West Texas Intermediate Oil Prices Research Articles

Forecasting international tourist arrivals in South Korea: a deep learning approach

Purpose This study aims to develop a robust long short-term memory (LSTM)-based forecasting model for daily international tourist arrivals at Incheon International Airport (ICN), incorporating multiple predictors including exchange rates, West Texas Intermediate (WTI) oil prices, Korea composite stock price index data and new COVID-19 cases. By leveraging deep learning techniques and diverse data sets, the research seeks to enhance the accuracy and reliability of tourism demand predictions, contributing significantly to both theoretical implications and practical applications in the field of hospitality and tourism. Design/methodology/approach This study introduces an innovative approach to forecasting international tourist arrivals by leveraging LSTM networks. This advanced methodology addresses complex managerial issues in tourism management by providing more accurate forecasts. The methodology comprises four key steps: collecting data sets; preprocessing the data; training the LSTM network; and forecasting future international tourist arrivals. The rest of this study is structured as follows: the subsequent sections detail the proposed LSTM model, present the empirical results and discuss the findings, conclusions and the theoretical and practical implications of the study in the field of hospitality and tourism. Findings This research pioneers the simultaneous use of big data encompassing five factors – international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases – for daily forecasting. The study reveals that integrating exchange rates, oil prices, stock market data and COVID-19 cases significantly enhances LSTM network forecasting precision. It addresses the narrow scope of existing research on predicting international tourist arrivals at ICN with these factors. Moreover, the study demonstrates LSTM networks’ capability to effectively handle multivariable time series prediction problems, providing a robust basis for their application in hospitality and tourism management. Originality/value This research pioneers the integration of international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases for forecasting daily international tourist arrivals. It bridges the gap in existing literature by proposing a comprehensive approach that considers multiple predictors simultaneously. Furthermore, it demonstrates the effectiveness of LSTM networks in handling multivariable time series forecasting problems, offering practical insights for enhancing tourism demand predictions. By addressing these critical factors and leveraging advanced deep learning techniques, this study contributes significantly to the advancement of forecasting methodologies in the tourism industry, aiding decision-makers in effective planning and resource allocation.

Explore the Impact of Macroeconomic Indicators on WTI Oil Prices

This research paper explores the relationships between West Texas Intermediate (WTI) oil prices and various macroeconomic indicators. Given the significant role of WTI prices in shaping global economic activities, understanding their volatile dynamics is crucial for numerous industries. Predicting these prices, however, poses significant challenges due to the complex nature of the oil market, influenced by myriad factors such as geopolitical developments, supply-demand imbalances, and diverse macroeconomic parameters. The research addresses this issue by developing a predictive model for WTI oil prices, deploying an Autoregressive Integrated Moving Average (ARIMA) model. The study has a selection of macroeconomic variables that accurately reflect the global economy's various aspects influencing WTI prices. These include the NASDAQ index, the US Dollar Index (DXY), the Economic Uncertainty Index, the 5-Year Inflation Break Even, the US 3-Month Treasury, the 10Y Less 2Y, the US Oil Demand, and the difference between oil Demand and Supply. This research aims to deepen our understanding of the multifaceted relationship between WTI oil prices and these critical macroeconomic indicators. In doing so, this study offers substantial academic contributions to comprehending oil price dynamics. Additionally, it provides valueable insights for industry stakeholders, financial analysts, policymakers, and investors, enhancing economic planning and risk management across sectors.

