Abstract
Denizcilik
piyasası türetilmiş bir talep yapısına saiptir, bu yüzden dünya ekonomisindeki
bir canlanma piyasaya hareket getirecektir. Canlı piyasalardaki gemi söküm fiyatları ana
olarak iki faktörden etkilenir: ilk olarak çeliğe olan talebin artmasından,
ikinci olarak da iyi piyasa koşullarında eski ve köhne gemiler bile karlı
işletilebileceğinden dolayı söküme gönderilen gemi sayısının azalmasından. Bu
yüzden bu iki faktör hurda fiyatlarının navlun oranlarına parallel bir şekilde
artmasına neden olur. Bu çerçevede bu çalışmanın amacı mevcut kısıtlı literature
navlun gelirleriyle hurda fiyatları arasındaki makro düzeyde inceleyerek
katkıda bulunmaktır. Navlun gelirleri Baltic Dry Index ile, ve gemi söküm
fiyatları Hindistan Hurda Fiyatları ile temsil edilmiştir. Değişkenler arasındaki
istatistiksel ilişkiyi belirlemek için korelasyon ve regresyon analizleri kullanılmıştır.
Her iki analizin sonuçları da pozitif yönde anlamlı bir ilişkinin varlığını
doğrulamıştır.
References
- Athenian Shipbrokers, S. A. (2017). Monthly Report. Retrieved September, 9, 2016.
- Banerjee, M., & Frees, E. W. (1997). Influence diagnostics for linear longitudinal models. Journal of the American Statistical Association, 92(439), 999-1005.
- Bloomberg Data Platform (2016), Baltic Dry Index, Retrieved August, 20, 2016
- Brown, R. L., Durbin, J., & Evans, J. M. (1975). Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society. Series B (Methodological), 149-192.
- Chang, M. (2014). Principles of Scientific Methods. New York: CRS Press
- Geman, H. (2008). Risk Management in Commodity Markets: From Shipping to Agricultures and Energy. UK: Wiley
- Grammenos, C. (2010). The Handbook of Maritime Economics and Business. Great Britain: MPG Books
- Guide, Eviews User’s. Quantitative Micro Software. United States of America (2007).
- Gujrati, D.N. (2004). Basic Econometric, (4th Ed.). The McGraw-Hill Companies. NewYork.
- Jugović, A., Komadina, N., & Perić Hadžić, A. (2015). Factors influencing the formation of freight rates on maritime shipping markets. Pomorstvo: Scientific Journal of Maritime Research, 29(1), 23-29.
- Karlis, T., & Polemis, D. (2016). Ship demolition activity: A monetary flow process approach. Pomorstvo: Scientific Journal of Maritime Research, 30(2), 128-132.
- Merikas, A., Merika, A., & Sharma, A. (2015, January). Exploring Price Formation in the Global Ship Demolition Market. In 2015 Annual Meetings.
- Mikelis NE. (2007, September). A statistical overview of ship recycling. In: International symposium on maritime safety, security and environmental protection, Athens.
- Mikelis, N. (2013, April). Ship recycling markets and the impact of the Hong Kong Convention. In International Conference on Ship Recycling SHIPREC.
- Randers, J., & Göluke, U. (2007). Forecasting turning points in shipping freight rates: lessons from 30 years of practical effort. System Dynamics Review, 23(2‐3), 253-284.
- Sall, J. (1990). Leverage plots for general linear hypotheses. The American Statistician, 44(4), 308-315.
- Saraf, M., Stuer-Lauridsen, F., Dyoulgerov, M., Bloch, R., Wingfield, S., & Watkinson, R. (2010). Ship breaking and recycling industry in Bangladesh and Pakistan. The World Bank Washington.
- Kaycheng Soh (2016). Understanding test and exam results statistically: An essential guide for teachers and school leaders. Springer, Singapore
- Stopford, M. (2009). Maritime economics 3e. Routledge.
- UNCTAD (United Nations Conference on Trade and Development) Statistics, Ship Demolition Statistics, 2016
Abstract
Maritime
market has a derived demand structure, so a pickup in the world's economy will bring
vitality to the market. Demolition prices in buoyant market are mainly affected
by two factors: first, the demand for steel increases; second, the number of
ships sent for demolition decreases because even old and obsolete ships can be
profitably operated in good market conditions. So these two factors causes
demolition prices to increase in parallel with freight rates. In this context aim
of this study is to contribute existing limited literature by examining the
relationship between shipping earnings and demolition prices at the macro
level. Shipping earnings are represented by the Baltic Dry Index (BDI), and
scrap prices are represented by India Demolition Prices. Correlation and
regression analyzes are used as methods to determine the statistical relationships
between the variables in the study. The result of both analyzes confirms the
positive significant relationship between the variables.
References
- Athenian Shipbrokers, S. A. (2017). Monthly Report. Retrieved September, 9, 2016.
- Banerjee, M., & Frees, E. W. (1997). Influence diagnostics for linear longitudinal models. Journal of the American Statistical Association, 92(439), 999-1005.
- Bloomberg Data Platform (2016), Baltic Dry Index, Retrieved August, 20, 2016
- Brown, R. L., Durbin, J., & Evans, J. M. (1975). Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society. Series B (Methodological), 149-192.
- Chang, M. (2014). Principles of Scientific Methods. New York: CRS Press
- Geman, H. (2008). Risk Management in Commodity Markets: From Shipping to Agricultures and Energy. UK: Wiley
- Grammenos, C. (2010). The Handbook of Maritime Economics and Business. Great Britain: MPG Books
- Guide, Eviews User’s. Quantitative Micro Software. United States of America (2007).
- Gujrati, D.N. (2004). Basic Econometric, (4th Ed.). The McGraw-Hill Companies. NewYork.
- Jugović, A., Komadina, N., & Perić Hadžić, A. (2015). Factors influencing the formation of freight rates on maritime shipping markets. Pomorstvo: Scientific Journal of Maritime Research, 29(1), 23-29.
- Karlis, T., & Polemis, D. (2016). Ship demolition activity: A monetary flow process approach. Pomorstvo: Scientific Journal of Maritime Research, 30(2), 128-132.
- Merikas, A., Merika, A., & Sharma, A. (2015, January). Exploring Price Formation in the Global Ship Demolition Market. In 2015 Annual Meetings.
- Mikelis NE. (2007, September). A statistical overview of ship recycling. In: International symposium on maritime safety, security and environmental protection, Athens.
- Mikelis, N. (2013, April). Ship recycling markets and the impact of the Hong Kong Convention. In International Conference on Ship Recycling SHIPREC.
- Randers, J., & Göluke, U. (2007). Forecasting turning points in shipping freight rates: lessons from 30 years of practical effort. System Dynamics Review, 23(2‐3), 253-284.
- Sall, J. (1990). Leverage plots for general linear hypotheses. The American Statistician, 44(4), 308-315.
- Saraf, M., Stuer-Lauridsen, F., Dyoulgerov, M., Bloch, R., Wingfield, S., & Watkinson, R. (2010). Ship breaking and recycling industry in Bangladesh and Pakistan. The World Bank Washington.
- Kaycheng Soh (2016). Understanding test and exam results statistically: An essential guide for teachers and school leaders. Springer, Singapore
- Stopford, M. (2009). Maritime economics 3e. Routledge.
- UNCTAD (United Nations Conference on Trade and Development) Statistics, Ship Demolition Statistics, 2016
Details
| Subjects | Business Administration |
|---|---|
| Journal Section | Articles |
| Authors | |
| Publication Date | June 30, 2018 |
| Published in Issue | Year 2018 Volume: 2 Issue: 1 |
