BIST Tourism and BIST Electricity Index Relationship

In this study, which is based on daily data, the relationship between BIST electricity index and BIST tourism index was measured between 2012:M9 – 2022:M9 periods. The aim of the study is to measure the relationship between BIST electricity index and BIST tourism index. VAR Granger causality test was applied to determine whether there is any causal relationship between the variables. It has been determined as a result of the analysis that the BIST electricity index has no effect on the BIST tourism index. Two-way ineffectiveness was determined among the variables. In addition, it was obtained as a result of the analysis that the applied correlation relationship was weak between these variables. The results obtained from the study are important in terms of measuring the effects among BIST indices.

Hedging potentials of green investments against climate and oil market risks

PurposeThis study aims to examine the ability of clean energy stocks to provide cover for investors against market risks related to climate change and disturbances in the oil market.Design/methodology/approachThe study adopts the feasible quasi generalized least squares technique to estimate a predictive model based on Westerlund and Narayan's (2015) approach to evaluating the hedging effectiveness of clean energy stocks. The out-of-sample forecast evaluations of the oil risk-based and climate risk-based clean energy predictive models are explored using Clark and West's model (2007) and a modified Diebold & Mariano forecast evaluation test for nested and non-nested models, respectively.FindingsThe study finds ample evidence that clean energy stocks may hedge against oil market risks. This result is robust to alternative measures of oil risk and holds when applied to data from the COVID-19 pandemic. In contrast, the hedging effectiveness of clean energy against climate risks is limited to 4 of the 6 clean energy indices and restricted to climate risk measured with climate policy uncertainty.Originality/valueThe study contributes to the literature by providing extensive analysis of hedging effectiveness of several clean energy indices (global, the United States (US), Europe and Asia) and sectoral clean energy indices (solar and wind) against oil market and climate risks using various measures of oil risk (WTI (West Texas intermediate) and Brent volatility) and climate risk (climate policy uncertainty and energy and environmental regulation) as predictors. It also conducts forecast evaluations of the clean energy predictive models for nested and non-nested models.

Oil in the age of coronavirus: Hysteria or appropriate fall?

We look at the oil price fall in the beginning of 2020 and the effects of coronavirus and the attention towards it on these prices. Such a fall was observed at multiple markets simultaneously with the spread of coronavirus and the panic around it, and oil market wasn't an exception. Using OLS time series models, we investigate - what was the main reason behind such a fall - the coronavirus pandemic itself or rather the attention towards it. We prove the absence of straight effects of the COVID-19 itself on oil prices. At the same time we find significant negative impact of the attention towards COVID-19 on the Internet search on the oil prices. We investigate the role of the OPEC in mitigating the negative impact of coronavirus and the attention towards it. We found that after the OPEC summit both the number of Covid cases and the attention towards the disease lost its influence on oil prices. Our paper is relevant for the behavioral finance researchers, as well as for those who look at the influence of informational shocks on different markets and particularly, on the oil market and at the effect of the COVID-19 on the economy.