The impact of Russia–Ukraine war on crude oil prices: an EMC framework

As the second-largest oil producer and natural gas exporter, Russia’s war with Ukraine has severely impacted the energy market. To what extent has the war influenced crude oil prices, and has it altered the long-term dynamics of oil prices? An objective analysis of the effects of the Russia–Ukraine war on the crude oil market can assist relevant entities in developing both short-term emergency strategies and long-term response plans. This study establishes an analytical framework of the event analysis method based on multiresolution causality testing (EMC). The results of the multiresolution causality testing reveal a significant one-way causality between the Russia–Ukraine war and crude oil prices. Afterward, using the event analysis based on variational mode decomposition (VMD), from October 1, 2021, to August 25, 2022, as the event window, we found that the war and its chain events caused the West Texas Intermediate (WTI) crude oil prices to increase by $37.14, a 52.33% surge, and the Brent crude oil price to rise by $41.49, a 56.33% increase. During the event window, the Russia–Ukraine war can account for 70.72% and 73.62% of the fluctuation in WTI and Brent crude oil prices, respectively. Furthermore, the war amplified oil price volatility and fundamentally altered the trend of crude oil prices. Consequently, this study proposes four recommendations: the establishment of an emergency management mechanism for the oil market, the diversification of oil and gas imports by energy-importing countries, the steady advancement of energy transformation, and the judicious use of financial instruments by enterprises to hedge risks.

Asymmetry in the prices of crude oil and diesel and gasoline prices in Brazil

PurposeThe aim of this study was to determine if there are asymmetries in the pass-through of West Texas Intermediate (WTI) crude oil prices to its derivatives (diesel and gasoline) in the Brazilian market.Design/methodology/approachInitially, the future WTI oil price series was analyzed using the self-exciting threshold autoregressive (SETAR) and logistic smooth transition autoregressive (LSTAR) non-linear models. Subsequently, the threshold autoregressive error-correction model (TAR-ECM) and Markov-switching model were used.FindingsThe findings indicated high prices throughout 2008 due to the subprime crisis. The findings indicated high prices throughout 2008 due to the subprime crisis. The results indicated that there is long-term pass-through of oil prices in both methods, suggesting an equilibrium adjustment in the prices of diesel and gasoline in the analyzed period. Regarding the short term, the variations in contemporary crude oil prices have positive effects on the variations in fuel prices. Lastly, this behavior can partly be explained by the internal price management structure adopted during almost all of the analyzed period.Originality/valueThis paper contributes to the literature at some points. The first contribution is the modeling of the oil price series through non-linear models, further enriching the literature on the recent behavior of this time series. The second is the simultaneous use of the TAR-ECM and Markov-switching model to capture possible short- and long-term asymmetries in the pass-through of prices, as few studies have applied these methods to the future price of oil. The third and main contribution is the investigation of whether there are asymmetries in the transfer of oil prices to the price of derivatives in Brazil. So far, no work has investigated this issue, which is very relevant to the country.

Research of the non-linear dynamic relationship between global economic policy uncertainty and crude oil prices

This paper examines the relationship between global economic policy uncertainty (GEPU, Global economic policy uncertainty refers to the fact that market actors cannot accurately predict whether the government will change the current economic policy, if so, when the government will make changes, and what the content of the current policy changes will be. These are difficult for economic actors to accurately predict.) and crude oil prices from a global perspective within a non-linear analytical framework. Firstly, the non-linear dynamic trends between GEPU and WTI (West Texas Intermediate) crude oil prices are examined using the VAR-filtered BDS test, followed by the test of the non-linear Granger causality, which shows significant non-linear causal relationship between GEPU and WTI oil prices. Further, this paper use threshold vector autoregressive model (TVAR) and threshold vector error correction model (TVECM) to analysis the long-term and short-term non-linear dynamic adjustment processes between GEPU and WTI oil prices, the result shows that there is an optimal threshold between GEPU and WTI. In the low global economic policy uncertainty regime, the relationship between GEPU and WTI is mutually beneficial, while in the high global economic policy uncertainty regime, the relationship between GEPU and WTI is mutually inhibited. Meanwhile, the TVECM results show that in the first regime, GEPU adjusts towards equilibrium at a faster rate, while in the second regime, WTI adjusts towards equilibrium at a faster rate.

Copper price prediction using LSTM recurrent neural network integrated simulated annealing algorithm.

