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.

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.

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 Pandemic Waves' Impact on the Crude Oil Price and the Rise of Consumer Price Index: Case Study for Six European Countries

This study examines the response of the Consumer Price Index (CPI) in local currency to the COVID-19 pandemic using monthly data (March 2020–February 2022), comparatively for six European countries. We have introduced a model of multivariate adaptive regression that considers the quasi-periodic effects of pandemic waves in combination with the global effect of the economic shock to model the variation in the price of crude oil at international levels and to compare the induced effect of the pandemic restriction as well and the oil price variation on each country's CPI. The model was tested for the case of six emergent countries and developed European countries. The findings show that: (i) pandemic restrictions are driving a sharp rise in the CPI, and consequently inflation, in most European countries except Greece and Spain, and (ii) the emergent economies are more affected by the oil price and pandemic restriction than the developed ones.

Update on Risk Criteria for Crude Oil Tanker Fleet

In recent years, the maritime trade of crude oil has suffered notable perturbations caused by the unbalanced relationship between supply and demand. The COVID-19 pandemic caused a drop in oil consumption in 2019, followed by a reduction in production in 2020. The seaborne transport of oil accounts for approximately 50–60% of all crude oil in world production. The crude oil market is a crucial regulator of the global economy and instabilities in this market have noticeable effects on collective risks. The immediate risks that the society see are the changes in the cost of living, which are followed by political uncertainties. Less visible are the risks that these uncertainties have on shipping companies and the level of management stability they have to maintain in order to keep seagoing safe. This paper presents an update on the overall state of risk management for the crude oil tanker fleet, evidenced by EMSA and other international marine organisations. The previous paper, entitled Safety Assessment of Crude Oil Tankers, which applied the methodology of the Formal Safety Assessment (FSA), was published in 2018 and covered the historical data related to the fleet size, accident reports, amount of oil spilled on sea and the economic value of the crude oil transport business. The particular focus of this paper is on the evolution of the risk acceptance criteria over the years and the difference in the predictions from 2018 to the present day. The effects of the pandemic on crude oil shipping are discussed through the changes in the risks. Three of them are analysed: PLL (potential loss of lives), PLC (potential loss of containment) and PLP (potential loss of property). The representation of the risk applies the F-N curves among the risk acceptance criteria lines observed for different tanker sizes. Among the three risks, the paper exposes the vulnerability of the loss of containment risk, where the strong economic impact of the oil trade outweighs the environmental concerns. In relation to the PLC, the paper proposes the approach of relating the oil spill acceptability with the spill quantity and ship revenue instead of to the cost of cleaning or the cost of environment recovery.

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.

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.

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.

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.

Quantile Dependence between Crude Oil Returns and Implied Volatility: Evidence from Parametric and Nonparametric Tests

We examine the daily dependence and directional predictability between the returns of crude oil and the Crude Oil Volatility Index (OVX). Unlike previous studies, we apply a battery of quantile-based techniques, namely the quantile unit root test, the causality-in-quantiles test, and the cross-quantilogram approach. Our main results show evidence of significant bi-directional predictability that is quantile-dependent and asymmetric. A significant positive Granger causality runs from oil (OVX) returns to OVX (oil) returns when both series are in similar lower (upper) quantiles, as well as in opposite quantiles. The Granger causality from OVX returns to oil returns is only significant during periods of high volatility, although it is not always positive. The findings imply that the forward-looking estimate of oil volatility, reflecting the sentiment of oil market participants, should be considered when studying price variations in the oil market, and that crude oil returns can be used to predict oil implied volatility during bearish market conditions. Therefore, the findings have implications regarding predictability under various conditions for oil market participants.

Presidential Policies' Effects on Oil and Gas Price and Productions Within the United States from 1977-2021

Amidst the ongoing COVID-19 pandemic, the contentious U.S. 2020 presidential election featured candidates with quite different stances on regulating the oil and gas industry, leaving many to question the longevity of fossil fuel use. However, little research explores the relationship between presidential policies and the oil market. In this paper, extensive research into presidential energy policies and their effects on domestic oil prices and production dating back to 1977 helps us identify whether we can predict the industry's future under Joe Biden's administration. The paper's results suggest the domestic oil industry is more dependent on external foreign events - with presidential policies offering almost negligible effects on prices and production.

Examining contagion effects between global crude oil prices and the Southeast Asian stock markets during the COVID-19 pandemic

Many previous studies identify the contagion effect among various types of assets, defined as the increase in correlation of these assets during a financial or economic crisis. During the COVID-19 outbreak, a historic fall in global fuel demand and oil prices has been witnessed. Because crude oil has a strategic position among the export products of the Southeast Asian economies, even a tiny global oil price change leads to a plunge in these stock markets. This study addresses the spillovers of the volatility between the West Texas Intermediate crude oil prices and stock indices across six ASEAN emerging economies. Besides, the study examines whether a contagion connecting the global energy prices and these stock markets exists during the coronavirus pandemic. The empirical results are acquired by applying the Bayesian test for equality of means on the dynamic conditional correlations computed from DCC-GARCH models. The findings present positive volatility transmission from crude oil prices toward these emerging equity markets. During the health crisis, co-movements intensify, indicating the occurrence of contagion effects. The empirical results provide valid implications for policymakers and international investors because a precise volatility forecast is vital for managing portfolio risk. © Mien Thi Ngoc Nguyen, 2023.

