Economics at your fingertips  

Forecasting changes in the South African volatility index: A comparison of methods

Ushir Harrilall () and Yudhvir Seetharam ()
Additional contact information
Ushir Harrilall: University of the Witwatersrand, South Africa
Yudhvir Seetharam: University of the Witwatersrand, South Africa

EuroEconomica, 2015, issue 2(34), 51-70

Abstract: Increased financial regulation with tougher capital standards and additional capital buffers has made understanding volatility in financial markets more imperative. This study investigates various forecasting techniques in their ability to forecast the South African Volatility Index (SAVI). In particular, a time-delay neural network’s forecasting ability is compared to more traditional methods. A comparison of the residual errors of all the forecasting tools used suggests that the time-delay neural network and the historical average model have superior forecasting ability over traditional forecasting models. From a practical perspective, this suggests that the historical average model is the best forecasting tool used in this study, as it is less computationally expensive to implement compared to the neural network. Furthermore, the results suggest that the SAVI is extremely difficult to forecast, with the volatility index being purely a gauge of investor sentiment in the market, rather than being seen as a potential investment opportunity.

Keywords: forecasting; volatility index; neural networks; time series; emerging markets. (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed

Downloads: (external link) (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this article

More articles in EuroEconomica from Danubius University of Galati Contact information at EDIRC.
Series data maintained by Florian Nuta ().

Page updated 2017-09-29
Handle: RePEc:dug:journl:y:2015:i:2:p:51-70