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Journals / Ege Akademik Bakış / 2011 / Cilt: 11 - Sayı: Özel

Borsa endeks getirilerinde ikili uzun hafıza analizi

Analyzing the dual long memory in stock market returns

Mert URAL
C. Coşkun KÜÇÜKÖZMEN
Pages
19–28
Publication Date
2022-10-12
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Further, it is found that incorporating information on structural breaks in variance improves the accuracy of estimating volatility dynamics and effectively reduces the persistence of volatility.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The purpose of this study is to examine the dual long memory properties for five stock market returns by using joint ARFIMAFIGARCH model and structural break test in context of weak form efficient market hypothesis.
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Key Findings & Data:The models are estimated by using daily closing prices for S&P500, FTSE100, DAX, CAC40 and ISE100.
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Conclusion & Impact:Further, it is found that incorporating information on structural breaks in variance improves the accuracy of estimating volatility dynamics and effectively reduces the persistence of volatility.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

The purpose of this study is to examine the dual long memory properties for five stock market returns by using joint ARFIMAFIGARCH model and structural break test in context of weak form efficient market hypothesis. The models are estimated by using daily closing prices for S&P500, FTSE100, DAX, CAC40 and ISE100. In an effort to assess the impact of structural breaks in volatility persistence, the breaks in variance are detected by using the Iterated Cumulative Sums of Squares (ICSS) algorithm, and dummy variables are incorporated to the models. Empirical findings show that the dual long memory exists for all stock markets. Also the volatility has a predictable structure and indicates that all stock markets are weak form inefficient. Further, it is found that incorporating information on structural breaks in variance improves the accuracy of estimating volatility dynamics and effectively reduces the persistence of volatility.

Özet

Bu çalışmanın amacı, zayıf formda etkin piyasa hipotezi bağlamında birleşik ARFIMA-FIGARCH modeli ve yapısal kırılma testi kullanarak beş farklı borsa endeks getiri serisi için ikili uzun hafıza özelliklerini incelemektir. Modeller S&P500, FTSE100, DAX, CAC40 ve ISE100 borsa endekslerinin günlük kapanış fiyatları kullanılarak test edilmiştir. Volatilite sürekliliği üzerinde yapısal kırılmaların etkilerini belirlemek üzere ICSS (Iterative Cumulative Sums of Squares) algoritması ile varyanstaki kırılmalar tespit edilmiş ve modellere kukla değişkenler olarak eklenmiştir. Analiz sonuçlarına göre, tüm borsalar için ikili uzun hafızanın bulunduğu anlaşılmıştır. Ayrıca volatilitenin öngörülebilir yapı göstermesi nedeniyle tüm borsaların zayıf formda etkinsiz oldukları sonucuna varılmıştır. Bunun yanı sıra, varyanstaki yapısal kırılmaların modellere eklenmesiyle volatilite dinamiklerinin daha doğru hesaplandığı ve volatilite sürekliliğinin fiilen azaldığı saptanmıştır

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Cite This Article

URAL, M., KÜÇÜKÖZMEN, C. C. (2022). Borsa endeks getirilerinde ikili uzun hafıza analizi. Ege Akademik Bakış, Cilt: 11 - Sayı: Özel, 19–28. https://ajindex.com/articles/495470

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