Ekonomik Verilerin Analizinde Zaman Serisi Zinciri Grafik Modelleri: S&P500 Üzerine Bir Vaka Çalışması

Abstract

Bu çalışmanın amacı, ekonomik ve finansal değişkenler arasındaki ilişkilerin araştırılmasıdır. Bu konu hem gelişmekte olan hem de gelişmiş ülke piyasaları için literatürde çokça tartışılmıştır ancak bu çalışmanın literatüre katkısı, ilişkilerin yönünün farklı bir yöntem kullanılarak araştırılmasıdır. Yapılmış çalışmalarda seçili farklı finansal ve ekonomik değişkenler arası ilişkiler araştırılırken regresyon, var analizi ve Granger nedensellik gibi doğrusal modellerin kullanıldığı görülmektedir. Bu çalışmada, zaman serisi zinciri grafik modeli, seçilen ekonomik ve finansal değişkenler arasındaki ilişkiyi zaman içinde incelemek için kullanılmıştır. Zaman serisi zinciri grafik modeli, farklı zaman noktalarında tekrar tekrar ölçülen değişkenler arasındaki koşullu bağımlılığın keşfedilmesini sağlamakta ve verileri yıla göre segmentlere ayırarak analiz edildiğinde de önerilen modelin doğruluğu ortaya koyulmuş olmaktadır. Ek olarak, grafik modeller hassasiyet ve otokorelasyon matrisi analizi için kullanılmıştır. Gloyal ve Welch'in (2021) çalışmasındaki Amerika Birleşik Devletleri’ne ait veri setindeki aralıklı koşullu bağımlılık ve seyrek zamansal bağımlılık gösteren 16 değişken için uyguladığımız bu çalışma, politika yapıcılara değişkenler arası ilişkinin var olup olmadığını göstermesi açısından önemlidir ve daha sonraki aşamalarda Türkiye verilerine de uygulanabilir.

Keywords

• S&P500
• Zaman Serisi Zinciri Grafik Modeli
• Yatırım

Time Series Chain Graphical Models in the Inference of Economic Data: A Case Study from S&P 500

Abstract

Main purpose of this study is the investigation of the relationships between economic and financial variables. This subject is well documented in the literature for both emerging and developed markets but, the contribution of this study to the literature is that the direction of the relationships is investigated by using a different method. In this study, the time series chain graphical model is utilized to examine the relationship between selected economic and financial variables over time. Time Series Chain Graphical Model enables to explore the conditional dependence among variables that are repeatedly measured at different time points. Our research validates the accuracy of the proposed model by segmenting the data by year. Additionally, graphical models are employed for precision and autocorrelation matrix analysis. We use the USA dataset, which can be found in the study of Gloyal and Welch (2021), there exist 16 variables that exhibit occasional conditional dependence and infrequent temporal dependence. This analysis, which is important in showing policy makers whether there is a relationship between variables, can also be applied to Turkish data at later stages.

Keywords

• S&P500
• Time Series Chain Graphical Model
• Investment
• Financial Markets

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