The impact of COVID-19 on the portfolio optimization

PurposeThis article unveils first the lead–lag structure between the confirmed cases of COVID-19 and financial markets, including the stock (DJI), cryptocurrency (Bitcoin) and commodities (crude oil, gold, copper and brent oil) compared to the financial stress index. Second, this paper assesses the role of Bitcoin as a hedge or diversifier by determining the efficient frontier with and without including Bitcoin before and during the COVID-19 pandemic.Design/methodology/approachThe authors examine the lead–lag relationship between COVID-19 and financial market returns compared to the financial stress index and between all markets returns using the thermal optimal path model. Moreover, the authors estimate the efficient frontier of the portfolio with and without Bitcoin using the Bayesian approach.FindingsEmploying thermal optimal path model, the authors find that COVID-19 confirmed cases are leading returns prices of DJI, Bitcoin and crude oil, gold, copper and brent oil. Moreover, the authors find a strong lead–lag relationship between all financial market returns. By relying on the Bayesian approach, findings show when Bitcoin was included in the portfolio optimization before or during COVID-19 period;the Bayesian efficient frontier shifts to the left giving the investor a better risk return trade-off. Consequently, Bitcoin serves as a safe haven asset for the two sub-periods: pre-COVID-19 period and COVID-19 period.Practical implicationsBased on the above research conclusions, investors can use the number of COVID-19 confirmed cases to predict financial market dynamics. Similarly, the work is helpful for decision-makers who search for portfolio diversification opportunities, especially during health crisis. In addition, the results support the fact that Bitcoin is a safe haven asset that should be combined with commodities and stocks for better performance in portfolio optimization and hedging before and during COVID-19 periods.Originality/valueThis research thus adds value to the existing literature along four directions. First, the novelty of this study lies in the analysis of several financial markets (stock, cryptocurrencies and commodities)' response to different pandemics and epidemics events, financial crises and natural disasters (Correia et al., 2020;Ma et al., 2020). Second, to the best of the authors' knowledge, this is the first study that examine the lead–lag relationship between COVID-19 and financial markets compared to financial stress index by employing the Thermal Optimal Path method. Third, it is a first endeavor to analyze the lead–lag interplay between the financial markets within a thermal optimal path method that can provide useful insights for the spillover effect studies in all countries and regions around the world. To check the robustness of our findings, the authors have employed financial stress index compared to COVID-19 confirmed cases. Fourth, this study tests whether Bitcoin is a hedge or diversifier given this current pandemic situation using the Bayesian approach.

Booming Mature Field Production Throughout the Covid Pandemic Crisis Impact on the Oil and Gas Industry Using a Strategic-Technical Innovative Approach - Success Story from Dragon Oil - Egypt

The oil industry is experiencing a critical situation as the Covid-19 pandemic outbreak. There are several challenges that facing the industry specially the investors as the global decline in demand for Energy merchandises, the future exploration and development drilling in new assets that require massive investments is still uncertain based on the current market price and conditions. The much-reported fall in oil prices and the acute pressure on IOCs to survive in this environment led the companies to stop many ongoing projects and shrink work profile that affected the oil production all over the world. The situation in Egypt is quite challenging for the investors as Egypt is a big consumer, along with the political stability that kept the economy running directed the big IOCs to embrace innovative approaches to lower the operating costs that has the direct impact on the cost per barrel to support maintaining the country growth and secure current energy demand. Dragon Oil company as newly introduced to Egypt's market after acquiring the market shares of one of the major joint ventures in Egypt (Gulf of Suez Petroleum Company- GUPCO) in October 2019 has faced the same dilemma of exerted pressure on the expenditures (Capex and Opex) in order to cope with the global market circumstances. However that didn't deter the company to embrace an innovative way of thinking and handling for the situation. Dragon Oil/GUPCO multi-disciplinary teams achieved successfully a production incremental increase of 10,000 barrels per day through the past six month by adapting a strategic management innovative plans, alternative lower cost technical solutions, production optimization and introducing new proved technologies to the 50 years old assets. This paper will highlight the complete workflow adopted by GUPCO/Dragon Oil teams covering the whole process aspects;appraise, select, define and execution phases to achieve the company goals. The work done was including restoring production from Shut-in offshore platforms or wells via fixing the surface network using neoteric solutions, widely applying rigless interventions using several new techniques in the current producers to maximize their production and optimizing the production cycle across the four production chokes In Summary, Dragon Oil/GUPCO teams managed to increase GUPCO's production despite of the restricted budget and the negative impact of COVID-19 pandemic on the oil price and reach an outstanding performance in operation excellence and safety aspects that results in arresting the natural decline and increase the growth production by about 15% from the 2019 Average production. Copyright © 2023, Society of Petroleum Engineers.

