Oil Price Returns Research Articles

Forecasting crude oil prices: Does global financial uncertainty matter?

In this paper, we introduce an informative uncertainty measure, global financial uncertainty (GFU), for the prediction of crude oil price returns. We find that GFU exhibits significant and remarkable forecasting power for crude oil price returns both in- and out-of-sample with monthly R2 of 13.63% and 11.32%, respectively. This predictive power outperforms and complements those of popular economic variables and uncertainty measures. Further analysis shows that a mean–variance investor can obtain considerable economic gains based on the return forecasts of GFU. By dissecting the GFU’s predictability, we observe that the strong forecasting efficacy of GFU for crude oil price returns may stem from its notable power during high-risk conditions and its significant effects on oil demand dynamics.

On the Effects of Physical Climate Risks on the Chinese Energy Sector

We examine the impact of physical climate risks on energy markets in China, distinguishing between traditional energy and new energy stock markets, and the energy commodity market, utilizing a time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR). Specifically, we investigate the dynamic effects of five specific subtypes of physical climate risks, namely waterlogging by rain, drought, typhoon, cryogenic freezing, and high temperature, on WTI oil prices and coal prices. The findings reveal that these physical climate risks exhibit time-varying similar effects on the returns of traditional energy and new energy stocks, but heterogeneous effects on the returns of WTI oil prices and coal prices. Finally, we categorize and examine the impact of both acute and chronic physical risks on the energy commodity market.

Forecasting crude oil returns in different degrees of ambiguity: Why machine learn better?

Numerous studies have demonstrated the strong out-of-sample predictive ability of machine learning models, particularly in variable selection and dimension reduction, on crude oil price returns. We find significant disparities in out-of-sample predictive performance between these two methods under varying degrees of ambiguity, a fuzziness measure proposed by Izhakian (2020), independent of outcomes, risks, and attitudes. Variable selection methods exhibit strong out-of-sample predictive power in low ambiguity environments, but weaker performance in high ambiguity environments, whereas dimension reduction methods show the opposite pattern. Furthermore, the optimal penalty coefficient selected by variable selection methods during in-sample model fitting is highly correlated with ambiguity, indicating that the predictive ability of variable selection stems from its ability to accurately identify the correct predictors.

Between Exchange Rate Returns and Crude Oil Returns in Oil-Importing Countries

Fluctuations in the financial markets stem from the reactions of investors to both market activities and more wide-ranging macroeconomic indices. This research examines the interconnectivity between returns on exchange rates and crude oil prices for ten oil-importing countries. Quarterly data spanning the period from 2000Q1 to 2022Q4 was used in the estimation. Returns had to be calculated from raw data, which were exchange rates and crude oil prices. The research methodologies include quantile regression and VAR-GARCH estimations. The study revealed a long-term association between crude oil market returns and foreign exchange markets, with currency fluctuations negatively impacting crude oil returns. The spill-over effect from the currency market to the oil market is weaker than the transmission effect from the oil market to the currency market of oil-importing countries. In particular, it was empirically established that shocks from past volatility in the currency markets of countries that import oil had commensurately lower volatility in the oil market by 6 percent. Whereas, the spillover from the crude oil market to the currency market depicts that increased turbulence on the crude oil market in the current period stimulated 55.66 percent amplified volatility in the currency market for oil-importing nations. Oil returns had a volatility persistence value of 0.1255, ratifying the weak volatility persistence of oil returns. The size of the volatility persistence of exchange rate returns is 0.997, an indication of high volatility persistence for currency returns. The study found the absence of leverage effects for currency values given a positive coefficient with a magnitude of 0.8529. In other words, bad news does not cause higher turbulence in exchange rate returns than good news will. For crude oil returns, the size of the leverage effects term is -0.0529, which is negative and significant (p<.05), implying that bad news in oil-importing economies has asymmetric impacts on the volatility of returns on crude oil prices. In effect, the oil price returns react more strongly to bad news than these returns react to good news. The findings of the study underscore the intricate relationships between local currencies and energy prices within the context of a global financial market, highlighting the significance of understanding these dynamics for effective decision-making and risk management in an increasingly interconnected world. It was recommended, among others, that international collaboration amongst countries is crucial given the global nature of energy markets.

