Please use this identifier to cite or link to this item:
http://hdl.handle.net/1893/26731
Appears in Collections: | Accounting and Finance Journal Articles |
Peer Review Status: | Refereed |
Title: | Cross-border exchanges and volatility forecasting |
Author(s): | Goyal, Abhinav Kallinterakis, Vasileios Kambouroudis, Dimos S Laws, Jason |
Contact Email: | d.s.kambouroudis@stir.ac.uk |
Keywords: | Volatility forecasting Exchange groups Feedback trading Global financial crisis JEL Classification: G01 G02 G15 G17 |
Issue Date: | 2018 |
Date Deposited: | 16-Feb-2018 |
Citation: | Goyal A, Kallinterakis V, Kambouroudis DS & Laws J (2018) Cross-border exchanges and volatility forecasting. Quantitative Finance, 18 (5), pp. 789-799. https://doi.org/10.1080/14697688.2017.1414512 |
Abstract: | We test for the performance of a series of volatility forecasting models (GARCH 1,1; EGARCH 1,1; CGARCH) in the context of several indices from the two oldest cross-border exchanges (Euronext; OMX). Our findings overall indicate that the EGARCH (1,1) model outperforms the other two, both before and after the outbreak of the global financial crisis. Controlling for the presence of feedback traders, the accuracy of the EGARCH (1,1) model is not affected, something further confirmed for both the pre and post crisis periods. Overall, ARCH effects can be found in the Euronext and OMX indices, with our results further indicating the presence of significant positive feedback trading in several of our tests. |
DOI Link: | 10.1080/14697688.2017.1414512 |
Rights: | This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. This is an Accepted Manuscript of an article published by Taylor & Francis Group in Quantitative Finance on 23 Jan 2018, available online: http://www.tandfonline.com/10.1080/14697688.2017.1414512 |
Files in This Item:
File | Description | Size | Format | |
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QF 2017 RR.pdf | Fulltext - Accepted Version | 576.06 kB | Adobe PDF | View/Open |
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