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Impact of Energy Use Segregation on Carbon Emissions: The Role of FDI in Net Importing and Net Exporting Countries Read chapter Read first chapter

2020 | OriginalPaper | Chapter

4. Optimal Forecast Models for Clean Energy Stock Returns

Authors : Victor Troster, Muhammad Shahbaz, Demian Nicolás Macedo

Published in: Econometrics of Green Energy Handbook

Publisher: Springer International Publishing

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Abstract

This chapter searches for optimal models for forecasting the Wilder Hill Clean Energy Index (ECO), the Standard and Poor’s Global Clean Energy Index (SPCLE), the MAC Global Solar Energy Index (SUN), and the European Renewable Energy Index (EURIX). These indices measure the stock market performance of renewable energy companies. We employ fat-tailed distributed models, and we analyze their in-sample and out-of-sample performance for the returns and the 1%-Value-at-Risk (VaR) of renewable energy indices. Heavy-tailed distributed GARCH and GAS are optimal for all renewable energy returns. They also have the lowest out-of-sample mean-squared error and the best coverage for 1%-VaR of renewable energy returns. These findings highlight the relevance of modeling the kurtosis for renewable energy returns, and they are relevant for policymakers and investors who invest in the renewable energy sector.

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Literature
Ahmad, W., Sadorsky, P., & Sharma, A. (2018). Optimal hedge ratios for clean energy equities. Economic Modelling, 72, 278–295.CrossRef
Ang, B. W., Choong, W. L., & Ng, T. S. (2015). Energy security: Definitions, dimensions and indexes. Renewable and Sustainable Energy Reviews, 42, 1077–1093.CrossRef
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307–327.MathSciNetCrossRef
Bondia, R., Ghosh, S., & Kanjilal, K. (2016). International crude oil prices and the stock prices of clean energy and technology companies: Evidence from non-linear cointegration tests with unknown structural breaks. Energy, 101, 558–565.CrossRef
Creal, D., Koopman, S. J., & Lucas, A. (2013). Generalized autoregressive score models with applications. Journal of Applied Econometrics, 28, 777–795.MathSciNetCrossRef
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74, 427–431.MathSciNetMATH
Diebold, F. X., Gunther, T. A., & Tay, A. S. (1998). Evaluating density forecasts with applications to financial risk management. International Economic Review, 39, 863–883.CrossRef
Ding, Z., Granger, C. W. J., & Engle, R. F. (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 1, 83–106.CrossRef
Engle, R. F., & Bollerslev, T. (1986). Modelling the persistence of conditional variances. Econometric Reviews, 5, 1–50.MathSciNetCrossRef
Engle, R. F., & Lee, G. (1999). A long-run and short-run component model of stock return volatility. In R. F. Engle, & H. White (Eds.), Cointegration, causality, and forecasting: A festschrift in honour of clive W. J. Granger. Oxford University Press, pp. 475–497.
Engle, R. F., & Manganelli, S. (2004). CAViaR: Conditional autoregressive value at risk by regression quantiles. Journal of Business & Economic Statistics, 22, 367–381.MathSciNetCrossRef
Gabaix, X. (2009). Power laws in economics and finance. Annual Review of Economics, 1, 255–294.CrossRef
Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48, 1779–1801.CrossRef
González-Rivera, G., Lee, T.-H., & Mishra, S. (2004). Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood. International Journal of Forecasting, 20, 629–645.CrossRef
Grubb, M., Vrolijk, C., & Brack, D. (2018). Routledge revivals: Kyoto protocol (1999): A Guide and Assessment. Routledge.
Harvey, A. C. (2013). Dynamic models for volatility and heavy tails: With applications to financial and economic time series. Cambridge: Cambridge University Press.CrossRef
Henriques, I., & Sadorsky, P. (2008). Oil prices and the stock prices of alternative energy companies. Energy Economics, 30, 998–1010.CrossRef
Hentschel, L. (1995). All in the family Nesting symmetric and asymmetric GARCH models. Journal of Financial Economics, 39, 71–104.CrossRef
Higgins, M. L., & Bera, A. K. (1992). A class of nonlinear ARCH models. International Economic Review, 33, 137–158.CrossRef
IRENA. (2017). Renewable energy statistics 2017. Abu Dhabi: The International Renewable Energy Agency.
Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6, 255–259.MathSciNetCrossRef
Kaldellis, J. K., & Zafirakis, D. (2011). The wind energy (r)evolution: A short review of a long history. Renewable Energy, 36, 1887–1901.CrossRef
Kittner, N., Lill, F., & Kammen, D. M. (2017). Energy storage deployment and innovation for the clean energy transition. Nature Energy, 2, 17125.CrossRef
Klein, D., Carazo, M. P., Doelle, M., Bulmer, J., & Higham, A. (2017). The Paris agreement on climate change: Analysis and Commentary. Oxford, UK: Oxford University Press.
Kumar, S., Managi, S., & Matsuda, A. (2012). Stock prices of clean energy firms, oil and carbon markets: A vector autoregressive analysis. Energy Economics, 34, 215–226.CrossRef
Lieb-Dóczy, E., Börner, A. R., & MacKerron, G. (2003). Who secures the security of supply? European perspectives on security, competition, and liability. Electricity Journal, 16, 10–19.CrossRef
Managi, S., & Okimoto, T. (2013). Does the price of oil interact with clean energy prices in the stock market? Japan and the World Economy, 27, 1–9.CrossRef
McAleer, M., & Da Veiga, B. (2008). Single-index and portfolio models for forecasting value-at-risk thresholds. Journal of Forecasting, 27, 217–235.MathSciNetCrossRef
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59, 347–370.MathSciNetCrossRef
REN21 (2017). Renewables 2017 global status report. Paris: REN21 Secretariat.
Sadorsky, P. (2012). Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies. Energy Economics, 34, 248–255.CrossRef
Sagar, A. D., & van der Zwaan, B. (2006). Technological innovation in the energy sector: R&D, deployment, and learning-by-doing. Energy Policy, 34, 2601–2608.CrossRef
Schellnhuber, H. J., Rahmstorf, S., & Winkelmann, R. (2016). Why the right climate target was agreed in Paris. Nature Climate Change, 6, 649–653.CrossRef
Teske, S., Pregger, T., Simon, S., Naegler, T., Graus, W., & Lins, C. (2011). Energy [R]evolution 2010-a sustainable world energy outlook. Energy Efficiency, 4, 409–433.CrossRef
Wang, Y., & Wu, C. (2012). Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models? Energy Economics, 34, 2167–2181.CrossRef
Wilson, C., & Grubler, A. (2011). Lessons from the history of technological change for clean energy scenarios and policies. Natural Resources Forum, 35, 165–184.CrossRef
Zakoian, J. M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18, 931–955.CrossRef
Metadata
Title
Optimal Forecast Models for Clean Energy Stock Returns
Authors
Victor Troster
Muhammad Shahbaz
Demian Nicolás Macedo
Copyright Year
2020
Book
Econometrics of Green Energy Handbook

Print ISBN: 978-3-030-46846-0

Electronic ISBN: 978-3-030-46847-7

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-46847-7_4