Oil Price Dynamics and Sectoral Indices in India – Pre, Post and during COVID Pandemic: A Comparative Evidence from Wavelet-based Causality and NARDL

Due to the COVID pandemic, the stock market has been affected adversely around the globe and investment decisions are now more challenging and riskier. Hence, in this paper, we aim to investigate the impact of oil prices on the Indian stock market and eight sectoral indices for the period of pre, post, and during the COVID pandemic. The maximal overlap discrete wavelet transform (MODWT) is used to decompose and to denoise the original time series data as oil price and market return are found to be noisy. We employ the wavelet-based Granger causality (WGC) and non-linear, autoregressive distributed lag model (NARDL) to investigate the causality in the frequency domain as well as the short-run and long-run asymmetry of oil price impact. Our analysis shows a feedback relation between low frequency (higher investment horizon) and the long-run asymmetric impact of oil prices on all sectors during all three periods. We discuss the dynamic time-varying relationship between the oil price and sectoral return along with the investment implications in detail.

Co-movement between COVID-19, oil price and American stock market during 2020: Fresh investigation from partial and multiple wavelet methods

The pandemic has brought economic and social challenges to society, policymakers, financial market investors and firms. The current study aims to shed light on the financial aspects of COVID-19 during the first wave in 2020. In doing so, the study aims to explore the association between COVID-19 cases, oil prices, and the stock market returns (Standard & Poor's). In this paper, the authors use the daily new cases of COVID-19, oil price and stock market return from 21 January 2020 to 10 April 2020. To check the nexus between studied factors, we apply Wavelet Coherence, Partial Wavelet Coherence and Multiple Wavelet Coherence methods. The graphical empirics of Wavelet Coherence Methods mentioned interesting outcomes; (i) COVID-19 and oil price tend to move in the same direction, with COVID-19 leading, (ii) COVID-19 affect stock market returns, and (iii) oil price and stock market returns have strong co-movement.

The Nexus of Crude Oil and Food Prices: Evidence of the Crude Oil Prices Influence on Food Insecurity Problem

Food cost is a fundamental factor in reducing world hunger and minimizing the food insecurity problem. Agricultural commodities prices, especially grains, are determinants of food costs. Therefore, studying the behavior of commodity prices responsible for food security on the planet is essential for economic agents, especially for macroeconomic policy decision-makers. Food production and prices are related to energy prices, mainly through biofuels and fertilizers. Among energy sources, crude oil is one of the main ones in the world's energy matrix. Many studies on crude oil and food prices have been carried out relating energy and food or, more specifically, crude oil and agricultural commodity prices. This work examines the dynamic relationship between oil and grain commodity prices: rice, wheat, corn and soybeans. It also verifies the causality and cointegration between each grain and crude oil price return. Besides that, Autoregressive vector models were estimated to infer the impulse response function and the variance decomposition. The sample period corresponds to the interval between the two biggest crises of the century, the subprime financial crisis and the Covid-19 pandemic sanitary crisis. Thus, the data is not impacted by significant abnormal variations caused by these crises. The inferences show an interaction between crude oil prices practiced in the international market and food commodity prices.

Predicting carbon and oil price returns using hybrid models based on machine and deep learning

SummaryPredicting carbon and oil prices is recently gaining relevance in the climate change literature. This is due to the fact that conventional energy market analysis and the design of mechanisms for climate change mitigation constitute key variables for artificial carbon markets. Yet, modelling non‐linear effects in time series remains a major challenge for carbon and oil price forecasting. Hence, hybrid models seem to be appealing alternatives for this purpose. This study evaluates the performance of 12 hybrid models, which weigh results from random forest, support vector machine, autoregressive integrated moving average and the non‐linear autoregressive neural network models. The weights are determined by (i) assuming equal weights, (ii) using a neural network to optimise individual weights and (iii) employing deep learning techniques. The findings of our work confirm the salient characteristics of modelling the non‐linear effects of time series and the potential of hybrid models based on neural networks and deep learning in predicting carbon and oil price returns. Furthermore, the best results are obtained from hybrid models that combine machine learning and traditional econometric techniques as inputs, which capture the linear and non‐linear effects of time series.

