Oil price shocks and volatility spillovers in the Nigerian sovereign bond market

Tule, Moses K.; Ndako, Umar B.; Onipede, Samuel F.

doi:10.1016/j.rfe.2017.03.003

Cited by 36 publications

(28 citation statements)

References 59 publications

(92 reference statements)

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“…Step 3. Calculate the sum scores including negative sum , neutral sum , and positive sum by using Equation (2).…”

Section: Variable Name Remark Compound Tmentioning

confidence: 99%

“…As a strategic energy source, price fluctuations will have an important impact on economic growth, bond markets, and national security, so the prediction of oil prices has been receiving much attention [1,2]. However, oil prices are not only affected by the fundamental factors of supply and demand, but also by non-fundamental factors such as geopolitics, big country games and market speculation.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Oil Price Using Web-based Sentiment Analysis

et al. 2019

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International oil price forecasting is a complex and important issue in the research area of energy economy. In this paper, a new model based on web-based sentiment analysis is proposed. For the oil market, sentiment analysis is used to extract key information from web texts from the four perspectives of: compound, negative, neutral, and positive sentiment. These are constructed as feature and input into oil price forecasting models with oil price itself. Finally, we analyze the effect in various views and get some interesting discoveries. The results show that the root mean squared error can be reduced by about 0.2 and the error variance by 0.2, which means that the accuracy and stability are thereby improved. Furthermore, we find that different types of sentiments can all improve performance but by similar amounts. Last but not least, text with strong intensity can better support oil price forecasting than weaker text, for which the root mean squared error can be reduced by up to 0.5, and the number of the bad cases is reduced by 20%, indicating that text with strong intensity can correct the original oil price forecast. We believe that our research will play a strong supporting role in future research on using web information for oil price forecasting.

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“…Step 3. Calculate the sum scores including negative sum , neutral sum , and positive sum by using Equation (2).…”

Section: Variable Name Remark Compound Tmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting Oil Price Using Web-based Sentiment Analysis

et al. 2019

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“…In the GARCH-type model, the conditional variance relies on the estimates of conditional mean process. Unlike Kang et al (2011) considering structural changes in the conditional variance estimates, in a recent study, Tule et al (2017) endogenously and sequentially detect structural breaks in return series with Bai and Perron (2003) test and modify the VARMA-AGARCH model by incorporating break points into conditional mean equation, to investigate information transmission between world oil markets and the sovereign bond market of Nigerian. Their results indicate that volatility spillover between markets is sensitive to structural breaks.…”

Section: Intraday Periodicitymentioning

confidence: 99%

“…Ignoring structural breaks in the financial time series may lead to bias in the estimation of the GARCH model due to overestimates of volatility persistence (Lamoureux and Lastrapes, 1990;Mikosch and Stȃricȃ, 2004;Charles and Darné, 2014;Tule et al, 2017). Several methods are available to detect structural breakpoints in financial time series, such as the Chow test (Chow, 1960), the cumulative sum (CUSUM) test (Brown et al, 1975), and the Bai and Perron (2003) test.…”

Section: Structural Break Testmentioning

confidence: 99%

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

et al. 2019

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The China's crude oil futures market (INE market), as it was first launched in late March of 2018, quickly draws much attention from global investors. In reference to the high frequency data, this research explores how well this new product reacts efficiently to international influences and to what extent it can be integrated with traditional benchmarks, such as WTI and Brent. The multivariate GARCH models are employed to capture the cross-market time-varying correlations, return and volatility spillovers, which are modified by incorporating the detected structural breaks in the return dynamics to improve the accuracy of model estimates. Empirical results indicate a strong integration of INE market with these international benchmarks. A high but time-varying correlation is observed with recurring highs around 0.7. Spillover effects have included significant bidirectional return and volatility spillovers between the INE and the international benchmark markets. Secondly, INE market appears to interact better with the Brent market than with the WTI market. Thirdly, structural breaks can influence correlations, the portfolio weights and hedge ratios. Lastly, the correlation between crude oil futures markets decreases significantly during the periods when structural breaks caused by economic and/or geopolitical events are identified. These findings have important implications in policy makings and economic decisions on portfolio management and hedging strategies.

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Crude oil market and Nigerian stocks: An asymmetric information spillover approach

Okorie

Lin

2020

Int. J Fin Econ

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This article examines the nature of information spillovers, in return and volatility, that exists between the full index stock of the Nigerian stock exchange (NSE) and the crude oil markets (Brent and West Texas Intermediate (WTI) oil markets). We adopted the asymmetric VAR − MGARCH − GJR − BEKK model and found evidence of unidirectional return spillover from the Brent oil market to the NSE and a bidirectional return spillover between the NSE market and the WTI oil market. The response of the WTI market to a shock on the NSE market is rather short-lived. There exists strong evidence of a lead-lag relationship between the crude oil market and the NSE market wherein the crude oil market leads or drives the NSE market return fluctuations. For both crude oil markets, the Brent and the WTI markets, there exists a bidirectional volatility spillover between the NSE index and the crude oil markets, as well as significant asymmetric shocks. Practical policy implications and recommendations were made, based on the findings.

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Oil price shocks and volatility spillovers in the Nigerian sovereign bond market

Cited by 36 publications

References 59 publications

Forecasting Oil Price Using Web-based Sentiment Analysis

Forecasting Oil Price Using Web-based Sentiment Analysis

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

Crude oil market and Nigerian stocks: An asymmetric information spillover approach

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