ISTFIX Balonlarını Ne Tetikler?

Öz

Denizcilik piyasasında arzın talebe olan tepkisindeki gecikmelerden dolayı büyük veya küçük sürekli konjonktürel dalgalanmalar gözlemlenmektedir. Bazı durumlarda gelirler gemi sahiplerine yaşam fırsatı vermeyecek kadar düşerken, bazı durumlarda onlara sıfırdan çok büyük kazançlara uzanma fırsatı sağlamaktadır. Tüm bu risklere rağmen denizcilik piyasası özellikle Türkiye gibi gelişmekte olan ülkeler için çok önemlidir. Bu çalışmanın amacı İstanbul Navlun Endeksi’nde (ISTFIX) fiyat balonları oluşma olasılığına etki eden faktörleri tespit etmektir. Bu doğrultuda ilk olarak genelleştirilmiş eküs Augmented-Dickey-Fuller (GSADF) testi ile fiyat balonları tespit edilmiştir. GSADF testini takiben de balonların görüldüğü tarihlerden kukla değişken oluşturularak lojistik regresyon modeli kurulmuştur ve balon oluşumunu etkileyen faktörler tespit edilmeye çalışılmıştır. Veri seti 18.03.2011 ve 31.12.2017 tarihleri arasını kapsayan 354 gözlemden oluşmaktadır. Sonuçlara göre uzunlukları 6 ila 12 hafta arasında değişen 4 balon dönemi tespit edilmiştir. Lojistik regresyonda ise “avro” ve “yakıt fiyatı” değişkenlerinin balon oluşma olasılığını arttırdıkları ve “avro” değişkeninin marjinal etkisinin çok daha yüksek olduğu tespit edilmiştir. 

Anahtar Kelimeler

• Fiyat balonları,denizcilik,GSADF,lojistik regresyon,navlun endeksi.

What Triggers the ISTFIX Bubbles?

Abstract

Business cycles are constantly observed, whether small or large, due to delays in response of supply to demand in the maritime market. In some cases, the incomes are so low that it does not give the shipowners a chance to live, and in some cases they go from rags to riches. Despite these risks, the maritime market is vital especially for developing countries such as Turkey. The aim of this study is to determine the factors that influence the probability of price bubble formation in the İstanbul Freight Index (ISTFIX). In this direction, firstly the price bubbles were determined by generalized sup augmented Dickey-Fuller (GSADF) test. Following the GSADF test, a logit regression model was established by creating dummy variables from bubble dates and it was tried to determine the factors affecting bubble formation. The dataset consists of 354 weekly observations and covers the dates between 18.03.2011 and 31.12.2017. According to the results, 4 bubble periods with lengths ranging from 6 to 12 weeks were detected. In the logit model, it was found that “euro” and “fuel price” variables increase the probability of bubble formation and the marginal effect of “euro” is much higher. 

Keywords

• Price bubbles,maritime,GSADF,logit regression,freight index

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