Forecasting Corporate Green Investment Bonds – An Out of Sample Approach
Growing corporate awareness and advocacy for ethical finance and production and services has led to growth in green finance and green bonds, but research dealing on forecasting green bonds is scarce. Objective: The objective of this paper is to present a global analysis of in-sample and out of sample forecast of global corporate green investment bonds. Prior work: the Prior work foundation of this paper is inclined on the fusion of modern portfolio theory with sustainable investment. Similarly, global climate vision 2050 highlights the necessity of forecasting global green investment bond availability. Approach: Green investment bond data were collected from the S&P Green Bonds Index and the Gretl econometrics and statistics software were used to conduct in-sample and out-of-sample forecast of green bonds. Finding: results show that the in-sample forecast of green bonds provided a good approximation of actual green bonds at a low error rate of 1.3%. in the same vein, the out-of-sample forecast had a very low standard error of less than 1.5% and the forecast trend line lye within the 95% confidence error bars. Implication: it provides future information for investors’ green bond hedging; provides insight for climate policy advocates on the future of green finance to help plan climate adaptation and mitigation and useful for European countries who carry the burden of climate mitigation fund to developing countries. Future research should apply this method in other sustainable finance research. Value: This paper provides the first analysis of green bonds’ in-sample and out of sample forecast using the S&P green bonds index; it thus provides information that bridges research gap and that bridges future green bond uncertainty.
Benjamin Tobias Peylo, B.T (2012) A Synthesis of Modern Portfolio Theory and Sustainable Investment, The Journal of Investing, 21 (4) 33-46
Broadstock, D. C., & Cheng, L. T. (2019). Time-varying relation between black and green bond price benchmarks: Macroeconomic determinants for the first decade. Finance Research Letters, 29, 17-22.
Chiesa, M., & Barua, S. (2019). The surge of impact borrowing: the magnitude and determinants of green bond supply and its heterogeneity across markets. Journal of Sustainable Finance & Investment, 9(2), 138-161.
Ehlers, T., & Packer, F. (2017). Green bond finance and certification. Bank of International Settlement Quarterly Review. Retrieved from https://www.bis.org/publ/qtrpdf/r_qt1709h.pdf, date: 09.12.2019
European Commission (2016) Study on the potential of green bond finance for resource-efficient investments. Retrieved from
https://ec.europa.eu/environment/enveco/pdf/potential-green-bond.pdf, date: 09.12.2019
Gilliland, M (2010) the business forecasting deal – eliminating myths. Retrieved from https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781119199885.app1,date: 09.12.2019
Kim, S. B., & Cho, J. H. (2014). A study on forecasting green infrastructure construction market. KSCE Journal of Civil Engineering, 18(2), 430-443.
Li, Z., Tang, Y., Wu, J., Zhang, J., & Lv, Q. (2019). The Interest Costs of Green Bonds: Credit Ratings, Corporate Social Responsibility, and Certification. Emerging Markets Finance and Trade, Retrieved from https://doi.org/10.1080/1540496X.2018.1548350, date: 09.12.2019
Lin, Z. (2018). Modelling and forecasting the stock market volatility of SSE Composite Index using GARCH models. Future Generation Computer Systems, 79, 960-972.
Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of economic perspectives, 17(1), 59-82.
Mathews, J. A., & Kidney, S. (2012). Financing climate-friendly energy development through bonds. Development Southern Africa, 29(2), 337-349.
Ng, A. W. (2018). From sustainability accounting to a green financing system: Institutional legitimacy and market heterogeneity in a global financial centre. Journal of cleaner production, 195, 585-592.
Pati, P. C., Barai, P., & Rajib, P. (2018). Forecasting stock market volatility and information content of implied volatility index. Applied Economics, 50(23), 2552-2568.
Pham, L. (2016). Is it risky to go green? A volatility analysis of the green bond market. Journal of Sustainable Finance & Investment, 6(4), 263-291.
Porfir’ev, B. (2016). Green trends in the global financial system. Mirovaya ekonomika i mezhdunarodnye otnosheniya, 60(9), 5-16.
Reboredo, J. C. (2018). Green bond and financial markets: Co-movement, diversification and price spillover effects. Energy Economics, 74, 38-50.
Reboredo, J. C., & Ugolini, A. (2019). Price connectedness between green bond and financial markets. Economic Modelling,https://doi.org/10.1016/j.econmod.2019.09.004
Reichelt, H. (2010). Green bonds: a model to mobilise private capital to fund climate change mitigation and adaptation projects. The EuroMoney Environmental Finance Handbook. Retrieved from http://worldbank.or.jp/debtsecurities/web/Euromoney_2010_Handbook_Environmental_Finance.pdf, date: 09.12.2019
Schafer, H. (2018). Germany: the ‘greenhorn’in the green finance revolution. Environment: Science and Policy for Sustainable Development, 60(1), 18-27.
Schanes, K., Jager, J., & Drummond, P. (2019). Three scenario narratives for a resource-efficient and low-carbon Europe in 2050. Ecological economics, 155, 70-79.
Sheng, Q. I. A. O. (2007). Research on the Trend of Green Trade Measures Forecasting and China's Countermeasures [J]. Journal of International Trade, 10. Retrieved from
http://en.cnki.com.cn/Article_en/CJFDTotal-GJMW200710007.htm, date: 09.12.2019
Timmermann, A., & Granger, C. W. (2004). Efficient market hypothesis and forecasting. International Journal of forecasting, 20(1), 15-27
Copyright (c) 2021 Collins Ngwakwe
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The author fully assumes the content originality and the holograph signature makes him responsible in case of trial.