Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 156
Filter
1.
Research in International Business and Finance ; 64:101863, 2023.
Article in English | ScienceDirect | ID: covidwho-2165812

ABSTRACT

This paper aims to develop an artificial neural networkbased forecasting model employing a nonlinear focused time-delayed neural network (FTDNN) for energy commodity market forecasts. To validate the proposed model, crude oil and natural gas prices are used for the period 2007–2020, including the Covid-19 period. Empirical findings show that the FTDNN model outperforms existing baselines and artificial neural networkbased models in forecasting West Texas Intermediate and Brent crude oil prices and National Balancing Point and Henry Hub natural gas prices. As a result, we demonstrate the predictability of energy commodity prices during the volatile crisis period, which is attributed to the flexibility of the model parameters, implying that our study can facilitate a better understanding of the dynamics of commodity prices in the energy market.

2.
Resources Policy ; 80:103263, 2023.
Article in English | ScienceDirect | ID: covidwho-2165803

ABSTRACT

This paper analyses the dynamic comovement and extreme risk spillovers between international crude oil and China's non-ferrous metals futures by combining the application of Copula and CoVaR models, considering the effects of structural breaks and the frequency of cycles. The results show a positive linkage between crude oil and China's non-ferrous metals, with significant long-period comovement. Upside risk in crude oil prices can generate stronger extreme shocks to non-ferrous metals, as evidenced by the outbreak of COVID-19 in 2020. Compared to zinc and aluminum, copper is more susceptible to risk spillovers from crude oil, particularly in the event of a downside trend in crude oil. In addition, WTI crude oil and Brent crude oil show some variability in the upper and lower tail spillover effects. Our research explained to some extent the time-varying, cyclical and asymmetric impact on the non-ferrous metal futures market under crude oil shocks, which can be informative for industry planners, regulators, and investors in making targeted decisions. It is mainly the case when dealing with the many effects of geopolitical conflicts, trade frictions, and commodity supply and demand mismatches.

3.
Ann Oper Res ; : 1-44, 2022 Oct 28.
Article in English | MEDLINE | ID: covidwho-2148820

ABSTRACT

This study aims to explore the role of cryptocurrencies and the US dollar in predicting oil prices pre and during COVID-19 pandemic. The study uses three neural network models (i.e., Support vector machines, Multilayer Perceptron Neural Networks and Generalized regression neural networks (GRNN)) over the period from January 1, 2018, to July 5, 2021. Our results are threefold. First, our results indicate Bitcoin is the most influential in predicting oil prices during the bear and bull oil market before COVID-19 and during the downtrend during COVID-19. Second, COVID-19 variables became the most influential during the uptrend, especially the number of death cases. Third, our results also suggest that the most accurate model to predict the price of oil under the conditions of uncertainty that prevailed in the world during the bear and bull prices in the wake of COVID-19 is GRNN. Though the best prediction model under normal conditions before COVID-19 during an uptrend is SVM and during a downtrend is GRNN. Our results provide crucial evidence for investors, academics and policymakers, especially during global uncertainties.

4.
Energy Economics ; : 106474, 2022.
Article in English | ScienceDirect | ID: covidwho-2158775

ABSTRACT

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.

5.
International Journal of Energy Economics and Policy ; 12(6):137-145, 2022.
Article in English | Scopus | ID: covidwho-2156160

ABSTRACT

This paper estimates the asymmetric relationship between the crude oil market, stock market and COVID-19 pandemic in the case of KSA during the period of March 15, 2020–February 03, 2021. Nonlinear and long-run asymmetric cointegration were utilized for comprehensive research on this topic. Our findings are as follows: positive and negative shocks to the COVID-19 pandemic reduce stock market. Moreover, positive shock to crude oil market increases stock market, but negative shock has a negative and insignificant effect. Based on the results, this study concludes with suitable policy prescription. © 2022, Econjournals. All rights reserved.

