Statistical test for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si0006.gif" overflow="scroll"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:msub><mml:mrow><mml:mi>ρ</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="italic">DCCA</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>: Methods and data

Guedes, Everaldo Freitas; Brito, Adriana; Filho, Florêncio Mendes Oliveira; Fernandez, B.F.; Castro, Arleys Pereira Nunes de; Filho, Aloísio Machado da Silva; Zebende, Gilney Figueira

doi:10.1016/j.dib.2018.03.080

Cited by 26 publications

(25 citation statements)

References 5 publications

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“…The change in the pattern of correlations is quite evident, making it interesting to analyze the evolution of the ρDCCA over time. With this objective, we calculate the ∆ρDCCA to evaluate that change, also employing the test proposed by [57,58], considering the critical values identified in Table 2 (see Appendix B for details of this test). Note that these critical values are close to zero, making it difficult to distinguish between negative and positive values, but allowing analysis of the statistical significance of the data.…”

Section: Resultsmentioning

confidence: 99%

“…Table 2. Critical values to test the significance of ∆ρDCCA(n), considering the confidence levels (CL) of 90%, 95% and 99% and N = 2000 [57,58]. n = 4 n = 8 n = 16 n = 32 n = 62 n = 125 n = 250 CL = 90% 0.0009 0.0008 0.0008 0.0008 0.0008 0.0008 0.0008 CL = 95% 0.0010 0.0010 0.0009 0.0009 0.0009 0.0009 0.0010 CL = 99% 0.0014 0.0013 0.0012 0.0012 0.0012 0.0012 0.0013 In the following decade, with the correlations represented in Figure 3, a positive and significant correlation of oil with the Oil and Gas sector is identified (only in the longer time scale is the correlation marginally non-significant).…”

Section: Resultsmentioning

confidence: 99%

“…To do so, we use the ∆ρDCCA(n) proposed by [56]. For this measure, it is also possible to test its significance, aligning with the critical values of [57,58] and with the following testing hypotheses:…”

Section: Data and Methodsologymentioning

confidence: 99%

“…∆ρDCCA for the correlation between WTI oil and Euro Stoxx industrial indexes, considering sequential 5-year periods, as a function of n (days). Dashed lines are the critical values of the test developed by[57,58]. (A) measures…”

mentioning

confidence: 99%

“…Critical values to test the significance of ∆ρDCCA(n), considering the confidence levels (CL) of 90%, 95% and 99% and N = 2000[57,58].…”

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confidence: 99%

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The Exposure of European Union Productive Sectors to Oil Price Changes

Ferreira

Pereira

2020

Sustainability

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Oil is one of the most important products in the world, being used for fuel production but also as an input in several industries. After the oil shocks of the 1970s, which caused great turbulence, the interest in the analysis of this particular product grew. The analysis of the comovements between oil and other assets became a hot topic. In this study, we propose an analysis of how oil price correlates with several industry indexes. The detrended cross-correlation analysis coefficient (ρDCCA) is used, with data from 1992 to 2019, and we analyze not only the correlation between oil and several Euro Stoxx indexes during the whole sample, but also how that correlation evolved for the different decades (1990s, 2000s and 2010s). Naturally, oil and gas are the sectors that correlate the most with crude oil, with correlation coefficients reaching levels higher than 0.6 in some cases. However, the results also indicate that all sectors are now more exposed to oil price variations than in the past, with the financial sector as one of the sectors with the greatest increase in correlation.Sustainability 2020, 12, 1620 2 of 16 importance for the economy as a whole, but also because price shocks are common, having an impact on the economy. Moreover, and because oil is not only a very important raw material but also a non-renewable resource, the study of its behavior, in this case the comovements of its price with stock markets, could go towards a better understanding of how financial stock markets can be related with sustainability (see, for example, [12,13] for some discussion on this topic).In this study we propose an analysis of how oil price correlates with several of the Euro Stoxx industry indexes. The objective is to understand how the correlation differs among 10 different sectorial indexes: oil and gas, basic materials, industrials, consumer goods, health care, consumer services, telecommunications, utilities, financials and technology. Despite the large body of literature analyzing comovements between oil price and other assets, we found some need of work analyzing the possible dynamics of the relationship between crude oil prices and sectoral indexes in the EU. This kind of analysis could contribute to energy policies to reduce European countries' dependence on oil.Despite the existence of several approaches to the study of correlations between oil and different sectors, we propose a different approach. Basing our study on the detrended cross-correlation analysis coefficient (ρDCCA), and with data from 1992 to 2019, we analyse not only the correlation between oil and the chosen indexes during the whole sample, but we also make an analysis for each decade, i.e., the 1990s, 2000s and 2010s. With this, and based on the difference in the correlation between different periods (∆ρDCCA), it is possible to analyze the evolution of correlations over time and understand if those indexes' exposure to possible oil shocks increased or not, something of great importance for all the agents mentioned above.Our main findings point ...

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%