PREDICTING BALTIC DRY INDEX WITH LEADING INDICATORS

Year 2018, Volume: 10 Issue: 2, 233 - 248, 26.12.2018

Duygu Şahan Reha Memişoğlu , Sadık Özlen Başer

https://doi.org/10.18613/deudfd.495820

Cited By: 5

Abstract

The Baltic Dry Index (BDI) is issued by the Baltic Exchange on a daily
basis and it signals for the average cost of shipping raw materials on a number
of shipping routes. Baltic Dry Index is considered by both the private and
public authorities as an important indicator for freight rates, international
trade and economic activity. Conducting a long-term prediction for dry bulk
indices is challenging due to the high volatility of the dry bulk freight
market; therefore, a linear prediction spanning a shorter time period offers
both greater accuracy and can be used as a tool for speculation. The goal of
this paper is to form a linear benchmark model through Box-Jenkins approach
including explanatory variables selected rigorously to forecast Baltic Dry
Index. Using monthly data between January 2010 and June 2017, the analysis
results point out an ARIMAX (10,1,0) model with spot prices of gold and silver,
United States 10-year bond yield and commodity price index composed of
minerals, ores and metals. 

Keywords

Baltic Dry Index, ARIMAX model, forecast, freight market, time series

BALTIK KURU YÜK ENDEKSİ’NİN ÖNCÜ GÖSTERGELER İLE TAHMİNİ

Abstract

Baltik Kuru Yük Endeksi, Baltik Borsası tarafından
günlük olarak yayımlanmakta ve farklı rotalar için ham madde taşıma maliyetini
göstermektedir. Baltık Kuru Yük Endeksi hem kamu hem de özel
sektör yetkilileri tarafından navlun oranları, uluslararası ticaret ve ekonomik
faaliyetler için önemli bir gösterge olarak kabul edilmektedir. Kuru dökme yük
navlun piyasasının çok dalgalı bir yapıya sahip olmasından ötürü kuru yük
endeksleri üzerinde uzun vadeli tahminleme yapmanın oldukça zorlayıcı oluşu,
daha kısa vadeli ve doğrusal tahminleme yapılması ile hem daha isabetli
sonuçlar elde edilmesine hem de sonuçların spekülatif bir araç olarak
kullanılabilmesine olanak vermektedir. Bu çalışmanın amacı Baltık Kuru Yük
Endeksi’ni tahminlemek üzere seçilen açıklayıcı değişkenler içeren Box-Jenkins
yaklaşımı ile bir kıyaslama modeli oluşturmaktır. Ocak 2010-Haziran 2017
verilerinin aylık bazda kullanılması ile elde edilen analiz sonuçları altın ve
gümüş spot fiyatları, Amerika Birleşik Devletler 10 yıllık tahvil getirisi ile mineraller,
cevherler ve metallerden oluşan emtia fiyat endeksini içeren bir ARIMAX (10,1,0)
modelini işaret etmektedir.

Keywords

Baltık Kuru Yük Endeksi, ARIMAX modeli, tahminleme, navlun piyasası, zaman serisi