Re-validating the Phillips Curve hypothesis in Africa and the role of oil prices: A mixed-frequency approach

This paper investigates the relationship between unemployment and inflation in 29 African economies by re-examining the Phillips Curve hypothesis and evaluating the role of global oil prices in predicting inflation. Existing studies often use uniform-frequency variables, which can lead to biased results by omitting information from high-frequency series. We employ a mixed data sampling regression model to analyze inflation in selected African countries by regressing monthly inflation rates on annual unemployment rates and daily oil price returns. Our findings show that the Phillips Curve hypothesis holds in a few countries across Central Africa (Cameroon, the Central African Republic, Chad, and Republic of the Congo), East Africa (Rwanda), North Africa (Algeria), Southern Africa (Botswana and Malawi), and West Africa (Burkina Faso and Mauritania). In the other countries, the hypothesis does not hold, potentially indicating a predominance of cost-push inflation. Our analysis demonstrates that global oil prices impact inflation differently across these countries. Additionally, daily oil price data significantly enhance inflation forecasting compared to monthly data. Incorporating additional economic variables, such as exchange rates and interest rates, further improves forecast accuracy. We recommend targeted monetary policies to mitigate the impacts of oil price fluctuations and unemployment on inflation.

The Asymmetric Relationship between Conventional/Shale Rig Counts and WTI Oil Prices

This work analyses the asymmetric response of conventional and shale oil rig counts to WTI oil price returns. Our analysis shows that the rig count time series exhibited a structural change after the oil glut of 2014. All series are non-stationary in each sub-period but not cointegrated. Therefore, after controlling for possible confounding factors, a vector auto regressive (VAR) model is set up. Our specification accounts for the possible role of oil production and distinguishes between positive and negative oil price changes. It is shown that shale and conventional rig counts reacted differently in each subperiod to signed changes in oil price. Subsequently, by evaluating the response of rig counts to oil price shocks, their intensity and duration over time, we observe that the shale oil rig count reacts more intensively to positive than to negative oil price changes. On the contrary, the conventional rig count exhibits a modest reaction only to positive price changes. Finally, we robustify our findings by focusing on the data of the Permian basin, on the one hand, and the Anadarko, Bakken, Eagle Ford and Niobrara, on the other hand, which are characterized by different patterns in the number of Drilled but not Completed wells.

Stock market bubbles and the realized volatility of oil price returns

Using monthly data for the G7 countries from 1973 to 2020, we study whether stock market bubbles help to forecast out-of-sample the realized volatility of oil price returns. We use the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator (MS-LPPLS-CI) approach to identify both positive and negative bubbles in the short-, medium, and long-term. First, we successfully detect major crashes and rallies using the MS-LPPLS-CIs. Having established the relevance of the bubbles indicators, and given the large number of them, we use widely-studied shrinkage (Lasso, elastic net, ridge regression) approaches to estimate our forecasting models. We find that stock market bubbles have predictive value for realized volatility at a short to intermediate forecast horizon. The number of bubble predictors included in the penalized forecasting models tend to increase in the forecast horizon. We obtain our main finding for the various types of stock market bubbles, and for good and bad realized volatilities.

Causal Nexus Between COVID-19 Risk and Global Oil Price Shocks: Evidence From a Time and Frequency Causality Approach

This paper examines the causal relationship between global oil price returns and aggregate COVID-19 index and COVID-19 uncertainty index by using daily data from 31st December 2020 to 28th April 2021. The results from the causality test show unidirectional causality from aggregate COVID-19 index to oil price returns and from uncertainty index to oil price returns. Furthermore, the frequency domain causality reveals the same results for both short and long periods. From a policy perspective, investors and industry people should pay attention to the uncertainty caused by COVID-19.