The Pandemic Shut-In: Reactivating Wells Shut-In in Spring/Summer 2020 – The Impact on 10-Year Forecast and EUR Outlook: A Duvernay Oil Case Study

The COVID-19 pandemic forced Canadian oil and gas operators to cut crude oil production by almost 1 MMb/d in the first half of 2020 due to low oil prices driven by reduced demand. This study explores the forecast and EUR performance of unconventional horizontal oil wells producing from the Duvernay Formation in central Alberta that were shut-in versus those that continued to produce uninterrupted throughout the reduced production period. How were forecasted production and EURs impacted? Did the manner in which the wells were completed play a role? This paper investigates these questions and more in a regional case study of 95 unconventional Duvernay oil wells using public data and a fully automated, physio-statistical, predictive analytical production forecasting tool. The bases of the performance comparison were the results of a 10-year forecast and EUR outlook for the wells evaluated in January, 2020 before the production slow down, and then re-evaluated in January, 2021, 12 months later, after the wells that were shut-in were back on production. In general, wells that continued producing uninterrupted throughout the study period exhibited significantly improved forecast and EUR performance over wells that were shut-in. Analyzing the performance of the largest field (Cygnet with 32 wells), with respect to lateral length, the results pointed to shorter wells that were shut-in exhibiting the poorest performance, where the wells' EUR performance degraded by 7% on average. The proppant intensity study for the same wells told a similar story, with shut-in wells with smaller fracs exhibiting negligible EUR improvement (0.4%) compared to the other categories of wells, with respect to frac size and shut-in status. A proximity study investigated two pads, one with only shut-in wells and the other with only non-shut-in wells, with the results pointing to competitive drainage between individual wells despite the overall performance of a given pad being neutral. Copyright 2022, Unconventional Resources Technology Conference (URTeC)

Oil price shocks, economic policy uncertainty, and green finance: a case of China

This study investigates the long- and short-run effects of crude oil price (COP) and economic policy uncertainty (EPU) on China's green bond index (GBI) using the quantile autoregressive distributed lag model. The empirical results show that COP and EPU produce a significant positive and negative influence on GBI in the long-run across most quantiles, respectively, but their short-run counterparts are opposite direction and only significant in higher quantiles. Thus, major contributions are made accordingly and shown in the following aspects. The findings emphasise the importance of understanding how COP and EPU affect China's green bond market for the first time. In addition, both the long- and short-run effects are captured, but long-run shocks primarily drive the green bond market. Finally, time- and quantile-varying analyses are adopted to explain the nexus between COP and EPU to GBI, which considers not only different states of the bond market but also events that occur in different time periods. Some detailed policies, such as a unified and effective green bond market, an early warning mechanism of oil price fluctuation, and prudent economic policy adjustments, are beneficial for stabilising the green finance market.

The Differential Effects of Oil Prices on the Development of Renewable Energy in Oil-Importing and Oil-Exporting Countries in Africa

The shift to renewable sources of energy has become a critical economic priority in African countries due to energy challenges. However, investors in the development of renewable energy face problems with decision making due to the existence of multiple criteria, such as oil prices and the associated macroeconomic performance. This study aims to analyze the differential effects of international oil prices and other macroeconomic factors on the development of renewable energy in both oil-importing and oil-exporting countries in Africa. The study uses a panel vector error correction model (P-VECM) to analyze data from five net oil exporters (Algeria, Angola, Egypt, Libya and Nigeria) and five net oil importers (Kenya, Ethiopia, Congo, Mozambique and South Africa). The study finds that higher oil prices positively affect the development of renewable energy in oil-importing countries by making renewable energy more economically competitive. Economic growth is also identified as a major driver of the development of renewable energy. While high-interest rates negatively affect the development of renewable energy in oil-importing countries, it has positive effects in oil-exporting countries. Exchange rates play a crucial role in the development of renewable energy in both types of countries with a negative effect in oil-exporting countries and a positive effect in oil-importing countries. The findings of this study suggest that policymakers should take a holistic approach to the development of renewable energy that considers the complex interplay of factors, such as oil prices, economic growth, interest rates, and exchange rates.

