Options trading driven by volatility directional accuracy
AbstractAnalysts have claimed over the last years that the volatility of an asset is caused solely by the random arrival of new information about the future returns from the underlying asset. It is a common belief that volatility is of great importance in finance and it is one of the critical factors determining option prices and consequently driving option-trading strategies. This article discusses an empirical option trading methodology based on efficient volatility direction forecasts. Although in most cases accurate volatility forecasts are hard to obtain, forecasting the direction is significantly easier. Increase in the directional accuracy leads to profitable investment strategies. The net gain is depended on the size of the changes as well; however successful volatility forecasts in terms of directional accuracy was found to be sufficient for positive results. In order to evaluate the proposed methodology weekly data from CAX40, DAX and the Greek FTSE/ASE 20 stock indices were used.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Taylor and Francis Journals in its journal Applied Economics.
Volume (Year): 39 (2007)
Issue (Month): 2 ()
Contact details of provider:
Web page: http://www.tandf.co.uk/journals/routledge/00036846.html
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
- Lux, T. & M. Marchesi, . "Volatility Clustering in Financial Markets: A Micro-Simulation of Interacting Agents," Discussion Paper Serie B 437, University of Bonn, Germany, revised Jul 1998.
- K. Maris & G. Pantou & K. Nikolopoulos & E. PagourtzI & V. Assimakopoulos, 2004. "A study of financial volatility forecasting techniques in the FTSE/ASE 20 index," Applied Economics Letters, Taylor and Francis Journals, vol. 11(7), pages 453-457.
- Buckley, Adrian & Tse, Kalun & Rijken, Herbert & Eijgenhuijsen, Hans, 2002. "Stock Market Valuation with Real Options:: lessons from Netscape," European Management Journal, Elsevier, vol. 20(5), pages 512-526, October.
- N'zue Fofana & B. Wade Brorsen, 2001. "GARCH option pricing with implied volatility," Applied Economics Letters, Taylor and Francis Journals, vol. 8(5), pages 335-340.
- Andersen, Lars, 2002. "How Options Analysis Can Enhance Managerial Performance," European Management Journal, Elsevier, vol. 20(5), pages 505-511, October.
- Bruce Grace, 2000. "Black-Scholes option pricing via genetic algorithms," Applied Economics Letters, Taylor and Francis Journals, vol. 7(2), pages 129-132.
- Carlos Bautista, 2005. "How volatile are East Asian stocks during high volatility periods?," Applied Economics Letters, Taylor and Francis Journals, vol. 12(5), pages 319-326.
- Assimakopoulos, V. & Nikolopoulos, K., 2000. "The theta model: a decomposition approach to forecasting," International Journal of Forecasting, Elsevier, vol. 16(4), pages 521-530.
- William Pedersen, 1998. "Capturing all the information in foreign currency option prices: solving for one versus two implied variables," Applied Economics, Taylor and Francis Journals, vol. 30(12), pages 1679-1683.
- Vicky Bamiatzi & Konstantinos Bozos & Konstantinos Nikolopoulos, 2010. "On the predictability of firm performance via simple time-series and econometric models: evidence from UK SMEs," Applied Economics Letters, Taylor and Francis Journals, vol. 17(3), pages 279-282.
- Konstantinos Nikolopoulos, 2010. "Forecasting with quantitative methods: the impact of special events in time series," Applied Economics, Taylor and Francis Journals, vol. 42(8), pages 947-955.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael McNulty).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.