Gold Price, Oil Price, and Stock Market Return Spillovers: Empirical Evidence from Vietnam

This paper analyzes the spillovers of oil prices, gold prices and stock market returns in Vietnam. By adopting the time-varying parameter vector autoregression model (TVP-VAR), the results show a moderate interdependence among the variables from 2010-2022. Additionally, the relationship between oil prices, gold prices, and stock market returns changes over time and is influenced by economic and political events. Overall, stock market returns are net shock transmitters with the highest volatility among all the variables, while the oil and gold markets are net recipients. Finally, our results remain robust to Vietnam's alternative stock market index.

Modelling the Impact of Crude Oil Prices and Stock Price Index on Indonesia’s Exchange Rates

Abstract This paper employs various GARCH-type models and the daily data from 3 July 2006 to 30 June 2021 to examine the effect of crude oil prices and stock price index on exchange rates for Indonesia, the largest oil producer in Southeast Asia. Since the share markets and oil prices are very volatile, testing the stability of the parameters or system is desirable. We achieve this by using the Nyblom’s fluctuations test and account for the structural break associated with the fluctuations. Findings reveal that lower oil price return leads the Indonesian currency per US dollar to depreciate. In addition, we find that stock return has negative and significant relation with exchange rates. This lends support to the portfolio balance effect in which a decrease in stock prices leads to a depreciation of Indonesian Rupiah against the US dollar. Evidence from EGARCH model shows that shocks to the volatility of exchange rate have a symmetrical effect. Our results suggest that as lower oil prices and stock prices contributes to depreciation of Indonesia rupiah against USD, an appropriate monetary policy may require adjustment of interest rates to resist the exchange rate fluctuations without being detrimental to the banking system.

The impact of joint events on oil price volatility: Evidence from a dynamic graphical news analysis model

The analysis of event-oriented oil market risk deserves attention owing to the diversity, complexity and time variant of the oil-related events. From the aspect of oil price volatility, we examine the dynamic impact of joint events of different aspects on the oil market using graph analytical models and the proposed value-at-risk estimation approach. Based on the construction and evaluation of news indices, we confirm that events of six categories have strong short-term causality with oil price returns. Furthermore, we prove the capacity of event-oriented indices on oil market risk assessment tasks. Demand and energy-based events have shown their effect in improving oil market risk estimation, as proved by empirical analysis of assessing value-at-risk of Brent and WTI oil price. This paper confirms the impact of oil-related joint events on oil market risk and provides a new perspective of assessing latent oil market risks associated with exogenous events.

Unveiling the relationship between oil and green bonds: Spillover dynamics and implications

This study investigates the spillover dynamics and directionality between oil prices (OP) and green bonds (GB) using a bivariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) framework. The research aims to contribute to the understanding of the complex relationship between OP and GB and provide valuable policy insights to stakeholders. The results confirm the presence of significant spillovers between the two markets, indicating a transmission of risk and contagion. While the unconditional correlation between the returns of OP and GB is close to zero, bivariate asymmetric GARCH-BEKK estimations reveal a clear connection between the two markets. Past return shocks in the GB market are found to mitigate subsequent volatility in the OP, suggesting a level of substitution between the two markets. Volatility spillovers between the two markets are also evident, although their signs vary. This study contributes to the literature by filling a research gap and offering insights into the spillover dynamics and directionality between OP and GB. The results highlight the interconnectedness and transmission of risk between the two markets, suggesting the need to consider their relationship for investment decisions. Policymakers, investors, and other stakeholders can benefit from the insights provided in this research for effective risk management and investment strategies.

