MODELLING OF BIST TOURISM INDEX’S TRADING VOLUME WITH STABLE PARETIAN DISTRIBUTIONS

Year 2017, , 11 - 18, 30.12.2017

Hulya Basegmez Elif Cekici

https://doi.org/10.17261/Pressacademia.2017.738

Abstract

Purpose- The contribution of tourism
sector to the national economy is crucial. But the sector has a structure which
is always hold risks and uncertainties. 
For this purpose, the distribution of daily trading volumes of the
tourism companies that are located in the high-risk tourism sector and traded
in BIST will be modelled.

Methodology-
As the distribution of BIST Tourism trading volume data does not suitable
for normal distribution, it is modeled by analyzing with stable distributions.

Findings-
The parameters of stable distribution are estimated according to the quantiles
method which one of the most used estimation methods.

Conclusion-
Estimated parameter values show that the stable distributions can be used as an
appropriate model for daily trading volume of BIST tourism index. 

Keywords

Stable distributions, BIST, Symmetric stable distributions

References

• Borak, S., Härdle, W., & Weron, R. (2005). Stable distributions. In Statistical tools for finance and insurance (pp. 21-44). Springer Berlin Heidelberg.
• Çekici, E. (2003). İşlem Hacmi Verilerinin Kararlı Paretian Dağılımlarla Modellenmesi, Doktora Tezi, Marmara Üniversitesi, İstanbul.
• Fama, E. F., & Roll, R. (1968). Some properties of symmetric stable distributions. Journal of the American Statistical Association, 63(323), 817836.
• Fama, E. F., & Roll, R. (1971). Parameter estimates for symmetric stable distributions. Journal of the American Statistical Association, 66(334), 331-338.
• Hill, B. M. (1975). A simple general approach to inference about the tail of a distribution. The annals of statistics, 3(5), 1163-1174.
• Inskeep, E. (1991). Tourism planning: an integrated and sustainable development approach. Van Nostrand Reinhold.
• Kuruoglu, E. E. (2001). Density parameter estimation of skewed/spl alpha/-stable distributions. IEEE transactions on signal processing, 49(10), 2192-2201.
• Mandelbrot, B. (1963). The Variation of Certain Speculative Prices. The Journal of Business, 36(4), 394-419. Retrieved from http://www.jstor.org/stable/2350970.
• McCulloch, J. H. (1986). Simple consistent estimators of stable distribution parameters. Communications in Statistics:Simulation and Computation, 15(4), 1109-1136.
• Nolan, J. P. (1998). Parameterizations and modes of stable distributions. Statistics & probability letters, 38(2), 187-195.
• Nolan, J. P. (2001). Maximum likelihood estimation and diagnostics for stable distributions. Lévy processes: theory and applications, 379-400.
• Nolan, J.P. (2016). Stable Distributions: Models for Heavy Tailed Data. http://academic2.american.edu/~jpnolan/stable/chap1.pdf Önalan, Ö. (2010). α− Kararlı Dağılımlarla Finansal Risk Ölçümü, 28(1), 549-571.
• Uchaikin, V. V., & Zolotarev, V. M. (1999). Chance and stability: stable distributions and their applications. Walter de Gruyter.
• Yang, Y. (2012). Option Pricing With Non-Gaussian Distribution-Numerical Approach.
• Zhaozhi Fan. (2006). Parameter Estimation of Stable Distributions, Communications in Statistics - Theory and Methods, 35(2), 245-255 Zolotarev, V. M. (1996). One-dimensional Stable Distributions (Vol. 65). American Mathematical Society, USA.