Copper is an important mineral and fluctuations in copper prices can affect the stable functioning of some countries' economies. Policy makers, futures traders and individual investors are very concerned about copper prices. In a recent paper, we use an artificial intelligence model long short-term memory (LSTM) to predict copper prices. To improve the efficiency of long short-term memory (LSTM) model, we introduced a simulated annealing (SA) algorithm to find the best combination of hyperparameters. The feature engineering problem of the AI model is then solved by correlation analysis. Three economic indicators, West Texas Intermediate Oil Price, Gold Price and Silver Price, which are highly correlated with copper prices, were selected as inputs to be used in the training and forecasting model. Three different copper price time periods, namely 485, 363 and 242 days, were chosen for the model forecasts. The forecast errors are 0.00195, 0.0019 and 0.00097, respectively. Compared with the existing literature, the prediction results of this paper are more accurate and less error. The research in this paper provides a reliable reference for analyzing future copper price changes.

PERBANDINGAN MODEL ARIMA DENGAN MODEL NONPARAMETRIK POLINOMIAL LOKAL DAN SPLINE TRUNCATED UNTUK PERAMALAN HARGA MINYAK MENTAH WEST TEXAS INTERMEDIATE (WTI) DILENGKAPI GUI R

Crude oil as one of the most important natural resources experiences price fluctuations from time to time, even the spot price of West Texas Intermediate (WTI) world crude oil on 20th April 2020 reached -36,98 USD/barrel due to the Covid-19 pandemic. WTI oil price data was modeled using the ARIMA method, local polynomial, and spline truncated nonparametric regression then compared and obtained the best model and formed R Graphical User Interface (GUI). The ARIMA model and nonparametric time series models can be used to model time series data, but in the ARIMA model there are assumptions that must be fulfilled. Nonparametric time series models, which include local polynomial model and truncated spline do not need to fulfill these assumptions. The ARIMA model obtained did not fulfilled the assumptions of normality and residual homoscedasticity, so the modeling was stopped and modeling was only carried out using nonparametric regression methods. Based on the minimum MSE criteria, the best nonparametric model was obtained, namely nonparametric truncated spline model with degrees 3 and 3 knot points which was categorized as a strong model based on R-squared in sample value and having a very good forecasting ability based on MAPE out sample value.

Symmetric and asymmetric GARCH estimations of the impact of oil price uncertainty on output growth: evidence from the G7.

Crude oil is an essential source of energy. Without access to energy, output growth is impossible. As a result of this link, volatility in oil prices has the ability to induce fluctuations in the output of both developed and developing economies. Moreover, factors such as business cycles and policy changes often introduce nonlinearity into the transmission mechanism of oil price shocks. This study therefore examines not only the interconnectedness of oil price volatility and output growth, but also the nonlinear, asymmetric impact of oil price volatility on output growth in the countries making up the Group of Seven. To this end, monthly data on West Texas Intermediate oil price and industrial production indices of the Group of Seven countries over the period 1990:01 to 2019:08 is used for empirical analysis. The study employs the DCC and cDCC-GARCH techniques for symmetric empirical analysis. The asymmetric empirical analysis is also conducted via GJR-GARCH, FIEGARCH, HYGARCH and cDCC-GARCH techniques. The findings reveal disparities in the magnitudes of the positive and negative (asymmetric) effects of oil price shocks on output growth. The results also reveal that past news and lagged volatility have a significant impact on the current conditional volatility of the output growth of the Group of Seven countries. The study concludes that the impact of oil price volatility on output growth in the selected economies is asymmetric, the volatility is highly persistent and clustered, and the asymmetric GARCH models outperform the symmetric GARCH models.

