scholarly journals Algorithmic Sangfroid? The Decline of Sensitivity of Crude Oil Prices to News on Potentially Disruptive Terror Attacks and Political Unrest

2020 ◽  
Vol 13 (1) ◽  
pp. 52
Author(s):  
Paweł Mielcarz ◽  
Dmytro Osiichuk ◽  
Jarosław Cymerski
Keyword(s):  
Crude Oil ◽  
Commodity Prices ◽  
Price Volatility ◽  
Oil Prices ◽  
Informational Efficiency ◽  
Terror Attacks ◽  
Oil Price Volatility ◽  
Crude Oil Prices ◽  
Political Unrest ◽  
Commodity Trading

The paper postulates that enhanced informational efficiency and signal processing capacity, which have characterized the evolution of commodity markets’ architecture during the last two decades, have rendered commodity prices more robust with respect to external shocks. Our econometric analysis of times series over 2001–2015 revealed a persistent decline in the responsiveness of crude oil prices to inflows of information concerning potentially supply-disruptive events. International news on terrorist attacks involving damage to oil infrastructure including those occurring in proximity to oil extraction sites, political unrest, and conflicts of rivaling factions are all documented to exercise a decreasing impact on oil price volatility both over short and medium observation spans. The previously observed spikes in oil prices accompanying similar disruptive events in OPEC countries are also shown to flatten over time as price sensitivity to information shocks declines. The discovered weakening of market response becomes more pronounced from the mid-2000s, which corresponds to the period of rapid algorithmization of commodity trading.

scholarly journals Modeling Asymmetric Volatility In Oil Prices

2011 ◽  
Vol 27 (3) ◽  
pp. 71 ◽  
Author(s):  
Syed Aun Hassan
Keyword(s):  
Crude Oil ◽  
Price Volatility ◽  
Oil Prices ◽  
Oil Price ◽  
Asymmetric Volatility ◽  
Us Economy ◽  
Oil Price Volatility ◽  
Crude Oil Prices ◽  
The Us ◽  
Asymmetric Garch

<p>Recent volatility in crude oil prices has affected economies around the world, especially the US economy, which is the largest consumer of oil. This paper focuses on how shocks to volatility of crude oil prices may affect future oil prices. The paper uses daily crude oil price data for the past 10 years to test and model the oil price volatility by fitting different variations of GARCH including a univariate asymmetric GARCH model to the series. Tests show high persistence and asymmetric behavior in oil price volatility, and reveal that negative and positive news have a different impact on oil price volatility. These results will help interested observers better understanding of the energy markets and has important consequences for the overall economy.</p>

Crude Oil Price and Food Security Related Variables in Malaysia

2016 ◽  
Vol 4 (2) ◽  
pp. 17-21
Author(s):  
Shri Dewi Applanaidu ◽  
Mukhriz Izraf Azman Aziz
Keyword(s):  
Food Security ◽  
Crude Oil ◽  
Price Index ◽  
Price Volatility ◽  
Food Price ◽  
Oil Price ◽  
Crude Oil Price ◽  
Price Changes ◽  
Oil Price Volatility ◽  
Crude Oil Prices

Objective - This study analyzes the dynamic relationship between crude oil price and food security related variables (crude palm oil price, exchange rate, food import, food price index, food production index, income per capita and government development expenditure) in Malaysia using a Vector Auto Regressive (VAR) model. Methodology/Technique - The data covered the period of 1980-2014. Impulse response functions (IRFs) was applied to examine what will be the results of crude oil price changes to the variables in the model. To explore the impact of variation in crude oil prices on the selected food security related variables forecast error variance decomposition (VDC) was employed. Findings - Findings from IRFs suggest there are positive effects of oil price changes on food import and food price index. The VDC analyses suggest that crude oil price changes have relatively largest impact on real crude palm oil price, food import and food price index. This study would suggest to revisiting the formulation of food price policy by including appropriate weight of crude oil price volatility. In terms of crude oil palm price determination, the volatility of crude oil prices should be taken into account. Overdependence on food imports also needs to be reduced. Novelty - As the largest response of crude oil price volatility on related food security variables food vouchers can be implemented. Food vouchers have advantages compared to direct cash transfers since it can be targeted and can be restricted to certain types of products and group of people. Hence, it can act as a better aid compared cash transfers. Type of Paper - Empirical Keywords: Crude oil price, Food security related variables, IRF, VAR, VDC

scholarly journals The Dynamic Relationship between Crude Oil Prices and Stock Market Price Volatility in Nigeria: A Cointegrated VAR-GARCH Model

