Unit root modeling for trending stock market series

Salisu, Afees A.; Ndako, Umar B.; Oloko, Tirimisiyu F.; Akanni, Lateef O.

doi:10.1016/j.bir.2016.05.001

Cited by 30 publications

(8 citation statements)

References 58 publications

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“…Furthermore, we test the stationarity of the series, using both conventional Augmented Dickey Fuller (ADF) and the recently developed GARCH-based unit root test by Narayan and Liu (2015). The GARCH-based unit root is an extended version of ADF test that accounts for conditional heteroscedasticity and structural breaks in the series (Salisu et al , 2016). Using these two tests, the null hypothesis of unit root in the series can only be rejected after the series have been first differenced.…”

Section: Methodsmentioning

confidence: 99%

Crude oil price–shale oil production nexus: a predictability analysis

Olofin

Oloko

Isah

et al. 2020

IJESM

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Purpose The purpose of this study is to investigate the predictability of crude oil price and shale oil production, in a bid to examine the possibility of bi-directional causality. Design/methodology/approach The study adopts a recently developed predictability model by Westerlund and Narayan (2015), which accounts for persistence, endogeneity and heteroscedasticity. It also accounts for structural breaks in the predictive models. Findings The empirical results show that only a unidirectional causal relationship from crude oil price to shale oil production exists. This happens as crude oil price appears to be a good predictor of shale oil production; however, shale oil production does not serve as a good predictor for crude oil price. Accounting for structural break was found to improve the predictability and forecast accuracy of the predictive model. Our result is robust to choice of crude oil price benchmarks (West Texas Intermediate, Brent, Dubai Fateh and Refiners’ Acquisition Cost) and their denominations (real or nominal). Research limitations/implications The result implies that crude oil price must be considered when predicting shale oil production. Meanwhile, the non-significance of shale of production in crude oil price predictive model provides information to potential analyst, researchers and countries predicting crude oil price that failure to account for the effect of shale oil production would not have significant impact on the forecast accuracy of their models. Originality/value The study contributes originally to the literature on crude oil price–shale oil production in four major ways. First, it applies a recently developed predictability method by Westerlund and Narayan (2015), which is more suitable for dealing with persistence, conditional heteroscedasticity and endogeneity in the predictors. Second, it investigates existence of reverse causality between crude oil price and shale oil production. Third, it examines the variation in the response and effect of four major crude oil price benchmarks. Fourth, it considers crude oil price in both real and nominal terms.

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Section: Methodsmentioning

confidence: 99%

Crude oil price–shale oil production nexus: a predictability analysis

Olofin

Oloko

Isah

et al. 2020

IJESM

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“…For robustness, we extend the multivariate volatility analysis by testing and accounting for structural breaks, where such exist, to enhance the precision of the model. A good amount of available empirical literature suggested and demonstrated the importance of accounting for structural breaks alongside controlling for volatility while dealing with high frequency financial series (see, e.g., Narayan and Liu 2011 , 2015 ; Salisu and Adeleke 2016 ; Salisu et al 2016 ). The effects of ignoring structural shifts in the data have affected the optimal weights, OHR, and hedge effectiveness (see previous pieces of evidence in Babikir et al 2012 ; Mongi and Dhouha 2016 ).…”

Section: Robustness—accounting For Structural Breaksmentioning

confidence: 99%

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

Salisu

Obiora²

2021

Financ Innov

Self Cite

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This 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.

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“…This implies that, series must be free from unit root. Salisu et al [66] argued that for a series to be fit for statistical analysis, it must possess the stationarity properties, that is, constant mean and variance, because:…”

Section: Unit Root Testmentioning

confidence: 99%

Oil Price Volatility Spillover Effects on Food Prices in Nigeria

Oluwaseyi

2018

SSRN Journal

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Studies have been done on oil price volatility spillover effects on the prices of food in both pre-crisis and post-crisis periods. However, what has been sparingly studied is oil price volatility spillover effects on urban prices of food and rural prices of food. The disparity in the rural-urban spending in Nigeria is an area that can further be explored by evaluating the effects of oil price volatility spillover on prices of food in these areas. This study therefore adopts GARCH (1, 1)-TY model to evaluate the impulse response function and variance decomposition of these effects on prices of food in pre-crisis and post-crisis periods. Findings show that in full sample and postcrisis periods both aggregate price of food (APF) and urban average price of food (APFU) positively respond to oil price shocks while rural average price of food (APFR) responds negatively to oil price shocks. However, the response of the urban average price of food proves to be more significant in the post-crisis periods as it appears relatively most affected in this period by a greater percentage of oil price shocks.

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Unit root modeling for trending stock market series

Cited by 30 publications

References 58 publications

Crude oil price–shale oil production nexus: a predictability analysis

Crude oil price–shale oil production nexus: a predictability analysis

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

Oil Price Volatility Spillover Effects on Food Prices in Nigeria

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