What do we know about the idiosyncratic risk of clean energy equities?

The COVID-19 pandemic stimulated the need to invest in clean energy firms for better returns and climate risk mitigation. This study provides a detailed overview of the impact of idiosyncratic risk (IVOL) on excess returns of 95 clean energy stocks. Overall, investors in clean energy stocks are guided by the pessimist group of investors who underprice the high IVOL stocks and demand high-risk premiums to diversify the firm-specific risk. Further, during the COVID-19 period, there is no significant relationship between clean energy excess stock returns and IVOL. During this period, clean energy stocks were exposed to higher information asymmetry, limiting the arbitrage opportunities and producing a weaker return-IVOL relation indicating that clean energy stocks reflect the properties of technology stocks. IVOL has a low level of persistence which may be helpful in forecasting. This study offers valuable insights for regulators and investors from the investment decisions, asset pricing, and diversification perspective.

A Wavelet Analysis of the Dynamic Connectedness among Oil Prices, Green Bonds, and CO2 Emissions

Wavelet power spectrum (WPS) and wavelet coherence analyses (WCA) are used to examine the co-movements among oil prices, green bonds, and CO2 emissions on daily data from January 2014 to October 2022. The WPS results show that oil returns exhibit significant volatility at low and medium frequencies, particularly in 2014, 2019-2020, and 2022. Also, the Green Bond Index presents significant volatility at the end of 2019-2020 and the beginning of 2022 at low, medium, and high frequencies. Additionally, CO2 futures' returns present high volatility at low and medium frequencies, expressly in 2015-2016, 2018, the end of 2019-2020, and 2022. WCA's empirical findings reveal (i) that oil returns have a negative impact on the Green Bond Index in the medium term. (ii) There is a strong interdependence between oil prices and CO2 futures' returns, in short, medium, and long terms, as inferred from the time-frequency analysis. (iii) There also is evidence of strong short, medium, and long terms co-movements between the Green Bond Index and CO2 futures' returns, with the Green Bond Index leading.

Wavelet analysis of S&P 500, oil prices, and economic policy uncertainty

We collect a total of 1830 data from January 2020 to June 2022 and use R for data processing and wavelet analysis. Moreover, we analyze the interactions between the COVID-19 pandemic, the Russian-Ukrainian war, crude oil price, the S&P 500 and economic policy uncertainty within a time-frequency frame work. As a result that the COVID-19 pandemic and the Russian-Ukrainian war has the extraordinary effects on the three indexes and the effect of the Russian- Ukrainian war on the crude oil price and US stock price higher than on the US economic uncertainty. © COPYRIGHT SPIE.

Novel COVID-19 Outbreak and Global Uncertainty in the Top-10 Affected Countries: Evidence from Wavelet Coherence Approach

This study explores the association of novel COVID-19 with the dominant financial assets, global uncertainty, commodity prices, and stock markets of the top ten corona-affected countries. We employ a wavelet coherence technique to unearth this linkage using daily data of COVID-19 deaths and reported cases from 1 January 2020 until 26 February 2021. The study finds a weak coherence between COVID-19 and global uncertainty variables in the short and medium term, while a strong positive correlation has been witnessed in the long run. The COVID-19 cases impact the stock markets in the short and medium term, while no significant impact is reported in the long run. On the other hand, a substantial impact of the COVID-19 outbreak has also been found on the exchange rate. In addition, the real asset market, such as gold, remains more stable during the COVID-19 outbreak. Thus, the study recommends that investors and portfolio managers should add such assets to their investment options to safeguard the excessive risk and downside momentum of the equity market. The study also has implications for regulators who are concerned with the neutrality of the COVID-19 effect and market stability. © 2023 by the authors.

