Risk (VaR) by providing extra insights into Expected Shortfall (ES) forecasting over the last decade, including several oil crises. We introduce a conditional volatility model combined with the Cornish-Fisher expansion for ES forecasting. In comparison to the widely used volatility models and innovation distributions, this approach is superior for predicting the ES of long positions but overestimates VaR for short positions. Overall, the volatility model addressing leverage effects with skewed t innovation produces the most accurate joint VaR and ES forecasting. Moreover, the magnitude of ES relative to VaR varies across models and time, implying that ES should be used in conjunction with VaR to inform timely risk management decisions. The results would be of interest to the regulatory authorities, energy companies, and financial institutions for oil tail-risk forecasting.

Feverish sentiment, lockdown stringency, oil volatility, and clean energy stocks during COVID-19 pandemic

Purpose - This study seeks to investigate role of the coronavirus disease 2019 (COVID-19) pandemic on clean energy stocks for the United States for the period 21 January 2020-16 August 2021. Design/methodology/approach - At the empirical stage, the Fourier-augmented vector autoregression approach has been used. Findings - According to the empirical results, the response of the clean energy stocks to the feverish sentiment, lockdown stringency, oil volatility, dirty assets, and monetary policy dies out within a short period of time. In addition, the authors find that there is a unidirectional causality from the feverish sentiment index and the lockdown stringency index to the clean energy stock returns;and from the monetary policy to the clean energy stocks. At the same time, there is a bidirectional causality between the lockdown stringency index and the feverish sentiment index. The empirical findings can be helpful to both practitioners and policy-makers. Originality/value - Among the COVID-19 variables used in this study is a new feverish sentiment index, which has been constructed using principal component analysis. The importance of the feverish sentiment index is that it allows us to examine the impact of the aggregate level of fear in the economy on clean energy stocks.

Will Oil Price Volatility Cause Market Panic?

It is generally known that violent oil price volatility will cause market panic;however, the extent to which is worthy of empirical test. Firstly, this paper employs the TVP-VAR model to analyze the time-varying impacts of oil price volatility on the panic index using monthly data from January 1990 to November 2021. Then, after using the SVAR model to decompose the oil price volatility, this paper uses the PDL model to analyze the heterogeneous impacts of oil price volatility from different sources. Finally, based on the results of oil decomposition, this paper uses the TARCH model to analyze the asymmetric impacts of oil price volatility in different directions. The results show that: (1) oil price volatility can indeed cause market panic, and these impacts exhibit time-varying characteristics;(2) oil price volatility from different sources has different impacts on the panic index, and the order from high to low is oil-specific demand shocks, supply shocks, and aggregate demand shocks;and (3) oil price volatility has asymmetric impacts on the panic index, and positive shocks have greater impacts than negative.

Testing oil price volatility during Covid-19: Global economic impact.

This paper is occasioned by the current events in the crude oil markets throughout the Covid pandemic time. The study analyzes the evolving nature of crude oil cost unpredictability caused by the variations that influence the crude sector throughout the current contagion. Every day's dataset is within the first month of 2020 and December 30, 2021 were measured by applying VAR and GARCH models. The results corroborate that the current contagion has adverse effects on the crude sector, primarily in two ways. It resulted in the headwinds for demand and cut international demand for crude oil, increasing uncertainty for major advanced and developing nations. Next, it resulted in output headwinds as the pandemic caused hydrocarbons conflicts among the leading crude supplying countries. The two headwinds seem to have caused the more than necessary crude unpredictability. Moreover, it was found that the United States output, total requirements, and crude-leaning demand shocks adversely affect the supply unpredictability of the United States and the extractive sectors. The findings depict that crude price instability responded significantly to the contagion caused by crude headwinds. Specifically, the study recorded the effect of uncertainty because of these headwinds beyond financiers' concerns about crude price instability. This study indicates that spillovers do not have meaningful forecast data, igniting critical debates concerning the relevance of the spillover indicator for predicting at minimal sampling occurrence.

Oil value-at-risk forecasts: a filtered semiparametric approach

The Covid-19 pandemic has set the stage for greater volatility in oil prices. Given this unprecedentedly volatile environment, protection against market risk has never been more important. Value-at-risk (VaR) is a popular metric to measure and control risk. However, the widely used historical simulation approach is unresponsive to upticks in stress. Therefore, the need has arisen for an alternative method that is easy to implement while still achieving forecast accuracy. We propose the generalized autoregressive conditional heteroscedasticity (GARCH) model combined with the Cornish–Fisher expansion (a semiparametric approach to address skewness and excess kurtosis as well as volatility dynamics) for the oil VaR forecast. We com-pare the performance of the proposed approach with that of historical simulation and GARCH-type models with alternative residual distributions: historical simulation, normal, skewed Student t and generalized Pareto. The study is based on the daily spot data from the Energy Information Administration for the period from December 19, 2012 to October 30, 2020 for Brent and from November 13, 2012 to October 30, 2020 for West Texas Intermediate, each with a total of 2001 observations. We find that the historical simulation approach significantly underestimates the risks for both long and short positions during the recent market turmoil, which confirms the importance of the filtering process in VaR forecasts. Moreover, the proposed approach provides the most accurate VaR forecasts, especially at high confidence levels for the long position. The analysis serves as a useful guide to energy market risk quantification for practitioners and policy makers. © Infopro Digital Limited 2022. All rights reserved.

Regime specific spillovers across US sectors and the role of oil price volatility

There is a growing literature studying return spillovers between similar assets and assets of different classes during crisis periods. However, less is known about return spillovers across stock sectors under high and low volatility regimes and whether they are affected by oil price volatility. Using daily data from May 10th, 2007 to February 28th, 2020, we first study the return spillovers between US stock sectors under low and high volatility regimes by implementing a Markov regime-switching vector autoregression with exogenous variables model, while considering the Fama-French factors as conditioning variables. Return spillovers under low and high volatility regimes show that the energy sector is the largest transmitter and receiver of spillovers to/from other US equity sectors. Rolling window analysis shows that spillovers intensified since the outbreak of the COVID19 pandemic. Second, we apply linear and non-linear Granger causality tests from oil price volatility to the spillover indices. The results show evidence that oil volatility has a causal impact on the spillover dynamics of US stock sectors and that the impact is particularly strong in the high volatility regime. Although the energy sector is one of the smallest sectors of the US stock market, it plays a large role in the network connectedness of stock sectors. The results are of interest to individual and institutional investors who consider US equity investments and to policymakers.