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

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

The hybrid model of autoregressive integrated moving average and fuzzy time series Markov chain on long-memory data

IntroductionThe price of crude oil as an essential commodity in the world economy shows a pattern and identifies the component factors that influence it in the short and long term. The long pattern of the price movement of crude oil is identified by a fractionally time series model where the accuracy can still be improved by making a hybrid residual model using a fuzzy time series approach.MethodsTime series data containing long-memory elements can be modified into a stationary model through the autoregressive fractional integrated moving average (ARFIMA). This fractional model can provide better accuracy on long-memory data than the classic autoregressive integrated moving average (ARIMA) model. The long-memory data are indicated by a high level of fluctuation and the autocorrelation value between lags that decreases slowly. However, a more accurate model is proposed as a hybridization time series model with fuzzy time series Markov chain (FTSMC).ResultsThe time series data collected from the monthly period of West Texas Intermediate (WTI) oil price as the standard for world oil prices for the 2003–2021 time period. The data of WTI oil price has a long-memory data pattern to be modeled fractionally, and subsequently their hybrids. The times series model of crude oil price is obtained as the new target model of hybrid ARIMA and ARFIMA with FTSMC, denoted as ARIMA-FTSMC and ARFIMA-FTSMC, respectively.DiscussionThe accuracy model measured by MAE, RMSE, and MAPE shows that the hybrid model of ARIMA-FTSMC has better performance than ARIMA and ARFIMA, but the hybrid model of ARFIMA-FTSMC provides the best accuracy compared to all models. The superiority of the hybrid time series model of ARFIMA-FTSMC on long-memory data provides an opportunity for the hybrid model as the best and more precise forecasting method.

Forecasting of WTI crude oil using combined ANN-Whale optimization algorithm

ABSTRACT The current study predicts West Texas Intermediate (WTI) petroleum prices using an artificial neural network (ANN) with a whale optimization algorithm (WOA). In implementing the model, five parameters, including gold price, coal price, natural gas price, Dollar-Euro exchange rate, and Dollar-Yuan exchange rate, have been used as input to the combined model. The intelligent and basic ANN algorithm results compared to finding the ANN-WOA algorithm capacity in predicting the future price of WTI oil. ANN-WOA model improved the WTI price predicting accuracy up to 22% compared to the ANN. The ANN-WOA method with a value of R2 = 0.93 compared to the ANN method with a value of R2 = 0.75 was able to reduce the model error well. According to the significant impact that the input parameters of the combination model had on the WTI oil price prediction, therefore, in studies that predict price or other variables, highly correlated variables can significantly increase the accuracy of the forecast.

Stock returns, oil prices and leverage: evidence from US firms

PurposeThis paper aims to investigate how the relation between stock returns of US firms and West Texas Intermediate (WTI) oil prices is affected by leverage from 1990 to 2020.Design/methodology/approachThis paper examines how the relationship between stock returns of US firms and WTI oil prices is affected by leverage from 1990 to 2020 using a fixed-effect model estimation framework.FindingsResults from the fixed-effect regression models suggest that leverage effects on stock returns are pervasive both in aggregate and cross-industry levels, while the mining industry is more sensitive. In addition to the positive oil price effects attenuated by leverage at the aggregate level, the authors observe stronger marginal effects of leverage only for the mining sector. Being more exposed to commodity prices, the positive effects of oil prices on stock returns in the mining sector are offset by large debt ratios. Asymmetries, effects of debt maturity structure and implications are also discussed.Research limitations/implicationsThis study is grounded on the contemporary cash flow claim of leverage NOT on the long-run effect of leverage considering cash flow constraints. The oil price increase is assumed to represent an advancement of the overall economy. This study does not capture the oil prices response to some other economic forces and vice-versa.Practical implicationsMining companies should therefore reduce the stock of debt with respect to their assets to make possible the “pass-through” from oil prices to the stock market.Originality/valuePreviously undocumented and the authors show that leverage reduces the total effect of oil prices on stock returns, consistent with the hypothesis. Asymmetric and debt maturity structures effects are also discussed.

The asymmetric and multi-scale volatility correlation between global oil price and economic policy uncertainty of China.