6.
Fluctuation & Noise Letters ; : 1, 2022.
Article in English | Academic Search Complete | ID: covidwho-2138149

ABSTRACT

The ongoing COVID-19 shocked financial markets globally, including China’s crude oil future market, which is the third-most traded crude oil futures after WTI and Brent. As China’s first crude oil futures are accessible to foreign investors, the Shanghai crude oil futures (SC) have attracted significant interest since launch at the Shanghai International Energy Exchange. The impact of COVID-19 on the new crude oil futures is an important issue for investors and policy makers. Therefore, this paper studies the short-term influence of COVID-19 pandemic on SC via multifractal analysis. We compare the market efficiency of SC before and during the pandemic with the multifractal detrended fluctuation analysis and other commonly used random walk tests. Then, we generate shuffled and surrogate data to investigate the components of multifractal nature in SC. And we examine cross-correlations between SC returns and other financial assets returns as well as SC trading volume changes by the multifractal detrended cross-correlation analysis. The results show that market efficiency of SC and its cross-correlations with other assets increase significantly after the outbreak of COVID-19. Besides that, the sources of its multifractal nature have changed since the pandemic. The findings provide evidence for the short-term impacts of COVID-19 on SC. The results may have important implications for assets allocation, investment strategies and risk monitoring. [ FROM AUTHOR]

7.
2nd Asian Conference on Innovation in Technology, ASIANCON 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2136098

ABSTRACT

The variations in the price of crude oil are very erratic, nonlinear, and dynamic with a high degree of uncertainty making it much more difficult to predict accurately. As a result, the opacity and intricacy in determining the crude oil price have been a significant topic of interest for researchers. This paper develops an efficient Genetic Algorithm(GA) based fine-tuned Support Vector Regression(SVR) model for predicting crude oil prices. The strategy utilizes key economic factors that ascertain the price per barrel, which serves as the input. The NASDAQ dataset used in this work encompasses ten years of daily data. The GA technique fine-tunes the parameters of the SVR model to boost the model's ability to foresee crude oil price fluctuations. The proposed model's performance is evaluated by employing various major criteria that compare our model to its counterparts, such as SVR and Long Short-Term Memory (LSTM) approaches. In light of these criteria, the findings of root mean square error (RMSE) and mean absolute percentage error (MAPE) indicate that this model surpasses others in predicting crude oil prices more accurately. Finally, this study also analyzes the impact of persistent uncertainness concerning the COVID-19 outbreak on crude oil price trends. © 2022 IEEE.

8.
Energy Economics ; : 106444, 2022.
Article in English | ScienceDirect | ID: covidwho-2130743

ABSTRACT

This paper investigates the relationship between oil and airline stock returns under different time frequencies. First, we propose an Autoregressive moving average model with mixed frequency exogenous variable to analyse the different impacts of oil on airline stock returns on daily, weekly, and monthly basis. We consistently find a negative oil-airline stock return nexus on a daily basis, but a positive relationship on a weekly basis. While the former supports the economic-based channel, the latter is in line with the market inertia channel. Our findings help explain mixed results reported in the literature. Further, our time frequency connectedness analysis shows that the economic-based channel dominates the market inertia channel since the connectedness is more pronounced in the short-run compared to the medium- and long-run. Our block connectedness results highlight that business models of airline firms can play a significant role in affecting the connectedness, in which the low-cost airlines are more sensitive to the oil price changes. It is worth noting that there are distinguished drivers of the oil-airline stock return nexus in different time frequencies. The drivers also vary between the Global Financial Crisis and the COVID-19 pandemic. Our results are consistent under a battery of robustness checks and deliver important implications to investors, portfolio managers, and executives of airline firms.

9.
Renewable Energy ; 2022.
Article in English | ScienceDirect | ID: covidwho-2122767

ABSTRACT

Understanding the interactions among climate change, carbon emission allowance trading, crude oil and renewable energy stock markets, especially the role of climate change in this system is of great significance for policy makers, energy producers/consumers and relevant investors. The present paper aims to quantify the time-varying connectedness effects among the four factors by using the TVP-VAR based extensions of both time- and frequency-domain connectedness index measurements proposed by Antonakakis et al. (2020) and Ellington and Barunik (2021) [8,48]. The empirical results suggest that, firstly, the average total connectedness among climate change, carbon emission allowance trading, crude oil and renewable energy stock markets is not so strong for the heterogenous fundamentals underlying them. Nevertheless, the time-varying total connectedness fluctuates fiercely through May 2005 to September 2021, varying from about 8% to 30% and rocket to very high levels during the global subprime mortgage crisis and the COVID-19 pandemic. Furthermore, the total connectedness mainly centers on the short-term frequency, i.e., 1–3 months. Secondly, climate change is generally the leading information contributor among the four factors, although not particularly strong, and its leading role also performs mainly on the short-term frequency (1–3 months). Thirdly, renewable energy stock market and crude oil market show tight interactions between them and they are the two major bridges of information exchanges across various time frequencies (horizons) in this system. Finally, we confirm the evidence that the primary net connectedness contributor and receiver switch frequently across different time frequencies, implying that it is extremely essential for policy makers, energy producers/consumers and investors to make time-horizon-specific regulatory, production/purchasing or investment decisions when facing the uncertain effects of climate change on the interactions among carbon emission allowance, crude oil and renewable energy stock markets.