2019 ◽  
pp. 1-12
Author(s):  
David Adugh Kuhe
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
Market Price ◽  
Garch Model ◽  
Price Volatility ◽  
Oil Prices ◽  
Market Prices ◽  
Dynamic Relationship ◽  
Crude Oil Prices ◽  
Long Run

This study investigates the dynamic relationship between crude oil prices and stock market price volatility in Nigeria using cointegrated Vector Generalized Autoregressive conditional Heteroskedasticity (VAR-GARCH) model. The study utilizes monthly data on the study variables from January 2006 to April 2017 and employs Dickey-Fuller Generalized least squares unit root test, simple linear regression model, unrestricted vector autoregressive model, Granger causality test and standard GARCH model as methods of analysis. Results shows that the study variables are integrated of order one, no long-run stable relationship was found to exist between crude oil prices and stock market prices in Nigeria. Both crude oil prices and stock market prices were found to have positive and significant impact on each other indicating that an increase in crude oil prices will increase stock market prices and vice versa. Both crude oil prices and stock market prices were found to have predictive information on one another in the long-run. A one-way causality ran from crude oil prices to stock market prices suggesting that crude oil prices determine stock prices and are a driven force in Nigerian stock market. Results of GARCH (1,1) models show high persistence of shocks in the conditional variance of both returns. The conditional volatility of stock market price log return was found to be stable and predictable while that of crude oil price log return was found to be unstable and unpredictable, although a dependable and dynamic relationship between crude oil prices and stock market prices was found to exist. The study provides some policy recommendations.

Do Fine Wines Blend with Crude Oil? Seizing the Transmission of Mean and Volatility Between Two Commodity Prices

2013 ◽  
Vol 8 (1) ◽  
pp. 49-68 ◽  
Author(s):  
Elie I. Bouri
Keyword(s):  
Financial Crisis ◽  
Crude Oil ◽  
Commodity Prices ◽  
Oil Prices ◽  
Multivariate Model ◽  
Volatility Transmission ◽  
Volatility Persistence ◽  
Crude Oil Prices ◽  
Risk Managers ◽  
Jel Classifications

AbstractThis study applies a multivariate model to examine the dynamics of mean and volatility transmission between fine wine and crude oil prices using daily observations from January 2004 to December 2011. The results suggest that the crude oil mean determines the wine market. In each series, volatility persistence is high and significant; innovations in each market seem to include figures that are valuable to risk managers seeking to predict volatility in other markets. During the financial crisis of 2008, wine and oil conditional volatilities climbed but then returned to their overall pre-crisis levels. (JEL Classifications: G11, G15, Q14, Q40)

VOLATILITY IN OIL PRICES AND MANUFACTURING ACTIVITY: AN INVESTIGATION OF REAL OPTIONS

2011 ◽  
Vol 15 (S3) ◽  
pp. 379-395 ◽  
Author(s):  
John Elder ◽  
Apostolos Serletis
Keyword(s):  
Real Options ◽  
Commodity Prices ◽  
Price Volatility ◽  
Oil Prices ◽  
Capital Budgeting ◽  
Oil Price ◽  
Real Options Theory ◽  
Oil Price Volatility ◽  
Manufacturing Activity ◽  
General Specification

Previous research shows that volatility in oil prices has tended to depress output, as measured by nonresidential investment and GDP. This is interpreted as evidence in support of the theory of real options in capital budgeting decisions, which predicts that uncertainty about, for example, commodity prices will cause firms to delay production and investment. We continue that investigation by analyzing the effect of oil price uncertainty on monthly measures of U.S. firm production related to industries in mining, manufacturing, and utilities. We use a more general specification, an updated sample that includes the increased oil price volatility since 2008, and we control for other nonlinear measures of oil prices. We find additional empirical evidence in support of the predictions of real options theory, and our results indicate that the extreme volatility in oil prices observed in 2008 and 2009 contributed to the severity of the decline in manufacturing activity.

