Forecasting excess stock returns with crude oil market data

2015 ◽  
Vol 48 ◽  
pp. 316-324 ◽  
Author(s):  
Li Liu ◽  
Feng Ma ◽  
Yudong Wang
Keyword(s):  
Crude Oil ◽  
Stock Returns ◽  
Market Data ◽  
Oil Market ◽  
Excess Stock Returns

scholarly journals How is COVID-19 Related to the Global Economy?

2020 ◽  
Vol 03 (12) ◽  
Author(s):  
Abdulelah Almuqrin ◽  
Keyword(s):  
Crude Oil ◽  
Stock Returns ◽  
Industrial Production ◽  
Forecast Error ◽  
Production Index ◽  
Oil Market ◽  
Industrial Production Index ◽  
Gold Market ◽  
Short Run ◽  
Global Stock

Using a structural vector autoregressive model (SVAR) model, this paper attempts to investigate the short-run interrelation between COVID-19 cases, the gold market, the oil market, the global stock market, and the real economy. We find that COVID-19 cases respond predominantly to its own innovations while the crude oil market, the gold market, and global stock returns are interrelated in the short-run. The industrial production index, the price of crude oil, and the price of gold account for approximately 22.64%, 9.5%, and 13.74% of the variations in global stock returns, respectively. Global COVID-19 cases can explain about 6.022% of the variance of the U.S. industrial production index of the 30-day ahead forecast error.

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.

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 Short-Term Exuberance and Long-Term Stability: A Simultaneous Optimization of Stock Return Predictions for Short and Long Horizons

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 620
Author(s):  
Ioannis Kyriakou ◽  
Parastoo Mousavi ◽  
Jens Perch Nielsen ◽  
Michael Scholz
Keyword(s):  
Stock Returns ◽  
Simultaneous Optimization ◽  
Investment Risk ◽  
Short Term ◽  
Fundamental Interest ◽  
Long Term Stability ◽  
Risk Appetite ◽  
Short And Long Term ◽  
Excess Stock Returns

The fundamental interest of investors in econometric modeling for excess stock returns usually focuses either on short- or long-term predictions to individually reduce the investment risk. In this paper, we present a new and simple model that contemporaneously accounts for short- and long-term predictions. By combining the different horizons, we exploit the lower long-term variance to further reduce the short-term variance, which is susceptible to speculative exuberance. As a consequence, the long-term pension-saver avoids an over-conservative portfolio with implied potential upside reductions given their optimal risk appetite. Different combinations of short and long horizons as well as definitions of excess returns, for example, concerning the traditional short-term interest rate but also the inflation, are easily accommodated in our model.

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