10.
Resour Policy ; : 103111, 2022 Nov 15.
Article in English | MEDLINE | ID: covidwho-2120395

ABSTRACT

Bitcoin is a new speculative investment with extremely volatile movement, thus possibly failing to act as a safe haven for crude oil when the price of this energy commodity plummeted following the global outbreak of COVID-19. Meanwhile, Tether is designed to behave similarly to the US dollar with stable fluctuation. In this study, we assessed their safe-haven properties in terms of risk reduction opportunities by proposing an improved version of Value-at-Risk (VaR) and Expected Shortfall (ES). Using vine copula-based AR-GJR-GARCH models, we demonstrated that Bitcoin exhibited inconsistent risk reduction capability for oil, particularly before COVID-19. When adding Tether into a portfolio containing oil and Bitcoin, the risk reduction was achieved for any portfolio allocation and was more pronounced amid the COVID-19 period. This suggests that Tether consistently served strong support for Bitcoin to protect oil investors against extreme risk and received a significant impact from the COVID-19 outbreak. However, the consistent safe-haven functionality of Tether was not as good as that of the US dollar in most cases, and this implied the vanishing of its stability. These results were robust when considering another asymmetric volatility model and another dependence model. Furthermore, the proposed improved VaR and ES forecasts outperformed their corresponding unimproved version in quantifying portfolio risk and therefore provided a more accurate assessment of safe-haven roles.

11.
Journal of Asset Management ; 2022.
Article in English | Web of Science | ID: covidwho-2106632

ABSTRACT

Documenting the interlinkages among assets that are widely used to hedge against inflation is crucial for investors, as the necessity to protect the investment portfolio is stronger under inflationary conditions. For this purpose, we investigate the volatility spillovers between treasury inflation-protected securities (TIPS) and a battery of other assets perceived as inflation hedges, including bonds, gold, real estate, oil and equities. The applied methodology comprehends the time-varying parameter vector autoregressive (TVP-VAR) extension of the Diebold and Yilmaz (Int J Forecast 28:57-66, 2012, 10.1016/j. ijforecast.2011.02.006) approach for the period 1/1/2010-3/31/2022. Our results indicate that the assets under consideration are moderately interconnected and subjected to several exogenous shocks, such as the US-China trade war, the COVID-19 pandemic and the Russia-Ukraine war. Furthermore, we assess the hedging effectiveness of TIPS against each asset by estimating hedge ratios and optimal portfolios weights, before and after the spread of COVID-19 pandemic, by using conditional variance estimations (DCC-GARCH). The empirical findings show that the short position in the volatility of TIPS is proved to be an excellent hedge for all the sampled assets, with the exception of short-term Treasury bonds, and their hedging ability was improved during COVID-19.

12.
Sustainability Accounting, Management and Policy Journal ; 2022.
Article in English | Web of Science | ID: covidwho-2097582

ABSTRACT

Purpose The purpose of this study is to examine the dynamic connectedness and volatility spillovers between commodities and corporations exhibiting the best environmental, social and governance (ESG) practices. In addition, the authors determine the optimal hedge ratios and portfolio weights for ESG and commodity investors and portfolio managers. Design/methodology/approach This study uses the novel frequency connectedness framework to point out volatility spillover between ESG indices covering the USA, developed and emerging markets and commodity indices, including energy (crude oil, natural gas and heating oil), industrial metals (aluminum, copper, zinc, nickel and lead) and precious metals (gold and silver) by using daily data between January 3, 2011 and May 26, 2021, covering significant socio-economic developments and the COVID-19 outbreak. Findings The results of this study suggest a total connectedness index at a mediocre level, mainly driven by the shocks creating uncertainty in the short term. And the results indicate that all ESG indices are net volatility transmitters, and all commodity indices other than crude oil and copper are net volatility receivers. Practical implications The results imply statistically significant hedging and portfolio diversification opportunities to investors and portfolio managers across the asset classes, proven by the hedging effectiveness analyses. Social implications This study provides implications for policymakers focusing on the risk of contagion among the commodity and ESG markets during turbulent periods to ensure international financial stability. Originality/value This study contributes to the existing literature by differentiating ESG portfolios as the USA, developed and developing markets and examining dynamic connectedness and volatility spillovers between ESG portfolios and commodities with a different technique. This study also contributes by considering COVID-19 outbreak.

