Forecasting short-term carbon emission futures price volatility: information for hedging carbon emission futures risk
-
Author(s)Collins C. NgwakweLink to ORCID Index: http://orcid.org/0000-0002-6954-8897
-
DOIhttp://dx.doi.org/10.21511/ee.08(4).2017.01
-
Article InfoVolume 8 2017, Issue #4, pp. 6-13
- Cited by
- 890 Views
-
187 Downloads
This work is licensed under a
Creative Commons Attribution-NonCommercial 4.0 International License
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
-
JEL Classification (Paper profile tab)Q54, G13
-
References28
-
Tables3
-
Figures3
-
- 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
-
- 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
-
- Alberola, E., Chevallier, J., & Chèze, B. (2008). Price drivers and structural breaks in European carbon prices 2005–2007. Energy policy, 36(2), 787-797.
- Balcılar, M., Demirer, R., Hammoudeh, S., & Nguyen, D. K. (2016). Risk spillovers across the energy and carbon markets and hedging strategies for carbon risk. Energy Economics, 54, 159-172.
- Baranzini, A., van den Bergh, J. C., Carattini, S., Howarth, R.B., Padilla, E., & Roca, J. (2017). Carbon pricing in climate policy: seven reasons, complementary instruments, and political economy considerations. Wiley Interdisciplinary Reviews: Climate Change.
- Byun, S. J., & Cho, H. (2013). Forecasting carbon futures volatility using GARCH models with energy volatilities.
- Chevallier, J. (2009). Carbon futures and macroeconomic risk factors: a view from the EU ETS. Energy Economics, 31(4), 614-625.
- Chevallier, J. (2011). A model of carbon price interactions with macroeconomic and energy dynamics. Energy Economics, 33(6), 1295-1312.
- Clarke, J., Heinonen, J., & Ottelin, J. (2017). Emissions in a decarbonised economy? GLOBAL lessons from a carbon footprint analysis of Iceland. Journal of Cleaner Production.
- Doi, N., Popov, S., Barcelona, E., & Asano, K. (2011). Assessment of investment requirements for low-carbon power generation in Asia and the Pacific–cost of CO2 emissions reduction and financial viability. Environmental Economics, 2(1), 86-99.
- Ellerman, A. D., Convery, F. J., & De Perthuis, C. (2010). Pricing carbon: the European Union emissions trading scheme. Cambridge: Cambridge University Press.
- Futures-Investor (2017). Futures trading.
- Gao, S., Smits, M., Mol, A. P., & Wang, C. (2016). New market mechanism and its implication for carbon reduction in China. Energy Policy, 98, 221-231.
- Hepburn, C. (2007). Carbon trading: a review of the Kyoto mechanisms. Annual Review of Environment and Resources, 32.
- IPCC (Intergovernmental Panel on Climate Change) (2017). Vulnerability to climate change and reasons.
- Investing.com (2017). Carbon emission futures.
- Capoor, K., & Ambrosi, P. (2008). State and trends of the carbon market 2008.
- Kanamura, T. (2016). Role of carbon swap trading and energy prices in price correlations and volatilities between carbon markets. Energy Economics, 54, 204-212.
- Kang, W., Rouwenhorst, K. G., & Tang, K. (2017). A Tale of Two Premiums: The Role of Hedgers and Speculators in Commodity Futures Markets. Yale International Center for Finance Working Paper, 14-24.
- MISFD (Montreal Institute of Structured Finance and Derivatives) (2014). Forecasting and Hedging Systematic Risk.
- Rannou, Y., & Barneto, P. (2016). Futures trading with information asymmetry and OTC predominance: Another look at the volume/volatility relations in the European carbon markets. Energy Economics, 53, 159-174.
- Smits, M. (2017). The New (Fragmented) Geography of Carbon Market Mechanisms: Governance Challenges from Thailand and Vietnam. Global Environmental Politics.
- Stern, N. H. (2007). The economics of climate change: the Stern review. Cambridge University Press.
- Tang, B. J., Shen, C., & Zhao, Y. F. (2015). Market risk in carbon market: an empirical analysis of the EUA and sCER. Natural Hazards, 75(2), 333-346.
- Xiong, L., Shen, B., Qi, S., Price, L., & Ye, B. (2017). The allowance mechanism of China’s carbon trading pilots: A comparative analysis with schemes in EU and California. Applied Energy, 185, 1849-1859.
- UNFCCC (2014). Kyoto Protocol.
- Zeng, S., Nan, X., Liu, C.,& Chen, J. (2017). The response of the Beijing carbon emissions allowance price (BJC) to macroeconomic and energy price indices. Energy Policy, 106, 111-121.
- Zhang, L., Zhang, J., Xiong, T., & Su, C. (2017). Interval Forecasting of Carbon Futures Prices Using a Novel Hybrid Approach with Exogenous Variables. Discrete Dynamics in Nature and Society.
- Zhang, Y. J. (2016). Research on carbon emission trading mechanisms: current status and future possibilities. International Journal of Global Energy Issues, 39(1-2), 89-107.
- Zhu, B., & Chevallier, J. (2017). Examining the structural changes of European carbon futures price 2005–2012. In Pricing and Forecasting Carbon Markets (pp. 33-45). Springer International Publishing.
-
The place and perspectives of Ukraine in international integration space
Vitalina Babenko 
,
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: 1531 Downloads: 242 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.
-
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: 1307 Downloads: 314 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. -
Assessment of public welfare in Ukraine in the context of the COVID-19 pandemic and economy digitalization
Serhii Kozlovskyi ,
Iaroslav Petrunenko ,
Viktoriia Baidala ,
Viktoriia Myronchuk ,
Tetiana Kulinich
doi: http://dx.doi.org/10.21511/ppm.19(1).2021.35
Problems and Perspectives in Management Volume 19, 2021 Issue #1 pp. 416-431 Views: 1207 Downloads: 793 TO CITE АНОТАЦІЯWith the emergence of the global COVID-19 pandemic in 2019, a process of transformation of the modern economic system took place, which requires new approaches to assessing economic processes. One of such processes is the assessment of public welfare. The purpose of this study is to develop an approach to assessing the level of public welfare of the population of Ukraine in the context of the COVID-19 pandemic and economy digitalization. To solve this problem, the methods of artificial intelligence, in particular the method of fuzzy sets theory, which allows using the incomplete information and making high-quality forecast calculations, are used. The factors influencing the level of public welfare during the COVID-19 pandemic have been identified. These are the following factors: gross domestic product, poverty rate, welfare index, human development index, subsistence level, and indicators that characterize the COVID-19 pandemic (i.e. the total number of COVID-19 cases, the total number of deaths from COVID-19, and the total number of vaccinations from COVID-19 in Ukraine). Using fuzzy sets theory, an economic-mathematical model for assessing the level of public welfare in the context of the COVID-19 pandemic in Ukraine was built. Two-dimensional dependences of the level of public welfare of Ukraine in the context of the COVID-19 pandemic on indicators such as gross domestic product, subsistence level, and the total number of cases of COVID-19 in Ukraine were obtained. The results of the study established that the level of public welfare in the context of the COVID-19 pandemic on the 0-100 scale is predicted to be as follows points: 2021 – 17, 2022 – 23, 2023 – 27, 2024 – 19, 2025 – 35 and will not meet international standards.
