Forecasting short-term carbon emission futures price volatility: information for hedging carbon emission futures risk
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Author(s)Collins C. NgwakweLink to ORCID Index: http://orcid.org/0000-0002-6954-8897
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DOIhttp://dx.doi.org/10.21511/ee.08(4).2017.01
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Article InfoVolume 8 2017, Issue #4, pp. 6-13
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169 Downloads
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This paper aimed to illustrate how short-term carbon futures speculators might use short-term carbon emission futures data to predict and forecast carbon prices. The paper became apposite given ubiquitous research focussing on long-term carbon futures data, which has left out short-term carbon emission futures speculators with information. Therefore, this paper demonstrated that short-term speculators in carbon futures could indeed use short-term time series data on carbon futures to make a reliable prediction and forecasting of carbon emissions futures price volatility within a short term and thus decide on investment opportunity. The sample data results showed that short-term data could produce a dependable in-sample futures prediction since the in-sample prediction fell within the 95% confidence interval. The demonstration also showed that short-term carbon futures data could assist speculators to conduct a reliable short-term out of sample forecast of carbon futures prices within the closer period. The paper offers practical assistance to carbon futures speculators and is equally important for academic studies for business and economic students on discussions and research bordering on carbon emissions, carbon trading, environmental economics and sustainable development. More carbon short-term forecasting is encouraged – such research should compare short-term forecasting of carbon futures amongst different carbon markets.
- Keywords
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JEL Classification (Paper profile tab)Q54, G13
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References28
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Tables3
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Figures3
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- Fig. 1. The spikes of carbon futures in April 2015 to July 2017
- Fig. 2. Lines of in-sample prediction September 2015 – July 2016 and the actual lines
- Fig.3. Out-of-sample forecast and actual lines
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- Table 1. Linear regression between of carbon futures with time trend as main regressor. Model 5: OLS, using observations September 2015 – July 2017 (T = 23). Dependent variable: carbon price
- Table 2. In-sample prediction Sept 2015 – July 2016 For 95% confidence intervals, t(10, 0.025) = 2.228
- Table 3. Out-of-sample forecast July 2017 – July 2018 For 95% confidence intervals, t(10, 0.025) = 2.228
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The place and perspectives of Ukraine in international integration space
Vitalina Babenko 
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Maryna Pasmor ,
Juliia Pankova ,
Mykhailo Sidorov
doi: http://dx.doi.org/10.21511/ppm.15(1).2017.08
Problems and Perspectives in Management Volume 15, 2017 Issue #1 pp. 80-92 Views: 1420 Downloads: 219 TO CITE АНОТАЦІЯThe resulting indexes for integration processes state development analysis were formed. The integral indexes illustrating integration state were calculated on the basis of member countries of BRICS and Ukraine. It provided the possibility to evaluate socioeconomic state of the countries from the point of view of international integration and create integration processes development scenarios. The strategic directions of integration processes development for Ukraine and member countries of BRICS were formed.
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Forecasting the level of financial security of the country (on the example of Ukraine)
Józef Antoni Haber ,
Alina Bukhtiarova ,
Svitlana Chorna ,
Olesia Iastremska ,
Tetiana Bolgar
doi: http://dx.doi.org/10.21511/imfi.15(3).2018.25
Investment Management and Financial Innovations Volume 15, 2018 Issue #3 pp. 304-317 Views: 1224 Downloads: 289 TO CITE АНОТАЦІЯIn the conditions of functioning of economic relations, which arise between subjects of the financial system of Ukraine, the question of creating safe conditions for their activity is increasingly being raised. Attention is paid to the investigation of the state of financial security of the country as a component of economic security, in terms of its key elements, which allows attention to the most important indicators and to develop measures to prevent existing threats.
The purpose of the paper is to forecast the level of financial security of the country based on regression analysis of impacts factors. The object of the study is the financial system as a mechanism, which is aimed at the activities of financial security subjects of the country to ensure its proper level. As a result of the regression analysis, it was found that changing in the country’s banking security by 1% will decrease the overall financial security index by 0.04 points, while the non-banking market will grow by 0.07 and the monetary component will decrease by 0.51.
Based on the calculation of the arithmetic mean of absolute deviations of independent variables, the estimated value of Ukraine’s financial security level is calculated, which is 40.09% in 2018.
Proposals for improving the “Methodological Recommendations for Calculating the Level of Economic Security of Ukraine” will help to solve the problem of mathematical substantiation of the choice of indicators for assessing financial security, minimize risks, eliminate subjectivity and improve the efficiency and the quality of the country’s financial security assessment methodology.
The article deals with the issues of the financial component of economic security as the main element of ensuring sustainable financial development of the country. -
Forecasting the level of earnings management of Russian and Chinese companies
Investment Management and Financial Innovations Volume 14, 2017 Issue #2 (cont. 1) pp. 264-280 Views: 1111 Downloads: 309 TO CITE АНОТАЦІЯThe purpose of the current paper is to elaborate a model to forecast a particular type of earnings management by companies: upward earnings management, downward earnings management or the absence of significant manipulation.
The sample analyzed in the current paper comprises 664 Russian and 2,380 Chinese public companies for the period 2009-2014. The forecast was made for 2014 based on annual accounting data for 2009-2013. Regression analysis, as well as Classification and Regression Tree modelling (CART), were used. The data forecast for 2014 was compared with actual data for that year, and the accuracy of the forecasting model was assessed.
The paper outlines the main conditions under which a particular type of earnings manipulation is expected to take place in a company in the accounting period following the current one. It is shown that the main factor influencing the company’s level of earnings manipulation of the next accounting period for both Russian and Chinese companies is the debt ratio calculated as the ratio of total liabilities to total assets. The other important factors are: the company size, return on equity, earnings persistence, the level of earnings manipulation in the current period and stock emission.