scholarly journals GLOBAL CRUDE OIL PRICE AND DOMESTIC FOOD COMMODITY PRICES: EVIDENCE FROM INDONESIA

2020 ◽  
Vol 14 (1) ◽  
pp. 95-120
Author(s):  
Tiara Kencana Ayu
Keyword(s):  
Panel Data ◽  
Crude Oil ◽  
Local Food ◽  
Price Change ◽  
Commodity Prices ◽  
Oil Prices ◽  
Jel Classification ◽  
Crude Oil Prices ◽  
The Impact ◽  
Food Commodity

Abstrak Penelitian untuk menganalisis hubungan antara harga minyak dunia dan harga komoditi pangan di pasar domestik masih jarang ditemukan. Dengan membuat Model Panel Data dari 34 provinsi di Indonesia pada tahun 2010-2017, penelitian ini bertujuan untuk menginvestigasi pengaruh perubahan harga minyak dunia terhadap beberapa harga komoditi pangan lokal (kedelai,import, kedelai lokal, beras lokal, dan jagung lokal). Hasil penelitian ini mengindikasikan bahwa harga minyak dunia dapat memengaruhi harga pangan lokal di Indonesia melalui tingginya biaya pengiriman pada aktivitas impor. Selain itu, harga komoditi pangan dunia juga terbukti dapat memengaruhi harga seluruh komoditi pangan lokal yang diteliti, yang mengimplikasikan bahwa harga komoditi pangan di Indonesia dipengaruhi oleh kondisi pasar internasional. Hasil penelitian ini memberikan masukan bagi pembuat kebijakan di Indonesia untuk mempertimbangkan perubahan harga minyak dunia dan harga komoditi global dalam menstabilkan harga komoditi lokal di Indonesia, terutama komoditi yang diimpor.   Abstract Globally, studies examining the nexus between global crude oil prices and food commodity prices in domestic markets are scant. Employing a panel data model of 34 provinces in Indonesia from 2010 - 2017, this study investigates the impact of global crude oil’s price change on some local food commodity prices (imported soybean, local soybean, local rice, and local maize). Previous studies found that local food commodity prices in some countries were not affected by global crude oil prices; however, this study, by controlling other factors which could affect local commodity prices, finds different results. This study’s findings indicate that global crude oil prices could affect Indonesia’s local commodity prices due to higher shipping costs in import activity. In addition, global commodity prices are also proved to affect all commodities examined in this study, which implies that local food commodity prices in Indonesia are influenced by the international market. This study provides input to policymakers in Indonesia to consider the movement of global crude oil prices and global commodity prices in stabilizing local food commodity prices in Indonesia, especially the imported commodities. JEL Classification: F15, O13, Q11

Modelling Crude Oil Prices Volatility in India

2019 ◽  
Vol 4 (2) ◽  
Author(s):  
Khyati Kathuria ◽  
Shikha Gupta ◽  
Nand Kumar
Keyword(s):  
Crude Oil ◽  
Linear Time ◽  
Price Volatility ◽  
Oil Prices ◽  
Oil Price ◽  
Crude Oil Price ◽  
Volatility Clustering ◽  
Price Series ◽  
Crude Oil Prices ◽  
Increasing Demand

Crude oil is a crucial component of India’s energy basket after coal. The increasing demand for crude oil in India is met through imports. Crude oil price changes affect the social stability, economic development, and national security of the country. Therefore, it is crucial to devise suitable methods to forecast crude oil price movements accurately.Thus, the purpose of this study is to evaluate the forecasting performance of linear and non-linear time series models. In the study Box Jenkins methodology is used to obtain a best fit ARIMA and GARCH type models and further use it to forecast the crude oil (Brent) prices. The study shows that the crude oil price series is volatile over the time trend and therefore uses the GARCH class models as well which are capable of capturing volatility clustering typical of oil price series. Performance of ARIMA & GARCH class modes is then compared to find out which model better forecasts the crude oil prices. Indian economy being vulnerable to volatility in the international crude oil market requires a methodology to accurately forecast the price volatility and therefore to fill this gap this study for forecasting and studying the behavior of crude oil price series was conducted.

scholarly journals The Relation between Implied Volatility Index and Crude Oil Prices

2019 ◽  
Vol 30 (5) ◽  
pp. 556-566
Author(s):  
Imlak Shaikh
Keyword(s):  
Neural Network ◽  
Quantile Regression ◽  
Crude Oil ◽  
Implied Volatility ◽  
Price Volatility ◽  
Oil Prices ◽  
Oil Futures ◽  
Volatility Index ◽  
Crude Oil Prices ◽  
Volatility Feedback

