Large-scale forecasting of large fluctuations using wavelet coherence and multifractal behavior and developing wavelet coherence for multiple time series

Shocks, jumps, booms, and busts are typical large fluctuation markers that appear in crisis. Identifying financial crises and estimating leading indicators with strong relations during crisis periods have an essential role in the literature. This thesis examines the dynamic co-movements of leading indicators' multifractal features to identify financial crises due to large fluctuations. The detected dynamic relationships predict leading indicators with scale-by-scale analysis and make large-scale predictions better than challenger models. As a natural result of these studies, the n-dimensional wavelet coherence method is examined, and the vectorwavelet package is transferred to the R program. This thesis consists of three independent parts, and the contents of the studies are summarized below. In the first part, stock returns' co-movements with other leading indicators in crisis periods are analyzed with multiple and quadruple wavelet coherence using interest rate, exchange rate, and trade balance differences. The scale-by-scale wavelet transformation was used to predict large-scale relationships, and stock return estimation was performed. In the second part, the multifractal characteristics of sectoral default probabilities of the real sector in Turkey and Turkey sovereign CDS rates were examined by detrended fluctuation analysis. Significant dynamic connections between the Hölder exponents of the default rates and CDS during financial crisis periods have been examined. During the periods of financial crises, among the Hölder exponents, severely correlated large scales show multifractal features. Scale-by-scale wavelet transform has been used to predict large-scale relationships, and hence vector fractionally autoregressive integrated moving average forecasting provides better results than scalar models. The final part of the thesis introduces a new wavelet methodology to handle multivariate time series dynamic co-movements by extending multiple quadruple wavelet coherence methodologies. The primary motivation of our works is to measure wavelet coherence analytically for the specific dimension.

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Eser Adı
(dc.title)
Large-scale forecasting of large fluctuations using wavelet coherence and multifractal behavior and developing wavelet coherence for multiple time series
Yazar [Asıl]
(dc.creator.author)
Oygur, Tunç
Yayın Tarihi
(dc.date.issued)
2022
Yazar Departmanı
(dc.creator.department)
Yeditepe University Graduate School of Social Sciences
Yazar Departmanı
(dc.creator.department)
Yeditepe University Graduate School of Social Sciences Master’s Program in Financial Economics
Yazar [KatkıdaBulunan]
(dc.contributor.author)
Erzurumlu, Yaman Ömer
Yazar [KatkıdaBulunan]
(dc.contributor.author)
Ünal, Gazanfer
Özet Bilgisi
(dc.description.abstract)
Shocks, jumps, booms, and busts are typical large fluctuation markers that appear in crisis. Identifying financial crises and estimating leading indicators with strong relations during crisis periods have an essential role in the literature. This thesis examines the dynamic co-movements of leading indicators' multifractal features to identify financial crises due to large fluctuations. The detected dynamic relationships predict leading indicators with scale-by-scale analysis and make large-scale predictions better than challenger models. As a natural result of these studies, the n-dimensional wavelet coherence method is examined, and the vectorwavelet package is transferred to the R program. This thesis consists of three independent parts, and the contents of the studies are summarized below. In the first part, stock returns' co-movements with other leading indicators in crisis periods are analyzed with multiple and quadruple wavelet coherence using interest rate, exchange rate, and trade balance differences. The scale-by-scale wavelet transformation was used to predict large-scale relationships, and stock return estimation was performed. In the second part, the multifractal characteristics of sectoral default probabilities of the real sector in Turkey and Turkey sovereign CDS rates were examined by detrended fluctuation analysis. Significant dynamic connections between the Hölder exponents of the default rates and CDS during financial crisis periods have been examined. During the periods of financial crises, among the Hölder exponents, severely correlated large scales show multifractal features. Scale-by-scale wavelet transform has been used to predict large-scale relationships, and hence vector fractionally autoregressive integrated moving average forecasting provides better results than scalar models. The final part of the thesis introduces a new wavelet methodology to handle multivariate time series dynamic co-movements by extending multiple quadruple wavelet coherence methodologies. The primary motivation of our works is to measure wavelet coherence analytically for the specific dimension.
Konu Başlıkları [Genel]
(dc.subject)
Financial crises
Konu Başlıkları [Genel]
(dc.subject)
Large Fluctuations
Konu Başlıkları [Genel]
(dc.subject)
Large-scale Forecast
Konu Başlıkları [Genel]
(dc.subject)
Multiscale Analysis
Konu Başlıkları [Genel]
(dc.subject)
Vector Wavelet Coherence
Açık Erişim Tarihi
(dc.date.available)
2022-09-21
Açıklama [Genel]
(dc.description)
Final published version
Kayıt Giriş Tarihi
(dc.date.accessioned)
2022-09-21
Tanım Koleksiyon Bilgisi
(dc.description.collectioninformation)
This item is part of the preprint collection made available through Yeditepe University library. For your questions, our contact address is openaccess@yeditepe.edu.tr
Açıklama [Not]
(dc.description.note)
Note: This preprint reports new research that has not been certified by peer review and should not be used as established information without consulting multiple experts in the field.
Yayın Türü [Ortam]
(dc.format)
application/pdf
Tek Biçim Adres
(dc.identifier.uri)
https://hdl.handle.net/20.500.11831/7968
Dil
(dc.language.iso)
eng
Yayıncı
(dc.publisher)
Yeditepe University Academic and Open Access Information System
Haklar
(dc.rights)
Yeditepe University Academic and Open Access Information System
Erişim Hakkı
(dc.rights.access)
Open Access
Telif Hakkı
(dc.rights.holder)
Unless otherwise stated, copyrights belong to Yeditepe University. Usage permissions are specified in the Open Access System, and "InC-NC/1.0" and "by-nc-nd/4.0" are as stated.
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(dc.rights.uri)
http://creativecommons.org/licenses/by-nc-nd/4.0
Telif Hakkı Url
(dc.rights.uri)
https://rightsstatements.org/page/InC-NC/1.0/?language=en
Yayın Turu [Akademik]
(dc.type)
preprint
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