Knowledge Discovery to Support WTI Crude Oil Price Risk Management

The high volatility of commodity prices and various problems that the energy sector has to deal with in the era of COVID-19 have significantly increased the risk of oil price changes. These changes are of the main concern of companies for which oil is the main input in the production process, and therefore oil price determines the production costs. The main goal of this paper is to discover decision rules for a buyer of American WTI (West Texas Intermediate) crude oil call options. The presented research uses factors characterizing the option price, such as implied volatility and option sensitivity factors (delta, gamma, vega, and theta, known as "Greeks”). The performed analysis covers the years 2008–2022 and options with an exercise period up to three months. The decision rules are discovered using association analysis and are evaluated in terms of the three investment efficiency indicators: total payoff, average payoff, and return on investment. The results show the existence of certain ranges of the analyzed parameters for which the mentioned efficiency indicators reached particularly high values. The relationships discovered and recorded in the form of decision rules can be effectively used or adapted by practitioners to support their decisions in oil price risk management.

Measuring the frequency and quantile connectedness between policy categories and global oil price

This study explores the connectedness between various categories of economic policy uncertainty (EPU) and global crude oil prices in different frequencies and quantiles using the generalized forecast error variance decomposition and data in the US, China, and Japan from January 2000 to May 2022. The empirical results may be summarized as follows. First, total short and long term connectedness exhibits different patterns and is more sensitive to extreme positive and negative shocks than regular shocks. Second, fiscal policy uncertainty (FPU) and monetary policy uncertainty (MPU) tend to act as net transmitters of shocks, while the roles of trade policy uncertainty (TPU) are mixed in the short term, irrespective of the country. However, under extreme market conditions, no specific-category EPU features a clear net transmitter/recipient. Finally, the results are qualitatively and quantitatively unaffected by the chosen proxy of crude oil prices and are not altered by global real economic activity. © 2023 Elsevier Ltd

Risk Measure between Exchange Rate and Oil Price during Crises: Evidence from Oil-Importing and Oil-Exporting Countries

This study investigates the risk spillover effect between the exchange rate of importing and exporting oil countries and the oil price. The analysis is supported by the utilization of a set of double-long memories. Thereafter, a multivariate GARCH type model is adopted to analyze the dynamic conditional correlations. Moreover, the Gumbel copula is employed to define the nonlinear structure of dependence and to evaluate the optimal portfolio. The conditional Value-at-Risk (CoVaR) is adopted as a risk measure. Findings indicate a long-run dependence and asymmetry of bidirectional risk spillover among oil price and exchange rate and confirm that the risk spillover intensity is different between the former and the latter. They show that the oil price has a stronger spillover effect in the case of oil exporting countries and the lowest spillover effect in the case of oil importing countries.

Analysis of the relationship between bitcoin, oil prices, and the Dow Jones Industrial Average using a wavelet-based approach

This paper analyzes the correlation between bitcoin, oil price fluctuations and the DOW Jones Industrial Index in the time-frequency framework. Coherent wavelet method applied to recent daily data in the United States (1863 in total). Our research has several implications and supports for policy makers and asset managers. We find that oil prices lead the U.S. market at both low and high frequencies throughout the observation period. This result suggests that sanctions against Russia by a number of countries, including the U.S., are influencing oil prices, while oil remains a major source of systemic risk to the U.S. economy and economic uncertainty between the international level is exacerbated by tensions between Russia and Ukraine. © COPYRIGHT SPIE.