Crude oil is a global commodity traded across the world market. The prices of the commodity over an extended period for crude oil have been analyzed using daily prices of crude oil futures and the implied volatility index (OVX). This paper aims to find the predictability of various parameters on the basis of time using neural network and quantile regression methods. Several estimates have been shown based on Barone, Adesi, and Whaley’s (BAW) model of neural network. Estimation parameters include opening, closing, highest and lowest price of the commodity and volumes traded for a given commodity on each trading day. The neural network estimates explain that future prices of the WTI/USO can be predicted with minimal error, and similar can be used to predict future volatility. The quantile regression results suggest that crude oil prices and OVX are strongly associated. The asymmetric association between the WTI/USO and OVX explains that the volatility feedback effect holds good for the OVX market. Bai and Perron least squares estimate evidence of the presence of a break in the time series. The main results uncover several interesting facts that implied volatility tends to remain calm during the global financial crises and higher throughout the post crisis period. The empirical outcome on the OVX market provides some practical implications for the trader and investor, in which oil futures can serve better to hedge the crude price volatility. The crude oil producer can short hedge enough through volatility futures and options to maintain the future quantity of crude to be produced.

scholarly journals Modeling Fluctuation of the Price of Crude Oil in Nigeria Using ARCH, ARCH-M Models

2020 ◽  
pp. 16-40
Author(s):  
Titus Eli Monday ◽  
Ahmed Abdulkadir
Keyword(s):  
Exchange Rate ◽  
Crude Oil ◽  
Foreign Exchange ◽  
Exchange Rate Volatility ◽  
Price Volatility ◽  
Oil Prices ◽  
Oil Price ◽  
Arch Model ◽  
Oil Price Volatility ◽  
Exchange Rate Movements

As a mono-product economy, where the main export commodity is crude oil, volatility in oil prices has implications for the Nigerian economy and, in particular, exchange rate movements. The latter is particularly important due to the twin dilemma of being an oil exporting and oil-importing country, a situation that emerged in the last decade. The study examined the effects of oil price volatility, demand for foreign exchange, and external reserves on exchange rate volatility in Nigeria using monthly data over the period from May, 1989 to April 2019. Drawing from the works of Atoi [1] Having realized the potentials of an Autoregressive conditional heteroskedasticity (ARCH) model several studies have use it in modeling financial series. However, when using the ARCH model in determining the optimal lag length of variables the processes are very cumbersome. Therefore, often time users encounter problems of over parameterization. Thus, Rydberg (2016) argued that since large lag values are required in ARCH model therefore there is the need for additional parameters. Sequel to that, this research uses the ARCH-M to solve the challenges. The study reaffirms the direct link of demand for foreign exchange and oil price volatility with exchange rate movements and, therefore, recommends that demand for foreign exchange should be closely monitored and exchange rate should move in tandem with the volatility in crude oil prices bearing in mind that Nigeria remains an oil-dependent economy.

Applications of Neural Networks in Modeling and Forecasting Volatility of Crude Oil Markets: Evidences from US and China

2011 ◽  
Vol 230-232 ◽  
pp. 953-957 ◽  
Author(s):  
Phich Hang Ou ◽  
Heng Shan Wang
Keyword(s):  
Crude Oil ◽  
Price Volatility ◽  
Oil Price ◽  
Parametric Models ◽  
Nonparametric Models ◽  
Oil Price Volatility ◽  
Crude Oil Prices ◽  
Oil Markets ◽  
Modeling And Forecasting ◽  
Forecasting Volatility

Previous researches on oil price volatility have been done with parametric models of GARCH types. In this work, we model volatility of crude oil price based on GARCH(p,q) by using Neural Network which is one of powerful classes of nonparametric models. The empirical analysis based on crude oil prices in US and China show that the proposed models significantly generate improved forecasting accuracy than the parametric model of normal GARCH(p,q). Among nine different combinations of hybrid models (for p = 1,2,3 and q = 1,2,3), it is found that NN-GARCH(1,1) and NN-GARCH(2,2) perform better than the others in US market whereas, NN-GARCH(1,1) and NN-GARCH(3,1) outperform in Chinese case.

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