EconPapers    
Economics at your fingertips  
 

Multifractality of the Istanbul and Moscow Stock Market Returns

Mehmet Balcilar

Emerging Markets Finance and Trade, 2003, vol. 39, issue 2, 5-46

Abstract: There is a growing awareness among financial researchers that the traditional models of asset returns cannot capture essential time series properties of the current stock return data. We examine commonly used models, such as the autoregressive integrated moving average (ARIMA) and the autoregressive conditional heteroskedasticity (ARCH) family, and show that these models cannot account for the essential characteristics of the real Istanbul Stock Exchange and Moscow Stock Exchange returns. These models often fail, and when they succeed, they do at the cost of an increasing number of parameters and structural equations. The measures of risk obtained from these models do not reflect the true risk to traders, since they cannot capture all key features of the data. In this paper, we offer an alternative framework of analysis based on multifractal models. Compared to the traditional models, the multifractal models we use are very parsimonious and replicate all key features of the data with only three universal parameters. The multifractal models have superior risk evaluation performance. They also produce better forecasts at all scales. The paper also offers a justification of the multifractal models for financial modeling.

Keywords: Key words: fractal Brownian motion; ; lder exponent; multifractal market hypothesis; multifractal spectrum; scaling phenomena; statistical self-similarity; Wavelet transform (search for similar items in EconPapers)
Date: 2003
References: Add references at CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://mesharpe.metapress.com/link.asp?target=contribution&id=Y44Q0JBX6JRAPBP2 (text/html)
Access to full text is restricted to subscribers.

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: https://EconPapers.repec.org/RePEc:mes:emfitr:v:39:y:2003:i:2:p:5-46

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/MREE20

Access Statistics for this article

More articles in Emerging Markets Finance and Trade from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2024-07-04
Handle: RePEc:mes:emfitr:v:39:y:2003:i:2:p:5-46