A New Approach for Forecasting Crude Oil Prices Based on Stochastic and Deterministic Influences of LMD Using ARIMA and LSTM Models

Crude oil is one of the non-renewable power sources and is the lifeblood of the contemporary industry. Every significant change in the price of crude oil (CO) will have an effect on how the global economy, including COVID-19, develops. This study developed a novel hybrid prediction technique that depends on local mean decomposition, Autoregressive Integrated Moving Average (ARIMA), and Long Short-term Memory (LSTM) models to increase crude oil price prediction accuracy. The original data is decomposed by local mean decomposition (LMD), and the decomposed components are reconstructed into stochastic and deterministic (SD) components by average mutual information to reduce the computation cost and enhance forecasting accuracy, predict each individual reconstructed component by ARIMA, and integrate the residuals with LSTM to capture the nonlinearity in residuals and help to find the final prediction result. The new hybrid model LMD-SD-ARIMA-LSTM has reduced the volatility and solved the issue of the overfitting problem of neural networks. The proposed hybrid technique is validated using publicly accessible data from the West Texas Intermediate (WTI), and forecast accuracy are compared using accuracy measures. The value of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) for ARIMA, LSTM, LMD-ARIMA, LMD-SD-ARIMA, LMD-ARIMA-LSTM, LMD-SD-ARIMA-LSTM, and Naïve are 1.00, 1.539, 5.289, 0.873, 0.359, 0.106, 4.014 and 2.165, 1.832, 9.165, 1.359, 1.139, 1.124 and 3.821 respectively. From these results, it is concluded that the proposed model LMD-SD-ARIMA-LSTM has minimum values for MAE and MAPE which assured the superiority of the proposed model in One-step ahead forecasting. Moreover, forecasting performance is also compared up to five steps ahead. The findings demonstrate that the suggested approach is a helpful tool for predicting CO prices both in the short and long term. Furthermore, the current study reduces labor costs by combing the stationary and non-stationary Product Functions (PFs) into stochastic and deterministic components with improved accuracy. Meanwhile, the traditional econometric model can strengthen the prediction behavior of CO prices after decomposition and reconstruction, and the new hybrid forecasting method has better performance in medium and long-term forecasting of the CO price. Moreover, accurate predictions can provide reasonable advice for relevant departments to make correct decisions. © 2013 IEEE.

Crude oil, crypto-assets and dependence: the impact of the COVID-19 pandemic

PurposeThis paper aims to empirically investigate the extent to which interdependence in markets may be driven by COVID-19 effects.Design/methodology/approachThe current global COVID-19 pandemic is adversely affecting the oil market (West Texas Intermediate) and crypto-assets markets.FindingsThe authors find that the dependence structure changes significantly after the global pandemic, providing valuable information on how the COVID-19 crisis affects interdependencies. The results also prove that the performance of digital gold seems to be better compared to stablecoin.Originality/valueThe authors fit copulas to pairs of before and after returns, analyze the observed changes in the dependence structure and discuss asymmetries on propagation of crisis. The authors also use the findings to construct portfolios possessing desirable expected behavior.

Risk Dependence and Risk Spillovers Effect from Crude Oil on the Chinese Stock Market and Gold Market: Implications on Portfolio Management

We analyze crude oil's dependence and the risk spillover effect on the Chinese stock market and the gold market. We compare both static and dynamic copula functions and calculate the average upward and downward spillover effect using the time-varying Copula model and the conditional value-at-risk approach. By utilizing daily data on crude oil prices, China's stock market, and the gold market, we observe an asymmetric spillover effect: the downside spillover effects from crude oil prices on the Chinese stock market and gold market are larger than the upside spillover effect. We then identify changes in the structure of the sample periods and calculate the dynamic conditional correlation between them. In addition, we explore the optimal weight and hedge ratios in diversified portfolios to mitigate potential risks. Our results suggest that investors and portfolio managers should frequently adjust their portfolio strategies, particularly during extreme events like COVID-19, when financial assets become more volatile. Furthermore, crude oil can help reduce the risk in the Chinese stock market and gold market to some extent during different sub-periods. © 2023 by the authors.

Forecasting volatility in oil returns using asymmetric GARCH models: evidence from Tanzania

Crude oil is, without a doubt, one of the most significant commodities in the modern world. The highly contagious coronavirus, the conflict between Ukraine and Russia, and not to mention the unusual turn of events worldwide have all significantly impacted crude oil prices. Since oil is required for all critical economic activities, such as production and transportation, a forecast for crude oil prices is essential. Using a range of GARCH models at such an intense time, this study attempted to close this gap by forecasting crude oil volatility. To forecast the returns of Brent crude oil prices from January 2002 to February 2022, this study uses a family of GARCH models. In the respective family of models, GJRGARCH (1,1) was the most effective in predicting the volatility of crude oil prices. The GJRGARCH model was chosen since it had a higher likelihood value and a lower information criteria value. A diagnostic check was done to evaluate the produced model further to ensure that the proposed model was good enough for forecasting crude oil volatility. The study suggests employing the GJRGARCH technique to predict future fluctuations in exceptional circumstances..