Power arch modelling of the volatility of emerging equity markets |
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| Institution: | 1. Lauritsen Lab, Caltech, Pasadena, CA 91125, USA;2. Physics Department, Brandeis University, Waltham, MA 02454, USA;3. Leung Center for Cosmology and Particle Astrophysics, National Taiwan University, Taipei 10617, Taiwan;4. Graduate Institute of Astrophysics, National Taiwan University, Taipei 10617, Taiwan;5. Department of Mathematics, University of California, Davis, CA 95616, USA;1. Imam Mohammad Ibn Saud Islamic University (IMSIU), Saudi Arabia;2. LARTIGE, University of Kairouan, Tunisia;3. Istanbul Medeniyet University, Turkey;4. Rajagiri Business School, Rajagiri Valley Campus, Kochi, India;5. LeBow College of Business, Drexel University, United States;6. Institute of Business Research, University of Economics, Ho Chi Minh City, Viet Nam;1. Faculty of Business and Commercial Sciences, Holy Spirit University of Kaslik, P.O. Box 446, Jounieh, Lebanon;2. School of Public Finance and Public Policy, Central University of Finance and Economics, Beijing, China;3. College of Business, University of Texas at San Antonio, One University Circle, San Antonio, TX 78249, USA;4. Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, China;5. School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China;6. Sustainable Development Research Institute for Economy and Society of Beijing, Beijing 100081, China |
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| Abstract: | In developed equity markets the APARCH model of Ding, Granger and Engle Ding, Z., Granger, C. and Engle, R., 1993. A long memory property of stock market returns and a new model. Journal of Empirical Finance 1, 83–106] has proven to be useful in modelling the leverage and asymmetry effects; power transformations and long memory; and non-normal conditional error distributions that characterise the data. Extending the analysis of Jayasuriya, Shambora and Rossiter Jayasuriya, S., Shambora, W. and Rossiter, R., 2005. Asymmetric volatility in mature and emerging markets, Working Paper, Ohio University.] to a wider set of emerging markets this paper explores the applicability of the model to emerging markets. The key findings are as follows. First, unlike developed markets where a power term of unity and a conditional standard deviation model appears to be appropriate, emerging markets demonstrate a considerably greater range of power values. Second, unlike developed markets where non-normal conditional error distributions appear to fit the data well, there are a set of emerging markets for which estimation problems arise with a conditional t distribution, and a conditional normal distribution appears to be the preferred option. Third, the degree of volatility asymmetry appears to vary across the set of emerging markets, with the Middle Eastern and African markets having very different volatility asymmetry characteristics to those of the Latin American markets. |
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