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Options trading driven by volatility directional accuracy

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  • K. Maris
  • K. Nikolopoulos
  • K. Giannelos
  • V. Assimakopoulos

Abstract

Analysts 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.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Applied Economics.

Volume (Year): 39 (2007)
Issue (Month): 2 ()
Pages: 253-260

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Handle: RePEc:taf:applec:v:39:y:2007:i:2:p:253-260

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References

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  1. 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.
  2. Andersen, Lars, 2002. "How Options Analysis Can Enhance Managerial Performance," European Management Journal, Elsevier, vol. 20(5), pages 505-511, October.
  3. 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 & Francis Journals, vol. 11(7), pages 453-457.
  4. N'zue Fofana & B. Wade Brorsen, 2001. "GARCH option pricing with implied volatility," Applied Economics Letters, Taylor & Francis Journals, vol. 8(5), pages 335-340.
  5. Mohsen Bahmani-Oskooee, 2002. "Does black market exchange rate volatility deter the trade flows? Iranian experience," Applied Economics, Taylor & Francis Journals, vol. 34(18), pages 2249-2255.
  6. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
  7. 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.
  8. Bruce Grace, 2000. "Black-Scholes option pricing via genetic algorithms," Applied Economics Letters, Taylor & Francis Journals, vol. 7(2), pages 129-132.
  9. Vicent AragO-Manzana & M Angeles Fernandezizquierdo, 2003. "Monthly seasonality of the returns and volatility of the IBEX-35 index and its futures contract," Applied Economics Letters, Taylor & Francis Journals, vol. 10(3), pages 129-133.
  10. Carlos Bautista, 2005. "How volatile are East Asian stocks during high volatility periods?," Applied Economics Letters, Taylor & Francis Journals, vol. 12(5), pages 319-326.
  11. Assimakopoulos, V. & Nikolopoulos, K., 2000. "The theta model: a decomposition approach to forecasting," International Journal of Forecasting, Elsevier, vol. 16(4), pages 521-530.
  12. William Pedersen, 1998. "Capturing all the information in foreign currency option prices: solving for one versus two implied variables," Applied Economics, Taylor & Francis Journals, vol. 30(12), pages 1679-1683.
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Cited by:
  1. 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 & Francis Journals, vol. 17(3), pages 279-282.
  2. Konstantinos Nikolopoulos, 2010. "Forecasting with quantitative methods: the impact of special events in time series," Applied Economics, Taylor & Francis Journals, vol. 42(8), pages 947-955.

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