References

• Adland, R.O. and Cullinane K.P.B. (2005). A time-varying risk premium in the term structure of bulk shipping freight rates. Journal of Transport Economics and Policy, 39(2), 191–208.
• Adland, R.O. and Stradenes, C. (2006). Market efficiency in the bulk freight market revisited. Maritime Policy & Management, 33(2), 107-117.
• Baffes, J. (2007). Oil spills on other commodities. Resources Policy, 32(3), 126-134.
• Bakshi, G., Panayotov, G. and Skoulakis, G. (2011). The Baltic Dry Index as a predictor of global stock returns, commodity returns, and global economic activity. American Finance Association 2012 Meetings, Chicago, USA.
• Box, G.E.P. and Jenkins, G.M. (1970). Time Series Analysis Forecasting and Control. San Francisco: Holden-Day.
• Chou, M.T. (2008). A fuzzy time series model to forecast the BDI. 4th International Conference on Networked Computing and Advanced Information Management. Gyeongju, China.
• Cullinane, K.P. (1992). A short-term adaptive forecasting model for BIFFEX speculation: a Box-Jenkins approach. Maritime Policy & Management: The Flagship Journal of International Shipping and Port Research, 19(2), 91-114.
• Cullinane, K.P., Mason, K.J. and Cape, M. (1999). A comparison of models for forecasting the Baltic Freight Index: Box-Jenkins revisited. International Journal of Maritime Economics, 1(2), 15-39.
• Dickey, D.A. and Fuller, W.A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427-431.
• Evans, J.J. (1994). An analysis of efficiency of the bulk shipping markets. Maritime Policy & Management, 21(4), 311-329.
• Fama, E. (1970). Efficient capital markets: a review of theory and empirical work. Journal of Finance, 22, 383-423.
• Geman, H. and Smith, W.O. (2012). Shipping markets and freight rates: an analysis of the Baltic Dry Index. Journal of Alternative Investments 15(1), 98-109.
• Hale, C. and Vanags, A. (1989). Spot and period rates in the dry bulk market: some tests for the period 1980-1986. Journal of Transport Economics and Policy, 23(3), 281-291.
• Hammoudeh, S. and Yuan, Y. (2008). Metal volatility in presence of oil and interest rate shocks. Energy Economics, 30, 606-620.
• Hyndman, R.J. and Khandakar, Y. (2008). Automatic time series forecasting: the forecast package for R. Journal of Statistical Software, 26(3), 1-22.
• Kavusannos, M.G. and Alizadeh A.H. (2002). The expectations hypothesis of the term structure and risk premiums in dry bulk shipping freight markets. Journal of Transport Economics and Policy, 36(2), 267-304.
• Marlow, P.B. and Gardner, B. (1980). Some thoughts on the dry bulk shipping sector. The Journal of Industrial Economics, 29(1), 71-84.
• Papailias, F., Thomakos, D.D. and Liu, J. (2017). The Baltic Dry Index: cyclicalities, forecasting and hedging strategies. Empirical Economics, 52(1), 255-282.
• Soytas, U., Sari, R., Hammoudeh, S. and Hacihasanoglu, E. (2009). World oil prices, precious metal prices and macroeconomy in Turkey. Energy Policy 37(12), 5557-5566.
• Stopford, M. (2009). Maritime Economics. New York: Routledge.
• Thalassinos, E.I., Hanias, M.P., Curtis, P.G. and Thalassinos, J.E. (2013). Forecasting financial indices: the Baltic Dry Indices. International Journal of Maritime, Trade & Economic Issues, 1(1), 109-130. Veenstra, A.W. (1999). Term structure of ocean freight rates. Maritime Policy & Management, 26(3), 279-293.
• Internet References:
• Bloomberg (2017). BDIY:IND, Bloomberg Professional. Available at: Subscription Service, https://www.bloomberg.com/quote/BDIY:IND Access Date: 20 July 2017.
• Federal Reserve Bank of Saint Louis (2017). Long-Term Government Bond Yields: 10-year. FRED Economic Data. https://fred.stlouisfed.org/series/IRLTLT01USM156N, Access Date: 22 December 2017.
• IMF (2016). Special Drawing Rights. Factsheets. https://www.imf.org/external/np/exr/facts/pdf/sdr.pdf, Access Date: 12 December 2017.
• OECD (2017a). Key Short-term Economic Indicators Dataset. OECD Statistics. http://stats.oecd.org/#, Access Date: 25 December 2017.
• OECD (2017b). Consumer Prices Dataset. OECD Statistics. http://stats.oecd.org/# , Access Date: 22 December 2017.
• The Baltic Exchange (2017). Market Information, indices, BDI. https://www.balticexchange.com/market-information/indices/BDI/, Access Date: 25 December 2017.
• Theodoulidis, B. and Diaz, D. (2009). Analysis of the Baltic Exchange Dry Index using data mining techniques. Working Paper, 23rd March 2009. Available at: https://ssrn.com/abstract=2419097, Access Date: 25 December 2017.
• UNCTAD (2017a). Review of Maritime Transport 2017. http://unctad.org/en/PublicationsLibrary/rmt2017_en.pdf, Access Date: 22 December 2017.
• UNCTAD (2017b). Free Market Commodity Price Indices. UNCTAD Statistics. http://unctadstat.unctad.org/wds/TableViewer/tableView.aspx?ReportId=28769, Access Date: 22 December 2017.
• UNCTAD (2017c). Free Market Commodity Prices. UNCTAD Statistics. http://unctadstat.unctad.org/wds/TableViewer/tableView.aspx?ReportId=28768, Access Date: 22 December 2017.
• Yahoo Finance (2017). S&P 500 Historical Data. World Indices. https://query1.finance.yahoo.com/v7/finance/download/%5EGSPC?period1=1262293200&period2=1498856400&interval=1mo&events=history&crumb=lSHiOrkd7HW, Access Date: 22 December 2017.