Measuring the market risk of freight rates: A forecast combination approach

Abstract

This paper addresses the issue of freight rate risk measurement via value at risk (VaR) and forecast combination methodologies while focusing on detailed performance evaluation. We contribute to the literature in three ways: First, we reevaluate the performance of popular VaR estimation methods on freight rates amid the adverse economic consequences of the recent financial and sovereign debt crisis. Second, we provide a detailed and extensive backtesting and evaluation methodology. Last, we propose a forecast combination approach for estimating VaR. Our findings suggest that our combination methods produce more accurate estimates for all the sectors under scrutiny, while in some cases they may be viewed as conservative since they tend to overestimate nominal VaR.

Portfolio value‐at‐risk estimation for spot chartering decisions under changing trade patterns: A copula approach

“…Accurate measurement of freight market risk is vital for shipping market risk management and fleet portfolio management (Argyropoulos & Panopoulou, 2018). The implicit assumption made in most of the existing literature on shipping risk management is that, either the variance or the standard deviation of freight series can be used as a proxy for the risk of that asset (see Alexandridis et al., 2018; Alizadeh et al., 2015; Berg‐Andreassen, 1998; Cullinane, 1995; Kavussanos & Visvikis, 2004, for example).…”

“…The implicit assumption made in most of the existing literature on shipping risk management is that, either the variance or the standard deviation of freight series can be used as a proxy for the risk of that asset (see Alexandridis et al., 2018; Alizadeh et al., 2015; Berg‐Andreassen, 1998; Cullinane, 1995; Kavussanos & Visvikis, 2004, for example). Using variance as the standard for risk measurement is overly simple, however, and has been proven to be inadequate for the shipping market, due to the existence of nonnormality and fat‐tailed behaviors (Argyropoulos & Panopoulou, 2018). Furthermore, the use of variance assumes symmetry, which may not be the case in reality.…”

“…In contrast to the vast body of literature on VaR model estimations and selections in conventional financial assets and commodities, comprehensive and systematic studies of freight market risk using VaR are rather limited (Angelidis & Skiadopoulos, 2008; Argyropoulos & Panopoulou, 2018; Kavussanos & Dimitrakopoulos, 2011). In addition, most studies are conducted for a univariate series.…”

“…Kavussanos and Dimitrakopoulos (2011) evaluated the performance of different freight rate risk models by employing a similar VaR methodology for the tanker sector. Argyropoulos and Panopoulou (2018) re‐evaluated the performance of popular VaR estimation methods and experimented with combinations of different methods for measuring both tanker and dry bulk freight rate risk. Detailed backtesting was also performed in this study; however, the study focuses on estimating VaR for individual routes or indices (Argyropoulos & Panopoulou, 2018).…”

Forecasting robust value-at-risk estimates: evidence from UK banks

“…asymptotically distributed χ 2 1 with one degree of freedom, where T ij , with i, j = 0(noviolation), 1(violation), is the number of observed events with the j th event following i th , and π 01 , π 01 and π are estimates of the probabilities of T i,j (Argyropoulos and Panopoulou 2017…”