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Languages: English
Types: Doctoral thesis
Subjects:
Studies in the area of shipping freight risk measurement and management are limited and the understandings of the impact of freight volatility dynamics on the freight market remain insufficient and under-researched. The few studies that explore different approaches to measuring freight risk disagree on the most suitable measures and this is down to different interpretation of the underlying conditional variance for freight rates. Thus, the intention of this study is to contribute to the literature in the field of shipping freight risk studies. In this thesis tanker freight risk is measured using univariate and multivariate value-at-risk measures that are structured on a variety of single- and multi-state conditional variance models. Moreover, uncorrelated freight risk factors and conditional freight-beta are estimated through an orthogonal conditional variance and a dynamic freight-beta approach for a portfolio of freight returns, respectively. This thesis also investigates the hypothesis of the state dependency of freight dynamics through a conditional freight limitation framework, which distinguishes between ‘ship-owner’ and ‘cargo-owner’ markets, in particular pre- and during the most recent financial crisis. Furthermore, the short and long term effect of the financial crisis on freight markets are examined through a multi-state Markov switching-regime framework that provides thresholds indicating different freight bands for distinct market conditions. Thus, the hypothesis of variation in the freight-return relation is investigated on the basis that up and down market movements are defined as shipping agent controlled. Additionally, specific and systematic risks for the tanker market are extracted and compared across distinct tanker segments. Finally, a practical insight into shipping practitioners’ measurement and management of freight risk for different shipping segments is examined, where the directional accuracy and volatility of short- and long-term forward curves are assessed and compared against a general perception in the literature.
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    • 7.3.2.2. Identifying turning-points post-exogenous structural break (during the shipping super boom-period)............................................................... 213
    • 7.3.3. Third stage: investigating and testing the significance of structural breaks within freight earnings................................................................................ 214
    • 7.3.4. Fourth stage: identifying expansions and contractions within the boom period .................................................................................................................... 217
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