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.

scholarly journals Revisiting the Integration of China Into the World Crude Oil Market: The Role of Structural Breaks

2019 ◽  
Vol 7 ◽  
Author(s):  
Zhenhua Liu ◽  
Zhihua Ding ◽  
Pengxiang Zhai ◽  
Tao Lv ◽  
Jy S. Wu ◽  
...  
Keyword(s):  
Crude Oil ◽  
Structural Breaks ◽  
Oil Market ◽  
The World

scholarly journals Contributions of Crude Oil Exchange Traded Funds in Price Discovery Process

2020 ◽  
Vol 23 (2) ◽  
pp. 393-407
Author(s):  
Keshab Shrestha ◽  
Sheena Philip ◽  
Yessy Peranginangin
Keyword(s):  
Crude Oil ◽  
Price Discovery ◽  
Exchange Traded Funds ◽  
Discovery Process ◽  
Information Share ◽  
Oil Futures ◽  
Price Series ◽  
Daily Data ◽  
Crude Oil Futures ◽  
Price Discovery Process

This study empirically investigates the contributions of three crude oil-based exchange-traded funds (ETFs) in the price discovery process. Using daily data on the crude oil spot, near month crude oil futures, and three crude-oil-based ETFs, we analyze the price discovery contributions of the five-price series. We use two information share measures, namely the generalized information share (GIS) measure (Lien and Shrestha, 2014) and the permanent-temporary decomposition (PT/GG) measure (Gonzalo and Granger, 1995). We find that the futures market dominates the price discovery process. However, we also find that the crude-oil-based ETFs significantly contribute to the price discovery process. Thus, we find that additional ETFs play a significant role in price discovery. Therefore, they are not redundant in terms of their price discovery contributions.

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

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.

scholarly journals Assessing Market Risk in BRICS and Oil Markets: An Application of Markov Switching and Vine Copula

2021 ◽  
Vol 9 (2) ◽  
pp. 30
Author(s):  
John Weirstrass Muteba Mwamba ◽  
Sutene Mwambetania Mwambi
Keyword(s):  
South Africa ◽  
Crude Oil ◽  
South African ◽  
Asset Allocation ◽  
Markov Switching ◽  
Tail Dependence ◽  
Lower Tail ◽  
Oil Market ◽  
Oil Markets ◽  
Vine Copula

This paper investigates the dynamic tail dependence risk between BRICS economies and the world energy market, in the context of the COVID-19 financial crisis of 2020, in order to determine optimal investment decisions based on risk metrics. For this purpose, we employ a combination of novel statistical techniques, including Vector Autoregressive (VAR), Markov-switching GJR-GARCH, and vine copula methods. Using a data set consisting of daily stock and world crude oil prices, we find evidence of a structure break in the volatility process, consisting of high and low persistence volatility processes, with a high persistence in the probabilities of transition between lower and higher volatility regimes, as well as the presence of leverage effects. Furthermore, our results based on the C-vine copula confirm the existence of two types of tail dependence: symmetric tail dependence between South Africa and China, South Africa and Russia, and South Africa and India, and asymmetric lower tail dependence between South Africa and Brazil, and South Africa and crude oil. For the purpose of diversification in these markets, we formulate an asset allocation problem using raw returns, MS GARCH returns, and C-vine and R-vine copula-based returns, and optimize it using a Particle Swarm optimization algorithm with a rebalancing strategy. The results demonstrate an inverse relationship between the risk contribution and asset allocation of South Africa and the crude oil market, supporting the existence of a lower tail dependence between them. This suggests that, when South African stocks are in distress, investors tend to shift their holdings in the oil market. Similar results are found between Russia and crude oil, as well as Brazil and crude oil. In the symmetric tail, South African asset allocation is found to have a well-diversified relationship with that of China, Russia, and India, suggesting that these three markets might be good investment destinations when things are not good in South Africa, and vice versa.

scholarly journals A Reappraisal of the Prebisch-Singer Hypothesis Using Wavelets Analysis

2021 ◽  
Vol 14 (7) ◽  
pp. 319
Author(s):  
Hany Fahmy
Keyword(s):  
International Trade ◽  
Structural Breaks ◽  
Developing Economies ◽  
Commodity Prices ◽  
Commodity Price ◽  
Data Set ◽  
Price Series ◽  
Long Run ◽  
Starting Point ◽  
The Impact

The Prebisch-Singer (PS) hypothesis, which postulates the presence of a downward secular trend in the price of primary commodities relative to manufacturers, remains at the core of a continuing debate among international trade economists. The reason is that the results of testing the PS hypothesis depend on the starting point of the technical analysis, i.e., stationarity, nonlinearity, and the existence of structural breaks. The objective of this paper is to appraise the PS hypothesis in the short- and long-run by employing a novel multiresolution wavelets decomposition to a unique data set of commodity prices. The paper also seeks to assess the impact of the terms of trade (also known as Incoterms) on the test results. The analysis reveals that the PS hypothesis is not supported in the long run for the aggregate commodity price index and for most of the individual commodity price series forming it. Furthermore, in addition to the starting point of the analysis, the results show that the PS test depends on the term of trade classification of commodity prices. These findings are of particular significance to international trade regulators and policymakers of developing economies that depend mainly on primary commodities in their exports.

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