Interactions between oil and financial markets — Do conditions of financial stress matter?

Wan, Jer-Yuh; Kao, Chung-Wei

doi:10.1016/j.eneco.2015.10.003

Cited by 53 publications

(27 citation statements)

References 49 publications

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“…Finally, based on volatility tests, they found uni-directional volatility transmission from energy markets to the high-yield bond market. In the context of investment bonds, Wan and Kao (2015) found that positive shocks in oil the spreads between the AAA and BAA rated bonds.…”

Section: Introductionmentioning

confidence: 99%

Oil price uncertainty and movements in the US government bond risk premia

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Gupta

Wang

et al. 2020

The North American Journal of Economics and Finance

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In this paper, we analyze the predictability of the movements of bond premia of US Treasury due to oil price uncertainty over the monthly period 1953:06 to 2016:12. For our purpose, we use a higher order nonparametric causality-in-quantiles framework, which in turn, allows us to test for predictability over the entire conditional distribution of not only bond returns, but also its volatility, by controlling for misspecification due to uncaptured nonlinearity and structural breaks, which we show to exist in our data. We find that oil uncertainty not only predicts (increases) US bond returns, but also its volatility, with the effect on the latter being stronger. In addition, oil uncertainty tends to have a stronger impact on the shortest and longest maturities (2-and 5-year), and relatively weaker impact on bonds with medium-term (3-and 4-year) maturities. Our results are robust to alternative measures of oil market uncertainty and bond market volatility.

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Section: Introductionmentioning

confidence: 99%

Oil price uncertainty and movements in the US government bond risk premia

Balcılar

Gupta

Wang

et al. 2020

The North American Journal of Economics and Finance

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“…A generation ago, there was research done on financial stress at the corporate level, owing to the inability to service debt or pensions (Brake & Boehlje, 1985; Hughes, Richardson, & Rister, 1985; Jolly, Paulsen, Johnson, Baum, & Prescott, 1985; Stone, 1991). Much literature since the financial crisis is related to stress at the level of the financial market as a whole (due to market volatility, for example) (Cyn‐Young & Mercado Jr, 2014; Hubrich & Tetlow, 2015; Tng & Kwek, 2015; Wan & Kao, 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

Exploration of financial stress indicators in a developing economy

et al. 2020

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Financial stress scales need to be incorporated into the borrower selection procedure before providing a loan. The three financial stress scales that we examined have strong correlations, but the two involving at least eight questions are better for discriminating between poor and rich. The financial stress indicator that separates male and female students the most is whether financial problems interfere with relationships with other people (interference is more for men). Older students are less satisfied with their financial position and seem to think that their parents worry more about disappointing them for materialistic reasons.

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“…e construction of GSVI based on Google Trends was explained in section (1), and an oil prices forecasting model was made. is section will compare the impact of Google Trends and PHD on oil prices in various aspects.…”

Section: E Impact Of Google Trends On Oil Price Fluctuations and Comparisonmentioning

confidence: 99%

“…As a strategic energy source, oil has both commodity, financial, and political attributes [1,2]. e fluctuation of oil prices will have an important impact on economic growth, stock exchange rates, bond markets, and national security, so the forecasting of crude oil prices has received much attention [3,4].…”

Section: Introductionmentioning

confidence: 99%

How Does Internet Information Affect Oil Price Fluctuations? Evidence from the Hot Degree of Market

Zhao

Guo

Miao

et al. 2020

Discrete Dynamics in Nature and Society

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Not only the fundamentals of supply and demand but also international oil prices are affected by nonfundamental indicators such as emergencies. With the development of big data technology, many unstructured and semistructured factors can be reflected through Internet information. Based on this, this paper proposes a HD-based oil price forecasting model to explore the impact of Internet information on international oil prices. Firstly, we use LDA and other methods to extract topics from massive online news. Secondly, based on conditional probability and correlation, the positive hot degree (PHD) and negative hot degree (NHD) of the oil market are constructed to realize the quantitative representation of Internet information. Finally, the SVAR method is established to explore the interactive relationship between HD and oil prices. The empirical results indicate that PHD and NHD have a better ability to predict international oil prices compared with Google Trends which is widely used in the other research. In addition, PHD has a significant positive impact on oil prices and NHD has a negative impact. In the long term, PHD accounts for 51.00% of oil price fluctuations, ranking the first among relevant influencing factors. The findings of this paper can provide support to investors and policy-makers.

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Interactions between oil and financial markets — Do conditions of financial stress matter?

Cited by 53 publications

References 49 publications

Oil price uncertainty and movements in the US government bond risk premia

Oil price uncertainty and movements in the US government bond risk premia

Exploration of financial stress indicators in a developing economy

How Does Internet Information Affect Oil Price Fluctuations? Evidence from the Hot Degree of Market

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