Measuring the value at risk of portfolios in the shipping freight market by utilizing copula functions
Creators: name: Nowruzi, Mohsen
Publisher: name: University of Tasmania
Year: 2024

description: Shipping freight market is characterized as an extremely volatile market with strong short- and long-run cycles. In this regard, freight market participants are exposed to a high level of market risk and, therefore, measuring freight rate risk has been a major task for the shipping freight market participants who usually hold a combination of various freight rates from different markets and/or sectors. Considering that there has been no research on the market risk measurement of freight portfolios and freight derivatives, this study concentrates on measuring the market risk of freight rates portfolios in both freight derivatives and physical freight markets. This study introduces a new methodology based on copula models to compute the Value at Risk (VaR) of portfolios by relaxing two fundamental assumptions i.e., linearity of relationships between freight rates and normality of their joint distributions. It aims to discover the market-risk structure of freight portfolios and compare the VaR of freight portfolios of different sectors. <br>This study constructed four freight portfolios using a dataset of physical freight rates and freight derivatives i.e., Freight Forward Agreements (FFAs) in the dry bulk and the tanker shipping freight markets. These portfolios represent price movements in the four main freight markets, namely, physical freight, dry bulk FFA, dirty tanker FFA and clean tanker FFA. The dataset was obtained from the Baltic Exchange and covered the period from 01 August 2014 to 29 June 2020 including 1491 daily observations from three major Baltic Exchange Indices i.e., Baltic Dry Index (BDI), Baltic Dirty Tanker Index (BDTI) and Baltic Clean Tanker Index (BCTI). For freight forward agreements, three dry bulk indexes including Baltic Capesize Index four time charter average (BCI 4TCavg), Baltic Panamax Index four time charter average (BPI 4TCavg) and Baltic Supramax Index six time charter average (BSI 6TCavg), three clean tanker routes TC2_37, TC5 and TC6, and three dirty tanker routes TD3C, TD7 and TD20 were selected to capture FFA price movements. <br>The methodology to compute the VaR of freight portfolios includes three steps. Firstly, a wide range of VaR methodologies from the three approaches of parametric, non-parametric and semi-parametric were performed to forecast one-day ahead VaR of individual freight rates at two confidence levels i.e., 95% and 99%. Specifically, parametric models include GARCH?family models and two-state Markov Regime Switching (2S-MRS) models; semi-parametric models involve Monte Carlo Simulation (MC) and Filtered Historical Simulation (FHS); and non-parametric models include Historical Simulation (HS) with different window sizes. Secondly, a variety of copula functions from different categories i.e., static, time-varying and Markov Regime Switching were employed to capture non-linear relationships between pairs of freight rates in each portfolio via coefficient of lower tail dependency (LTD). Finally, a range of backtesting procedures were applied over the VaR forecasts to evaluate the forecasting performance of VaR models for individual freight rates and the portfolios. <br>Out of sample results indicated the overall superiority of parametric VaR models over non?parametric and semi-parametric VaR models. The component GARCH model (cs GARCH), which is applied on freight rates for the first time, is found to be the only successful model for all the freight rates. Furthermore, the volatility of prices of dry bulk FFA and clean tanker FFA was found to follow a two-state regime switching structures with high and low volatility states. In addition, the goodness of fit results for the copula functions revealed the tail asymmetry of dependency structures between all the examined freight rates and that the non-linear relationships between freight rates vary during the time. Moreover, the backtesting results with respect to the VaR of four portfolios showed the high accuracy and robustness of the proposed model for VaR measurement of freight rates portfolios. Finally, this study revealed the impact of Covid-19 pandemic was not only on the volatility structure of all the freight rates but also the relationship structure and particularly the strength of relationships between the pairs of freight rates as well as risk level of freight portfolios. <br>This study contributes to the existing body of knowledge on portfolio risk management by proposing a new methodology based on copula/VaR models for portfolio market risk measurement, particularly in the shipping freight markets. In addition, it contributes to a better understanding of market risk structure of physical freight rates, freight derivatives and freight?rates relationships in different market condition. Findings of this study can assist freight market participants in forecasting their profitability and potential losses, designing effective trading strategies as well as improving their market risk management strategies.