With the monthly data of WTI oil price and economic policy uncertainty (EPU) of China from January 2000 to August 2020, this paper detailedly investigates the asymmetric volatility correlations between two types of EPU of China and global oil price in different time scales. The empirical results demonstrate that the volatility correlation between EPU of China and West Texas Intermediate (WTI) oil price is mainly reflected in the monetary policy uncertainty (MPU), while that of fiscal policy uncertainty (FPU) is much weaker. Specifically speaking, the volatility correlation between MPU of China and downward WTI oil price is significantly negative in the short-middle term (4–8 months) and changes to positive in the middle-long term (8–16 months), while that of upward WTI oil price is only significantly positive in the long term (16–32 months). Our findings provide a deeper understanding of the oil price-EPU correlation in China, and can be valuable guidance for diversified market participants such as government policy-makers and global investors.

Tail Dependence between Crude Oil Volatility Index and WTI Oil Price Movements during the COVID-19 Pandemic

This study investigates the dependence between extreme returns of West Texas Intermediate (WTI) crude oil prices and the Crude Oil Volatility Index (OVX) changes as well as the predictive power of OVX to generate accurate Value at Risk (VaR) forecasts for crude oil. We focus on the COVID-19 pandemic period as the most violate in the history of the oil market. The static and dynamic conditional copula methodology is used to measure the tail dependence coefficient (TDC) between the variables. We found a strong relationship in the tail dependence between negative returns on crude oil and OVX changes and the tail independence for positive returns. The time-varying copula discloses the strongest tail dependence of negative oil price shocks and the index changes during the COVID-19 health crisis. The findings indicate the ability of the OVX index to be a fear gauge with respect to the oil market. However, we cannot confirm the ability of OVX to improve one day-ahead forecasts of the Value at Risk. The impact of investors’ expectations embedded in OVX on VaR forecasts seems to be negligible.

Statistical properties of the detrended multiple cross-correlation coefficient

Cross-correlations are ubiquitous in nonstationary multivariable systems. By using detrended cross-correlation (DCCA) technique, recently a new multiple cross-correlation coefficient DMC was proposed to quantity the correlation between multivariate and a target variable. We studied the statistical properties of DMC in detail. More specifically, we first proved that 0≤DMC≤1, and then deduced that the DMC is approximately χ2 distributed for overlapping windows with different window size. In addition, the range of DMC was determined numerically by statistical test, where the multiple correlations can be regarded as statistically significant. Furthermore, we discussed the influence of the cross-correlation among the target variable and independent variables on DMC. Finally, by utilizing this statistical test on financial system and environment data, we found that the US dollar index affects international gold spot prices and West Texas Intermediate oil price greatly, and PM2.5 dominates in Beijing’s AQI system.

Unveiling the Effect of Mean and Volatility Spillover between the United States Economic Policy Uncertainty and WTI Crude Oil Price

Grounded in the Granger causality test, vector autoregression (VAR) model, and BEKK-GARCH model, our current study aims to examine the effect of mean and volatility spillover between the United States (US) economic policy uncertainty (EPU) and West Texas Intermediate (WTI) crude oil price. Using the US EPU monthly index and WTI spot price data from 1996 to 2019, we revealed that there is a one-way Granger causality link between the US EPU and spot price of WTI crude oil. The VAR model not only illustrated that there is a mean spillover effect between WTI oil price and US EPU, but they will also be affected by its memory, as well as the other’s past. At the same time, it also pointed out that this correlation has positive and negative directions. The BEKK-GARCH model test yielded similar conclusions to the VAR model and, importantly, proved a two-way volatility spillover effect between the US EPU and WTI spot price fluctuations. In conclusion, US economic policy has a substantial influence on the variation of global crude oil prices, as an essential strategic reserve resource and will also influence the government’s economic policy formulation. Understanding the association between WTI crude oil price and policy uncertainty not only helps investors to manage assets allocations and mitigate losses but also guides US policymakers to adjust the energy structure for economic sustainability.