13.
Resources Policy ; : 103085, 2022.
Article in English | ScienceDirect | ID: covidwho-2095957

ABSTRACT

This paper is an innovative attempt to empirically investigate the determinants of crude oil prices. The main objective is to distinguish between short- and long-term effects of some covariates on oil prices. The autoregressive distributed lag (ARDL) approach is applied to daily series spanning the period from January 2, 2003, to May 24, 2021, to analyze long-run relationships and short-run dynamics. The paper also focuses on the asymmetric effects of covariates and a nonlinear ARDL (NARDL) approach is used to explore this asymmetry. The use of an asymmetric error correction model with asymmetric cointegration provides new insights for examining the determinants of oil prices. All investigations of underlying oil price fluctuations are examined both before and in the COVID-19 pandemic. Our results, based on different econometric specifications, have key policy implications for policymakers both with and without COVID-19 potential considerations.

14.
Applied Energy ; 328:120194, 2022.
Article in English | ScienceDirect | ID: covidwho-2085919

ABSTRACT

Stable and accurate prediction of crude oil prices is critical to national security, economic development, and even international relations, against the background of the COVID-19 and Russia–Ukraine war. Therefore, a memory-based hybrid forecasting system is established for point prediction and interval prediction of crude oil prices in this research, which more thoroughly separates the noise in the raw data and achieves superior prediction performance. There are five main steps in the proposed model: decomposing the raw data into intrinsic mode functions (IMFs) through our new data preprocessing technique complete ensemble extreme-point symmetric mode decomposition with adaptive noise (CEESMDAN), ensemble of IMFs based on memory features, prediction of reconstructed components, error correction via grey wolf optimizer (GWO) for final point prediction, obtaining interval prediction by multi-objective grey wolf optimizer (MOGWO). The empirical results prove that the proposed model has excellent accuracy, robustness, and generalization in both point prediction and interval prediction, compared with various baseline models.

15.
Frontiers in Physics ; 10, 2022.
Article in English | Web of Science | ID: covidwho-2071115

ABSTRACT

Financial markets are widely believed to be complex systems where interdependencies exist among individual entities in the system enabling the risk spillover effect. The detrended cross-correlation analysis (DCCA) has found wide applications in examining the comovement of fluctuations among financial time series. However, to what extent can such cross-correlation represent the spillover effect is still unknown. This article constructs the DCCA network of commodity future markets and explores its proximity to the volatility spillover network. Results show a moderate agreement between the two networks. Centrality measures applied to the DCCA networks are able to identify key commodity futures that are transmitting or receiving risk spillovers. The evolution of the DCCA network reveals a significant change in the network structure during the COVID-19 pandemic in comparison to that of the pre- and post-pandemic periods. The pandemic made the commodity future markets more interconnected leading to a shorter diameter for the network. The intensified connections happen mostly between commodities from different categories. Accordingly, cross-category risk spillovers are more likely to happen during the pandemic. The analysis enriches the applications of the DCCA approach and provides useful insights into understanding the risk dynamics in commodity future markets.

16.
Asia-Pacific Journal of Business Administration ; 2022.
Article in English | Web of Science | ID: covidwho-2070193

ABSTRACT

Purpose This study examines the information transmission (return and volatility spillovers) among energy commodities (crude oil, natural gas, Brent oil, heating oil, gasoil, gasoline) and Asian stock markets which are net importers of energy (China, India, Indonesia, Malaysia, Korea, Pakistan, Philippines, Taiwan, Thailand). Design/methodology/approach The information transmission is investigated by employing the spillover index of Diebold and Yilmaz, using daily data for the period January 2000 to May 2021. Findings A Strong connectedness is documented between the two classes of asset, especially during crisis periods. Our findings reveal that most of the energy markets, except gasoil and natural gas, are net transmitters of information, whereas all the stock markets, excluding Indonesia and Korea, are net recipients. Practical implications The findings are helpful for portfolio managers and institutional investors allocating funds to various asset classes in times of crisis. Originality/value All data is original.

