Global crude oil market shocks and global commodity prices

2018 ◽  
Vol 43 (1) ◽  
pp. 92-105 ◽  
Author(s):  
Mark Melichar ◽  
Bebonchu Atems
Keyword(s):  
Crude Oil ◽  
Commodity Prices ◽  
Oil Market ◽  
Market Shocks

DO GLOBAL CRUDE OIL MARKET SHOCKS HAVE DIFFERENTIAL EFFECTS ON US REGIONS?

2017 ◽  
Vol 23 (5) ◽  
pp. 1978-2008 ◽  
Author(s):  
Bebonchu Atems ◽  
Mark Melichar
Keyword(s):  
Crude Oil ◽  
Aggregate Demand ◽  
Personal Income ◽  
Oil Shocks ◽  
Demand And Supply ◽  
Demand Shocks ◽  
Industrial Composition ◽  
Oil Market ◽  
Supply Shocks ◽  
Market Shocks

The paper investigates whether US regions respond differently to shocks in the crude oil market. We disentangle oil market shocks into distinct demand and supply shocks and examine the response of regional personal income to these shocks. Results indicate that for most regions, oil supply shocks decrease real personal income. Except for the Rocky Mountains and the Southwest, global aggregate demand shocks are recessionary, typically about a year and a half after the shock. When we split our data into oil-producing and non-oil-producing regions, we find that global aggregate demand shocks have no effect on oil-producing regions but cause a decrease in income in non-oil-producing regions. Our analysis further indicates that oil-specific demand shocks have positive and persistent impacts on oil-producing regions but are recessionary in non-oil-producing regions. We also document significant asymmetries in the regional responses to small versus large oil shocks. In addition, the paper shows that regional differences in industrial composition explain some of the variation in the responses of real regional personal income to oil shocks.

Fundamentals and Commodity Prices

2018 ◽  
Author(s):  
Michael S. Haigh
Keyword(s):  
Principal Component Analysis ◽  
Crude Oil ◽  
Real Time ◽  
Supply And Demand ◽  
Commodity Prices ◽  
Principal Component ◽  
Component Analysis ◽  
Commodity Markets ◽  
Economic Crises ◽  
Oil Market

Commodity markets occasionally co-move with the broader macro markets for reasons beyond their own fundamentally driven physical characteristics. This chapter focuses on two related avenues to look beyond the fundamentals of counting barrels, tonnes, bushels, or molecules. The first section uses a principal component analysis to disentangle how fundamentals versus non-fundamentals drive commodity prices and focuses on the crude oil market. The results are intuitive and allow isolating the extent to which supply and demand matter to price changes experienced in the market. Furthermore, the results enable understanding whether the diversification benefits of commodity markets exist in almost real time. Second, given the ability to segment fundamentally driven commodities from others, the chapter focuses on how much supply or demand factors attribute to the fundamental variation in prices. The analysis reveals that, in the oil market, supply concerns drive prices during geopolitical tensions, while demand concerns dominate during economic crises.

Contango in Cushing? - Evidence on Financial-Physical Interactions in the U.S. Crude Oil Market

2012 ◽  
Author(s):  
Louis H. Ederington ◽  
Chitru S. Fernando ◽  
Kateryna V. Holland ◽  
Thomas K. Lee
Keyword(s):  
Crude Oil ◽  
Oil Market ◽  
Physical Interactions ◽  
The U.S

scholarly journals COVID-19 pandemic and the crude oil market risk: hedging options with non-energy financial innovations

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Afees A. Salisu ◽  
Kingsley Obiora
Keyword(s):  
Crude Oil ◽  
Structural Breaks ◽  
Exchange Traded Funds ◽  
Modeling Framework ◽  
Risk Hedging ◽  
Financial Innovations ◽  
Oil Market ◽  
Price Series ◽  
Hedging Effectiveness ◽  
Multivariate Volatility

AbstractThis study examines the hedging effectiveness of financial innovations against crude oil investment risks, both before and during the COVID-19 pandemic. We focus on the non-energy exchange traded funds (ETFs) as proxies for financial innovations given the potential positive correlation between energy variants and crude oil proxies. We employ a multivariate volatility modeling framework that accounts for important statistical features of the non-energy ETFs and oil price series in the computation of optimal weights and optimal hedging ratios. Results show evidence of hedging effectiveness for the financial innovations against oil market risks, with higher hedging performance observed during the pandemic. Overall, we show that sectoral financial innovations provide resilient investment options. Therefore, we propose that including the ETFs in an investment portfolio containing oil could improve risk-adjusted returns, especially in similar financial crisis as witnessed during the pandemic. In essence, our results are useful for investors in the global oil market seeking to maximize risk-adjusted returns when making investment decisions. Moreover, by exploring the role of structural breaks in the multivariate volatility framework, our attempts at establishing robustness for the results reveal that ignoring the same may lead to wrong conclusions about the hedging effectiveness.

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.

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