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, Hö, lder exponent, multifractal market hypothesis, multifractal spectrum, scaling phenomena, statistical self-similarity, Wavelet transform
Publication Name (dc.title) | Multifractality of the Istanbul and Moscow Stock Market Returns |
Author/s (dc.contributor.yazarlar) | Mehmet Balcilar |
Publication type (dc.type) | Konferans Bildirisi |
Language (dc.language) | İngilizce |
Publication year (dc.date.issued) | 2003 |
National/International (dc.identifier.ulusaluluslararasi) | Uluslararası |
Source (dc.relation.journal) | Emerging Markets Finance and Trade |
Additional source name / Conference information (dc.identifier.kaynakadiekbilgi) | 5th International Conference on Economics Location: Ankara (Turkey) September 10-13, 2001 |
Number (dc.identifier.issue) | 2 |
Volume/Issue (dc.identifier.volume) | 39 |
Page (dc.identifier.startpage) | 5-46 |
ISSN/ISBN (dc.identifier.issn) | ISSN: 1540-496X; Online ISSN: 1558-0938 |
Publisher (dc.publisher) | Taylor & Francis |
Databases (dc.contributor.veritaban) | Web of Science Core Collection |
Databases (dc.contributor.veritaban) | Taylor & Francis |
Databases (dc.contributor.veritaban) | Scopus |
Index Type (dc.identifier.index) | CPCI-SSH |
Index Type (dc.identifier.index) | Scopus |
Impact Factor (dc.identifier.etkifaktoru) | 0,828 / 2017-WOS / 5 Year: 0,75 |
Abstract (dc.description.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. - |
Abstract (dc.description.abstract) | Keywords: Key words: fractal Brownian motion, Hö, lder exponent, multifractal market hypothesis, multifractal spectrum, scaling phenomena, statistical self-similarity, Wavelet transform |
URL (dc.rights) | http://www.tandfonline.com/doi/abs/10.1080/1540496X.2003.11052538 |
DOI (dc.identifier.doi) | 10.1080/1540496X.2003.11052538 |
Faculty / Institute (dc.identifier.fakulte) | İktisadi ve İdari Bilimler Fakültesi |
Department (dc.identifier.bolum) | İktisat Bölümü |
Author(s) in the Institution (dc.contributor.author) | Mehmet BALCILAR |
Kayıt No (dc.identifier.kayitno) | BLED0E2DB2 |
Record Add Date (dc.date.available) | 2016-03-18 |
Notes (Publication year) (dc.identifier.notyayinyili) | 2003 |
Wos No (dc.identifier.wos) | WOS:000182706800002 |
Subject Headings (dc.subject) | fractal brownian motion |
Subject Headings (dc.subject) | hölder exponent |
Subject Headings (dc.subject) | multifractal market hypothesis |
Subject Headings (dc.subject) | multifractal spectrum |
Subject Headings (dc.subject) | scaling phenomena |
Subject Headings (dc.subject) | statistical self-similarity |
Subject Headings (dc.subject) | wavelet transform |