TY  - JOUR
T1  - VOLATİLİTEDE YAPISAL KIRILMALAR VE UZUN HAFIZA: AFRİKA ÖRNEĞİ
TT  - STRUCTURAL BREAKS IN VOLATILITY AND LONG MEMORY: AFRICAN EXAMPLE
AU  - Soykan, M. E.
PY  - 2025
DA  - July
Y2  - 2025
DO  - 10.35408/comuybd.1551685
JF  - Yönetim Bilimleri Dergisi
PB  - Çanakkale Onsekiz Mart Üniversitesi
WT  - DergiPark
SN  - 1304-5318
SP  - 1474
EP  - 1500
VL  - 23
IS  - 57
LA  - tr
AB  - Bu çalışma MSCI Öncü Piyasalar Afrika Endeksi’nde uzun hafızanın bulunup bulunmadığını yapısal kırılmalar da dikkate alınarak analiz etmektedir. Bu endeks 12 Afrika ülkesini (Burkina Faso, Benin, Gine-Bissau, Fildişi Sahilleri, Kenya, Mauritius, Mali, Fas, Nijer, Senegal, Togo ve Tunus) içermektedir. 2008 ile 2023 yılları aralığını içeren veri kullanılmıştır. Veriler Refinitiv Eikon’dan temin edilmiştir. Umumiyetle bakıldığında geçmişte farklı sonuçların olduğu ve genel bir fikir birliğinin olmadığı görülmektedir. Bunun nedenleri olarak farklı tarih aralıklarının kullanılması, faydalanılan metotların ve varsayımların farklılığı ve incelenen ülkelerin farklı olması gösterilebilir. Yapılan analizde ilgili veri GARCH, A-GARCH, FIGARCH ve A-FIGARCH modelleri ile ele alınmış, minimum kriterleri yani en düşük HQ, SIC, Shibata ve AIC değerlerini sağlayan FIGARCH modelinin en uygun model olduğu ve bu modelde d parametresi 0 ile 0.5 arasında olduğundan (0.35) ilgili endekste uzun hafıza olduğu dolayısıyla bu piyasanın etkin olmadığı ifade edilebilir. Bu nedenle bu serideki varyans değerleri geçmiş değerlerden tahmin edilebilir. Adaptiv-FIGARCH modeli de maksimum ln(L) değeri verdiğinden ikinci sıradaki en uygun model olarak değerlendirilebilir. Bir başka ilginç sonuç olarak da yapısal kırılma dikkate alındığında A-FIGARCH modelinde literatürdeki sonuçlarla uyumlu olarak d, arch ve garch parametre değerlerinin biraz düştüğü fark edilmektedir. İlgili Afrika ülkeleri daha önce bu yöntemlerle topluca analiz edilmediğinden bu çalışmanın literatüre katkı sağlayacağına inanılmaktadır. Bu elde edilen sonuçlar ekseri akademisyenler, piyasa katılımcıları ve politika oluşturucular için volatilite serilerindeki uzun hafıza ve yapısal kırılma özelliklerini anlamak için ve gelecekteki volatiliteyi tahmin etmek için, piyasa etkinliğini test etmek için, finansal varlıkları fiyatlamak için, nicel yatırım stratejileri oluşturmak için ve piyasa riskini ölçmek için önem taşımaktadır.
KW  - Volatilite
KW  - Uzun Hafıza
KW  - Yapısal Kırılma
KW  - Afrika Endeksi
KW  - FIGARCH
N2  - This paper examines whether there is long memory in MSCI Frontier Markets Africa Index by considering structural breaks. The data include 12 African countries (Burkina Faso, Benin, Guinea-Bissau, Ivory Coast, Kenya, Mauritius, Mali, Morocco, Niger, Senegal, Togo ve Tunisia). Data covering the period between 2008 and 2023 is utilised. Data is obtained from Refinitive Eikon. Generally it seems that there are different results in the past and there is no general consensus. The reasons for this may be the use of different date ranges, the differences in the methods and assumptions used, and the differences in the countries examined. In the analysis, the relevant data is handled with GARCH, A-GARCH, FIGARCH and A-FIGARCH models. The FIGARCH model, which provides the minimum criteria, that is, the lowest HQ, SIC, Shibata and AIC values, is the most suitable model and since the d parameter in this model between 0 and 0.5 (0.35), it can be stated that this market is not efficient because the relevant index has a long memory. Therefore, variance values in this series can be estimated from past values. Since the Adaptiv-FIGARCH model gives the maximum ln(L) value, it can be considered as the second most suitable model. Another interesting result is that when the structural break is taken into account, it is noticed that the d, arch and garch parameter values in the A-FIGARCH model decrease slightly, in line with the results in the literature. It is believed that this study will contribute to the literature since the relevant African countries have not been analyzed collectively with these methods before. These results are generally important and useful for academicians, market participants and policy makers to understand the long memory and structural break characteristics in volatility series and to predict future volatility, to test market efficiency, to price financial assets, to make quantitative investment strategies and to measure market risk.
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UR  - https://doi.org/10.35408/comuybd.1551685
L1  - https://dergipark.org.tr/tr/download/article-file/4221371
ER  -