scholarly journals Modeling the Relationship between Crude Oil and Agricultural Commodity Prices

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1344 ◽  
Author(s):  
Duc Hong Vo ◽  
Tan Ngoc Vu ◽  
Anh The Vo ◽  
Michael McAleer
Keyword(s):  
Crude Oil ◽  
Global Financial Crisis ◽  
Aggregate Demand ◽  
Commodity Prices ◽  
Impulse Response Functions ◽  
Agricultural Commodities ◽  
Agricultural Commodity ◽  
Vector Autoregressive ◽  
Crude Oil Prices ◽  
Oil Markets

The food-energy nexus has attracted great attention from policymakers, practitioners, and academia since the food price crisis during the 2007–2008 Global Financial Crisis (GFC), and new policies that aim to increase ethanol production. This paper incorporates aggregate demand and alternative oil shocks to investigate the causal relationship between agricultural products and oil markets. For the period January 2000–July 2018, monthly spot prices of 15 commodities are examined, including Brent crude oil, biofuel-related agricultural commodities, and other agricultural commodities. The sample is divided into three sub-periods, namely: (i) January 2000–July 2006, (ii) August 2006–April 2013, and (iii) May 2013–July 2018. The structural vector autoregressive (SVAR) model, impulse response functions, and variance decomposition technique are used to examine how the shocks to agricultural markets contribute to the variance of crude oil prices. The empirical findings from the paper indicate that not every oil shock contributes the same to agricultural price fluctuations, and similarly for the effects of aggregate demand shocks on the agricultural market. These results show that the crude oil market plays a major role in explaining fluctuations in the prices and associated volatility of agricultural commodities.

scholarly journals Dynamic Interrelationship and Volatility Spillover among Sustainability Stock Markets, Major European Conventional Indices, and International Crude Oil

2020 ◽  
Vol 12 (9) ◽  
pp. 3908 ◽  
Author(s):  
Basel Maraqa ◽  
Murad Bein
Keyword(s):  
Crude Oil ◽  
Stock Returns ◽  
Global Financial Crisis ◽  
Oil Prices ◽  
Volatility Spillover ◽  
Socially Responsible ◽  
Crude Oil Prices ◽  
Daily Data ◽  
Oil Markets ◽  
The Global Financial Crisis

This study examines the dynamic interrelationship and volatility spillover among stainability stock indices (SSIs), international crude oil prices and major stock returns of European oil-importing countries (UK, Germany, France, Italy, Switzerland and The Netherlands) and oil-exporting countries (Norway and Russia). We employ the DCC-MGARCH model and use daily data for the sample period from 28 September 2001 to 10 January 2020. We find that the dynamic interrelationship between SSIs, stock returns of European oil importing/exporting countries and oil markets is different. There is higher correlation between SSIs and oil-importing countries, while oil-exporting countries have higher correlation with the oil market. Notably, the correlation between oil and stock returns became higher during and after the global financial crisis. This study also reveals the existence of significant volatility spillover between sustainability stock returns, international oil prices and the major indices of oil importing/exporting countries. These results have important implications for investors who are seeking to hedge and diversify their assets and for socially responsible investors.

An Empirical Investigation of Factors Affecting Stock Prices in Vietnam

2014 ◽  
pp. 74-89 ◽  
Author(s):  
Vinh Vo Xuan
Keyword(s):  
Exchange Rates ◽  
Crude Oil ◽  
Foreign Exchange ◽  
Stock Prices ◽  
Global Financial Crisis ◽  
Oil Prices ◽  
Foreign Exchange Rates ◽  
Factors Affecting ◽  
Crude Oil Prices ◽  
Before And After

This paper investigates factors affecting Vietnam’s stock prices including US stock prices, foreign exchange rates, gold prices and crude oil prices. Using the daily data from 2005 to 2012, the results indicate that Vietnam’s stock prices are influenced by crude oil prices. In addition, Vietnam’s stock prices are also affected significantly by US stock prices, and foreign exchange rates over the period before the 2008 Global Financial Crisis. There is evidence that Vietnam’s stock prices are highly correlated with US stock prices, foreign exchange rates and gold prices for the same period. Furthermore, Vietnam’s stock prices were cointegrated with US stock prices both before and after the crisis, and with foreign exchange rates, gold prices and crude oil prices only during and after the crisis.

