• ISSN: 2301-3567
    • Frequency: Quarterly (2013-2014); Monthly (Since 2015)
    • DOI: 10.18178/JOEBM
    • Editor-in-Chief: Prof. Eunjin Hwang
    • Executive Editor: Ms. Chloe Wu
    • Abstracting/ Indexing: Engineering & Technology Library,  Electronic Journals Library, Ulrich's Periodicals Directory, MESLibrary, Google Scholar, Crossref, and ProQuest.
    • E-mail: joebm@ejournal.net
JOEBM 2016 Vol.4(4): 252-257 ISSN: 2301-3567
DOI: 10.18178/joebm.2016.4.4.400

The Study of the Long Memory in Volatility of Renewable Energy Exchange-Traded Funds (ETFs)

Maya Malinda and Chen Jo Hui
Abstract— This research applied original price return and adjustment price return for both renewable and unrenewable energy ETFs. Comparing the long memory in volatility and asymmetric volatility of renewable and unrenewable energy ETFs, this study used three models, fractional autoregressive integrated moving average (ARFIMA), a combination of ARFIMA and fractionally integrated exponentially generalized autoregressive conditional heteroscedasticity (ARFIMA-FIEGARCH) and ARFIMA with hyperbolic generalized autoregressive conditional heteroscedasticity (ARFIMA-HYGARCH) models. The results show that by using the adjustment price return data samples, then the results are similar with original price return ETFs. Both unrenewable and renewable energy ETFs have a long memory in volatility and negative asymmetric volatility. ARFIMA-FIEGARCH model perform better to investigate long memory in volatility and asymmetric volatility for both energy ETFs among others.

Index Terms— Long memory in volatility, asymmetric volatility, renewable energy ETFs.

The authors are with the Chung Yuan Christian University, 200 Chung Pei Rd., Chung Li City, Taiwan 32023, R.O.C. (e-mail: hmy.malinda@yahoo.com, johui@cycu.edu.tw).

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Cite: Maya Malinda and Chen Jo Hui, " The Study of the Long Memory in Volatility of Renewable Energy Exchange-Traded Funds (ETFs)," Journal of Economics, Business and Management vol. 4, no. 4, pp. 252-257, 2016.

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