Petroleum Term Structure Dynamics and the Role of Regimes

Bentsos

2023

Int. J Fin Econ

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This article investigates whether time variation in the returns' co‐movement of oil and Baltic Dirty Tanker Index can be linked to oil market uncertainty. We measure uncertainty using a battery of different proxies considering both parametric and non‐parametric methods and study its role from both statistical and economic perspectives. Using a regression framework combined with regime switching analysis, we show that oil price uncertainty and the future correlation of oil and dirty‐tanker returns are negatively associated. This negative association is more pronounced in highly volatile periods. The identified regimes are directly linked to high‐low crude oil volatility periods with implications on the level of correlation they exhibit to oil returns. Results are robust across crudes and volatility measures. Additional robustness checks corroborate that results hold for individual dirty‐tanker routes and clean‐tanker cargoes.

Section: Data Description and Preliminary Analysismentioning

confidence: 99%

Oil price uncertainty and the relation to tanker shipping

Bentsos

2023

Int. J Fin Econ

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“…In the winter, demand inelasticity and higher marginal cost of production make active storage management less flexible to absorb demand shocks. In this context, model performance may vary when market conditions change, either in terms of market volatility and disequilibria (Nomikos & Pouliasis, 2015), volatility forecast accuracy (Nomikos & Pouliasis, 2011), or HE (see Chang, Lai, & Chuang, 2010).…”

Section: Performance Under Different Market Conditionsmentioning

confidence: 99%

A novel risk management framework for natural gas markets

Journal of Futures Markets

Visvikis

Papapostolou

et al. 2019

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This paper examines dynamic hedges in the natural gas futures markets for different horizons and explores the gains from devising risk management strategies. Despite the substantial progress made in developing hedging models, forecast combinations have not been explored. We fill this gap by proposing a framework for combining hedge-ratio predictions. Composite hedge ratios lead to significant reduction in portfolio risk, whether spot prices are partially predictable or not. We offer insights on hedging effectiveness across seasons, backwardation-contango conditions and the asymmetric profiles of long-short hedgers. We conclude that forecast combinations better reconcile realized performance with the hedging process, mitigating model instability. K E Y W O R D S dynamic futures hedging, forecast combination, natural gas J E L C L A S S I F I C A T I O N C32; C53; G11; G32; L95 3Gagnon and Lypny (1995) provide evidence in support of GARCH models. In contrast, Lien and Tse (2002) support the traditional regression approach. GARCH models exhibit few limitations. For example, the observed nonnormalities in return distributions are more pronounced than those implied by GARCH; the model fails to reproduce time variability in higher moments unless explicitly modeled, and a strong degree of persistence is imputed to volatility which may be due to structural breaks (Lamoureux and Lastrapes, 1990). POULIASIS ET AL.| 431 differ for different hedging horizons (weekly and monthly), between long and short hedges, during seasons (winter and summer) and across market conditions (backwardation and contango).Finally, we also assess hedging performance under the supposition of partially predictable spot prices. Ederington and Salas (2008) postulate that, when spot prices are partially predictable, traditional regression estimates the OHR inefficiently, leading to upward-biased riskiness levels for both hedged and unhedged positions and downward-biased risk reduction levels. Martínez and Torró (2015) consider seasonality when OHRs are computed in European gas markets. Their results indicate that hedging effectiveness is much higher when the seasonal pattern in spot price changes is approximated with the basis (futures-spot spread). Fama and French (1987) and Viswanath (1993) also find that the basis is a useful predictor of future changes in spot commodity prices.The structure of this paper is as follows. Section 2 presents the OHR methodology and demonstrates the forecast combination procedure. In Section 3, the data and their properties are described. Section 4 discusses the empirical results and evaluates the hedging effectiveness of the proposed strategies. The reality check for superior hedging ability is also discussed, while this section presents information on weight and hedge ratio stability, transaction costs, performance fees, downside risk and segmentation to market conditions. Last section concludes.How to cite this article: Pouliasis PK, Visvikis ID, Papapostolou NC, Kryukov AA. A novel risk management framework for nat...

“…Although common forces drive asset price returns and asset-specific market conditions (in our case shipping stock market states), we do not lose valuable information such as correlation breakdowns that might arise from the possibility that two segments of the industry might not be simultaneously at the same state. For example, Nomikos and Pouliasis (2015) use a factor Markov switching framework and find that, when one market is in the low-and the other in the highvariance state, it is more likely to observe lower correlations.…”

Section: Conditional Correlation Structurementioning

confidence: 99%

Shipping equity risk behavior and portfolio management

Transportation Research Part A: Policy and Practice

Papapostolou

Kyriakou

et al. 2018

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This paper investigates the dynamics of stock price volatility for different vessel-type segments of the U.S, water transportation industry. We measure market exposure by a portfolio of tanker, dry bulk, container, and gas stocks to examine tail behavior and tail risk dependence. The role of mixture distributions in predicting future volatility is studied from both statistical and economic perspectives. We further test for predictability in co-movements in the tails of sectors returns. Findings indicate that large losses are strongly correlated, supporting asymmetric transmission processes for financial contagion. Finally, using a non-parametric approach, we extend the model to the multivariate case and assess the value of volatility and correlation timing in optimal portfolio selection. The results can help to improve the understanding of time-varying volatility, correlation and tail systemic risk of shipping stock markets, and consequently, have implications for risk management and asset allocation practices, as well as regulatory policies.