Gold, crude oil, bitcoin and Indian stock market: recent confirmation from nonlinear ARDL analysis

PurposeThis paper investigates the causality among gold prices, crude oil prices, bitcoin and stock prices by using daily data from January 2014 to December 2021. The study also examines the data during the COVID-19 outbreak from January 2020 to December 2021.Design/methodology/approachTo estimate the long- and short-run causality, this study considers the nonlinear autoregressive distributed lag (NARDL) cointegration test.FindingsThe analysis found the existence of an asymmetric long-run cointegration among selected assets. Findings indicate that positive changes in bitcoin do not affect stock market in the long term. Changes in crude oil prices have a significant impact on stock prices. Moreover, it is observed that variations in the stock prices trigger a negative impact on gold prices. During the COVID-19 period, the study notices the presence of an asymmetric long-term cointegration between selected assets except bitcoin. Besides, findings revealed that negative price adjustments in gold lead to significant positive shocks in stock market.Originality/valueThese results provide critical information for policy performers and researchers to develop new strategies. Policy regulators can also consider the potential effects of the COVID-19 outbreak while developing strategies for investment decisions.

Are Bitcoin and Gold a Safe Haven during COVID-19 and the 2022 Russia–Ukraine War?

Our investigation strives to unearth the best portfolio hedging strategy for the G7 stock indices through Bitcoin and gold using daily data relevant to the period 2 January 2016 to 5 January 2023. This study uses the DVECH-GARCH model to model dynamic correlation and then compute optimal hedge ratios and hedging effectiveness. The empirical findings show that Bitcoin and gold were rather effective hedge assets before COVID-19 and diversifiers during the pandemic and Russia–Ukraine war. From hedging effectiveness perspectives, gold and Bitcoin are safe-haven assets, and the investment risk of G7 stock indices could be hedged by taking a short position during thepandemic period and war except for the pair Nikkei/Gold. Additionally, gold beats Bitcoin in terms of hedging efficiency. We thus demonstrate the central role of Bitcoin and gold as financial market participants, particularly during market turmoil and downward movements. Our findings can be of interest to investors, regulators, and governments to take into consideration the role of Bitcoin in financial markets.

The Impact of Changes in Oil Prices on the Global and Saudi Arabia Economy

This study aimed to identify the impact of COVID-19 on oil sector which faced the significant challenges, globally and in Saudi Arabia. The study sheds light on the unprecedented crisis that occurred in the decline in demand for oil and the collapse of its prices, and its effects and consequences on world economies and Arab economies, and the extent to which the Saudi economy has been affected by them in light of what markets have witnessed global oil, and opportunities to benefit from this crisis and the study relied on the descriptive and analytical methodology. The study paper will use the NARDL to explain the asymmetric effect of changes in oil price on Government Budget for Saudi Arabia. The study reached that this situation provides a further opportunity for Saudi Arabia to diversify the economy away from a reliance on oil, Government policies seek to facing these challenges by diversifying the economic base and developing and diversifying revenues non-oil products to reduce financial volatility, in addition to enhancing the role of the private sector focusing on supporting non-oil GDP growth rates to enhance the resilience of the economy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Forecasting Crude Oil Prices By Using ARIMA Model: Evidence From Tanzania

Purpose: The fluctuation in the price of crude oil on the global market has created a lot of attention to the researchers to investigate its price movement. This study tries to address the problem of predicting crude oil prices in a situation of unusual circumstances. Methodology: In this study, Box Jenkins methodology was used to analyze monthly dynamics of the Brent oil price from January 2002 to February 2022. Data were first differenced to achieve stationarity, and then ACF and residual diagnostics were utilized to choose models that were used for analysis Findings: The performance of various models were evaluated and ARIMA (0, 1, 1) was found to be the best model for forecasting crude oil prices. This study further reveals that despite the corona virus and the Ukraine war having a considerable impact on crude oil prices, such a model is still capable of capturing the underlying volatility in crude oil prices. Originality/Value: Oil demand suddenly decreased as a result of the corona outbreak, but then abruptly increased as a result of the conflict in Ukraine. Therefore, there is a need to update the ARIMA model in order to best predict the price of crude oil in a time of exceptional circumstances. Because of the nature of world oil market, predictions for the medium and long term are often therefore, we have limited the scope of our forecasts in this study to a single year in order to achieve the highest level of accuracy.