Prediction of Future Market Price for Agricultural Commodities

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Sagar Pathane ◽  
Uttam Patil ◽  
Nandini Sidnal
Keyword(s):  
Mobile Application ◽  
Market Price ◽  
Commodity Prices ◽  
Future Price ◽  
Future Market ◽  
Graphical Interface ◽  
Agricultural Commodities ◽  
Agricultural Commodity ◽  
Web Based ◽  
Agriculture Products

The agricultural commodity prices have a volatile nature which may increase or decrease inconsistently causing an adverse effect on the economy. The work carried out here for predicting prices of agricultural commodities is useful for the farmers because of which they can sow appropriate crop depending on its future price. Agriculture products have seasonal rates, these rates are spread over the entire year. If these rates are known/alerted to the farmers in advance, then it will be promising on ROI (Return on Investments). It requires that the rates of the agricultural products updated into the dataset of each state and each crop, in this application five crops are considered. The predictions are done based on neural networks Neuroph framework in java platform and also the previous years data. The results are produced on mobile application using android. Web based interface is also provided for displaying processed commodity rates in graphical interface. Agricultural experts can follow these graphs and predict market rates which can be informed to the farmers. The results will be provided based on the location of the users of this application.

Multivariate Markov-switching score-driven models: an application to the global crude oil market

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Szabolcs Blazsek ◽  
Alvaro Escribano ◽  
Adrian Licht
Keyword(s):  
Crude Oil ◽  
Moving Average ◽  
Local Level ◽  
Markov Switching ◽  
Impulse Response Functions ◽  
Common Trends ◽  
Vector Autoregressive ◽  
Oil Market ◽  
Long Run ◽  
Special Cases

Abstract A new class of multivariate nonlinear quasi-vector autoregressive (QVAR) models is introduced. It is a Markov switching score-driven model with stochastic seasonality for the multivariate t-distribution (MS-Seasonal-t-QVAR). As an extension, we allow for the possibility of having common-trends and nonlinear co-integration. Score-driven nonlinear updates of local level and seasonality are used, which are robust to outliers within each regime. We show that VAR integrated moving average (VARIMA) type filters are special cases of QVAR filters. Using exclusion, sign, and elasticity identification restrictions in MS-Seasonal-t-QVAR with common-trends, we provide short-run and long-run impulse response functions for the global crude oil market.

Price and volatility transmission in, and between, skimmed milk powder, livestock feed and oil markets

2017 ◽  
Vol 46 (4) ◽  
pp. 248-257 ◽  
Author(s):  
Dennis Bergmann ◽  
Declan O’Connor ◽  
Andreas Thümmel
Keyword(s):  
Crude Oil ◽  
Milk Powder ◽  
Multivariate Garch ◽  
Volatility Transmission ◽  
Crude Oil Prices ◽  
Multivariate Garch Model ◽  
Oil Markets ◽  
Skimmed Milk ◽  
Derivative Markets ◽  
Skimmed Milk Powder

Price and volatility transmission effects between European Union (EU) and World skimmed milk powder (SMP) prices, as well as those between both SMP series, soybeans and crude oil prices from 2004 to 2014 were analysed using a vector error correction model combined with a multivariate GARCH model. The results show significant transmission effects between EU and World SMP prices, but no significant transmission effects from soybeans or crude oil to either of the SMP prices. For policymakers and modellers, these results indicate the need to consider World SMP prices when considering EU prices. On the other hand, the finding of no transmission effects from soybean to SMP prices reduces the opportunity for a successful cross-hedging for dairy commodities using well-established soybean derivative markets.