17.
Cogent Economics & Finance ; 10(1), 2022.
Article in English | Web of Science | ID: covidwho-2070060

ABSTRACT

This study analyzes the trilateral relationship between macroeconomic variables of oil prices, stock market index, and exchange rate to demonstrate their behavior and inter-relationship in the economic setup of Pakistan. The investigated period includes daily time series data ranging from 4 January 2016 to 30 April 2021. The study consists of three sub-periods: the pre-COVID-19 period ranging from 4 January 2016 to 31 December 2019, COVID-19 period ranging from 1 January 2020 to 30 April 2021, and overall period ranging from 4 January 2016 to 30 April 2021 by using a Vector Autoregressive (VAR) model. The results illustrate that oil prices changes, and stock index have an insignificant direct relationship both in pre-COVID-19 and overall sub-periods of study while a positive and statistically significant relationship during the COVID-19 period. This research also suggests that stock index has a direct and statistically significant but negative impact on the exchange rate in all sub-periods of study. This research also gives practical implications for forex investors and traders to analyze the inflating and deflating stock market patterns for future investment opportunities. However, most of the previous studies emphasized on the direct influence of exchange rate on the stock market and no effort is made on vice versa association. Furthermore, this research presents a practical relevance for the stock market investors that health uncertainty regime affected the insignificant association between oil price and stock market indices and this relation turns out to be significant during the crisis regime.

18.
Applied Economics ; : 1-24, 2022.
Article in English | Web of Science | ID: covidwho-2069944

ABSTRACT

This study investigates the spillovers and information transmission between carbon, crude oil, and stock markets under various market conditions in Phase III of the EU ETS. For this purpose, we use a novel causality-in-quantiles test method and quantile impulse response functions based on daily data of carbon futures, Brent spot, and three representative equity indices in the Europe over the period from 27 January 2014 to 18 September 2020. We find that crude oil market has a unidirectional spillover effect on carbon market, and this causality is significant under normal to bullish market conditions. Furthermore, the causality-in-quantiles between crude oil and stock markets varies with specific equality index, and the information transmission from crude oil to stock market is strong in the normal stock market but invalid when stock markets become extremely bearish or bullish. The COVID-19 epidemic may cause structural changes in the oil-carbon and oil-stock nexus.

19.
Journal of Futures Markets ; 2022.
Article in English | Web of Science | ID: covidwho-2068566

ABSTRACT

We examine the price discovery performance of China's crude oil futures traded on the Shanghai International Energy Exchange (INE) for the spot prices of 19 types of deliverable and nondeliverable Asian crude oil. We find evidence for the INE crude oil futures price discovery function even at the early stage for almost all the deliverable crudes and some nondeliverable crudes. Both the INE crude oil futures price and the spot price significantly contribute to the price discovery process, with substantially time-varying informational roles. While the price discovery performance was severely damaged around the period of COVID-19 pandemic shock intensification in China with the temporary cancellation of nighttime trading, it improved to some extent after China started the recovery from the shock. But such improvement deteriorated drastically and disappeared since early 2021. Further analysis reveals that both economic fundamentals (e.g., the warehouse inventory) and trading-related characteristics of the futures market are significant determinants of the price discovery performance. The overall findings imply that the INE crude oil futures market has evolved into a useful and important information source in pricing Asian crudes, and is on the path to emerge as an Asian benchmark.

20.
International Journal of Research in Business and Social Science ; 11(6):288-299, 2022.
Article in English | ProQuest Central | ID: covidwho-2067467

ABSTRACT

[...]we canvass those Nigerian banks should reduce dividend payouts and increase retained profits as a buffer against exposed risks. To ensure the healthiness of banks in the banking industry as well as facilitate international transaction, the central bank of ten countries (Belgium, Canada, France, Germany, Italy, Japan, the Netherlands, Sweden, the UK and the US) formed the committee of banking supervision in 1988 (the Basel Committee on Banking Supervision). Since the formation of this committee, it has undergone at least three stages called the Basel I, Basel II and Basel III. Premised on shock to the economy brought on by the coronavirus pandemic, with economic growth in 2020 expected to contract by as much as 4.4 percent to 8.94 percent, a drop in oil receipt and a devalued Naira in the range of 380-450 to US dollar, the capital adequacy of banks could be severely threatened, (Egba, 2020). [...]scholars have extensively shown that bank specific performance indicators and macroeconomic factors affected capital adequacy ratio. [...]this paper examined the effect of banks specific-performance indicators and macroeconomic factors on bank financing which is the minimum funds required for their short-term obligation or capital adequacy ratio.

SELECTION OF CITATIONS
SEARCH DETAIL