Determinant of Forex Rolling Spot Contracts on Indonesia Commodity andDerivative Exchange (ICDX)

One of the derivative futures contract types is the Forex Rolling Spot Contracts (FRSC) traded in the Indonesia Commodity and Derivatives Exchange (ICDX). So far, no research has been conducted on this product type, which can be used as an investor reference. This study aims to find empirical evidence of how far macroeconomic variables affect the returns of ten FRSC investment instruments: AUD/USD, EUR/USD, GBP/USD, USD/JPY, USD/CHF, GBP/JPY, EUR/JPY, AUD/NZD, NZD/USD, and USD/CAD traded at ICDX during the period 2021 - 2022. The statistical analysis method used in this research is quantitative with descriptive explanations. Data collection techniques were obtained from secondary data through library research. Secondary data was obtained through the internet sites of ICDX, Indonesia Stock Exchange (IDX), Bank Indonesia, London Bullion Metal Association, Bloomberg, and the US Energy Information Administration. The analytical method used in this study is panel data analysis. The study type was an explanatory study with data processing using the STATA application. The study results showed that the determinant of interest rate and inflation did not affect FRSC return; JCI return and gold price return had a significant positive on FRSC return; bond index return and crude oil price return had a negative effect on FRSC return.

Oil price returns and firm's fixed investment: A production pattern

This paper aims to enlarge the acquaintance with the influence of oil price returns on fixed investment of all Chinese non-financial listed companies. According to our empirical results, oil price returns have a statistically and economically significant and negative predictive power for firm's fixed investment. Other main findings can be summarized as follows: (i) this oil return effect is robust and consistent even considering oil shocks and its different sources; (ii) the heterogeneities of this effect across industries and industrial layers present a “production pattern”; (iii) a composite cost and the financing constraints theory reveal the heterogeneities across firms with varying financial constraints, investment irreversibility, and industry competition; (iv) a combination of changes in demand stimulation and cost pressures contributes to the asymmetric impacts of oil price returns, which vary throughout the oil prices distribution.

Energy commodity and stock market interconnectedness: Evidence from carbon emission trading system

This research paper investigates the various dependence structures across oil prices, emission prices and stock markets for Middle East and Gulf Cooperation Council (GCC) countries. This study enables us to form a judgement on how the phased implementation of Kyoto protocol and COP26 on climate change is likely to be translated into changes in policy multipliers as the changes feed into the real economy through stock market movements. We employ the spillover index developed by Diebold and Yilmaz (2012) and Baruník and Křehlík (2018) to identify the most strongly related stock index to oil price return. Also, we investigate how emission price (which represents the cost of pollution) changes feed into oil price and how that impact on stock markets and what is the short- and long run spillovers between them. It is important to acknowledge some of the limitations of this study. One potential limitation is the focus on a specific geographic region, the Middle East and GCC countries. This may limit the generalizability of the findings to other regions. Additionally, the study focuses on the dependence structure among oil prices, emission prices, and stock markets, and does not consider other factors that may influence the relationship between these variables. Nonetheless, our study provides useful empirical evidence for assessing the impact of energy transition on both oil prices and stock price movements and can inform the development of diversified revenue streams for the region. In terms of future research, it would be worthwhile to extend the analysis to include additional variables, such as renewable energy prices, to gain a more comprehensive understanding of the dynamics at play in the energy markets of the Middle East and GCCs.

Asymmetric Impact of Structural Shocks of Crude Oil Price on Equity Market in Two Large Emerging Market Economies: Evidence from China and India

The objective of this particular study is to understand the asymmetric impact of structural shocks (in terms of supply and demand shocks on crude oil price) on equity market returns in the context of the largest two emerging market economies, that is, China and India. The timeframe of this study is January 1996 to January 2022. The study has been conducted, based on monthly data. The present study employs a structural vector auto regression as well as NARDL model to understand the asymmetry of these structural shocks. Our findings are in contradiction to the popular perception of the Press that crude oil price and equity market returns are inversely related. The present study found that different structural shocks have very different impacts on the stock returns. Our study also adds to the existing literature, as it showed that both the Chinese and Indian markets react in similar fashion to structural shocks in the crude oil market. Structural shocks have no impact on the Indian market, whereas there is a significant asymmetric impact of only precautionary demand shock in, both, the long and short runs, in the context of the Chinese market.