FORECASTING OF OIL AND AGRICULTURAL COMMODITY PRICES: VARMA VERSUS ARMA

2017 ◽  
Vol 12 (03) ◽  
pp. 1750012 ◽  
Author(s):  
MUSTAFA GÜLERCE ◽  
GAZANFER ÜNAL
Keyword(s):  
Moving Average ◽  
Commodity Prices ◽  
Wavelet Coherence ◽  
Autoregressive Moving Average ◽  
Multiple Time ◽  
Energy Prices ◽  
Agricultural Commodity ◽  
Vector Autoregressive ◽  
Dynamic Correlations ◽  
Model Free

The aim of this paper is to show that the estimates made with vector autoregressive–moving-average (ARMA) models based on the coherent time intervals of the multiple time series give more precise results than the univariate case. The previous literature on dynamic correlations (co-movement) in between food and energy prices has mixed results and mainly based on parametric approaches. Therefore, partial wavelet coherence (PWC) and multiple wavelet coherence (MWC) methods are used, respectively, to uncover the coherency simultaneously for time and frequency domains. In our study; world oil, corn, soybeans, wheat and sugar prices are examined instead of the return and volatility relationship between oil and agricultural commodities due to model-free approach of wavelet analysis.

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)

Hedging the risk of travel and leisure stocks: The role of crude oil

2020 ◽  
pp. 135481662092262
Author(s):  
Naji Jalkh ◽  
Elie Bouri ◽  
Xuan Vinh Vo ◽  
Anupam Dutta
Keyword(s):  
Crude Oil ◽  
Implied Volatility ◽  
Stock Index ◽  
Model Specification ◽  
The United Kingdom ◽  
Crude Oil Prices ◽  
Oil Markets ◽  
Hedge Ratios ◽  
Correlation Process

Unlike previous studies, we examine which of the implied volatilities of US stock and crude oil markets are more suitable and effective hedge for the downside risk of US travel and leisure (T&L) stocks. Using the corrected dynamic conditional correlation process, the results show that the T&L stock index is more negatively and more consistently correlated with the implied volatility of crude oil prices, suggesting that the oil implied volatility is a more suitable hedging asset. Similar results are reported for France, the United Kingdom, and developed markets. They are robust to the frequency of the data and model specification. Furthermore, the hedge ratios vary over time, which requires a regular update of hedged positions. Importantly, the highest hedge effectiveness is associated with the oil implied volatility.

Do global oil price shocks affect Indian metal market?

2018 ◽  
Vol 29 (6) ◽  
pp. 891-904 ◽  
Author(s):  
Nikhil Kaushik
Keyword(s):  
Crude Oil ◽  
Global Financial Crisis ◽  
Precious Metals ◽  
Oil Prices ◽  
Future Market ◽  
Copper And Zinc ◽  
Crude Oil Prices ◽  
Aluminium Copper ◽  
Metal Market ◽  
Industrial Metals

The main objective of this paper is to investigate the spillover effect of global crude oil prices on Indian metal market using dynamic conditional correlation generalized autoregressive conditional heteroskedasticity models. The study considers Indian metal market, Multi Commodity Exchange of India Limited METAL index and two precious metals gold and silver, and three industrial metals aluminium, copper and zinc over the period from 1 June 2006 to 31 March 2017. The results of the study show moderate co-movement between West Texas Intermediate (WTI) crude oil and Indian metal market. Precious metals gold and silver do not show either upward nor downward trend even in global financial crisis 2008–2009 while industrial metals aluminium, copper and zinc are weakly correlated to crude oil prices. In addition, it is found that global crude oil prices have short-term as well as long-term memory effect on Indian metal market and metal prices. The study presents the case for diverse stakeholders to improve strategic oil reserves for stabilizing oil prices during global turmoil. Also, policy makers and practitioners may draw meaningful conclusions from findings of the present study to improve future market for stabilizing spot prices of metals while operating in Indian metal markets.

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