U.S. stock prices and macroeconomic fundamentals: Fresh evidence using the quantile ARDL approach

AbstractThis paper explores the long‐run relationship and the associated short‐run dynamics between the U.S. stock market and three major macroeconomic fundamentals, namely the U.S. industrial production index, the U.S. 10‐year Treasury bond yield and the West Texas Intermediate oil price, for the time period covering 1985–2015. The quantile autoregressive distributed lag (QARDL) model presented by Cho et al. (2015) Journal of Econometrics, 188, 281–300, which combines the autoregressive distributed lag model of Pesaran and Shin (1998), Cambridge University Press, and Pesaran et al. (2001) Journal of Applied Econometrics, 16, 289–326, and the quantile regression methodology of Koenker and Bassett (1978), Econometrica, 46, 33–50, is used. This approach analyses the links among the variables over a range of quantiles which represent different states of the equity market. The findings show a significant quantile‐varying cointegration relationship between U.S. equity prices and the three selected macroeconomic factors. The U.S. industrial production index is found to have the largest long‐run influence on the equity market. These findings highlight the importance of market players as well as policymakers to develop a greater understanding and awareness of how macro variables impact stock prices under different stock market conditions, particularly in relation to optimal asset allocation and risk management strategies, among others.

A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of West Texas Intermediate Oil Prices and the DOW JONES Index

The paper features an examination of the link between the behaviour of oil prices and DowJones Index in a nonlinear autoregressive distributed lag nonlinear autoregressive distributed lag (NARDL) framework. The attraction of NARDL is that it represents the simplest method available of modelling combined short- and long-run asymmetries. The bounds testing framework adopted means that it can be applied to stationary and non-stationary time series vectors, or combinations of both. The data comprise a monthly West Texas Intermediate (WTI) crude oil series from Federal Reserve Bank of St Louis (FRED), commencing in January 2000 and terminating in February 2019, and a corresponding monthly DOW JONES index adjusted-price series obtained from Yahoo Finance. Both series are adjusted for monthly USA CPI values to create real series. The results of the analysis suggest that movements in the lagged real levels of monthly WTI crude oil prices have very significant effects on the behaviour of the DOW JONES Index. They also suggest that negative movements have larger impacts than positive movements in WTI prices, and that long-term multiplier effects take about 9 to 12 months to take effect.

A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of West Texas Intermediate Oil Prices and the DOW JONES Index

The paper features an examination of the link between the behaviour of oil prices and DowJones Index in a nonlinear autoregressive distributed lag NARDL framework. The attraction of NARDL is that it represents the simplest method available of modelling combined short- and long-run asymmetries. The bounds testing framework adopted means that it can be applied to stationary and non-stationary time series vectors, or combinations of both. The data comprise a monthly West Texas Intermediate (WTI) crude oil series from FRED, commencing in January 2000 and terminating in February 2019, and a corresponding monthly DOW JONES index adjusted-price series obtained from Yahoo Finance. Both series are adjusted for monthly USA CPI values to create real series. The results of the analysis suggest that movements in the lagged real levels of monthly WTI crude oil prices have very significant effects on the behaviour of the DOW JONES Index. They also suggest that negative movements have larger impacts than positive movements in WTI prices, and that long-term multiplier effects take about 9 to 12 months to take effect.

Detecting Co‐Movements in Non‐Causal Time Series

AbstractThis paper introduces the notion of common non‐causal features and proposes tools to detect them in multivariate time series models. We argue that the existence of co‐movements might not be detected using the conventional stationary vector autoregressive (VAR) model as the common dynamics are present in the non‐causal (i.e. forward‐looking) component of the series. We show that the presence of a reduced rank structure allows to identify purely causal and non‐causal VAR processes of order P>1 even in the Gaussian likelihood framework. Hence, usual test statistics and canonical correlation analysis can be applied, where either lags or leads are used as instruments to determine whether the common features are present in either the backward‐ or forward‐looking dynamics of the series. The proposed definitions of co‐movements are also valid for the mixed causal—non‐causal VAR, with the exception that a non‐Gaussian maximum likelihood estimator is necessary. This means however that one loses the benefits of the simple tools proposed. An empirical analysis on Brent and West Texas Intermediate oil prices illustrates the findings. No short run co‐movements are found in a conventional causal VAR, but they are detected when considering a purely non‐causal VAR.