Journal of Commodity Markets最新文献

Due to major shifts in the European energy supply, a structural change can be observed in Austrian electricity spot price data starting from the second quarter of the year 2021 onward. In this work, we study the performance of two different factor models for the electricity spot price in three different time periods. To this end, we consider three samples of EEX data for the Austrian base load electricity spot price, one from the pre-crisis from 2018 to 2021, the second from the time of the crisis from 2021 to 2023, and the whole data from 2018 to 2023. For each of these samples, we investigate the fit of a classical 3-factor model with a Gaussian base signal and one positive and one negative jump signal and compare it with a 4-factor model to assess the effect of adding a second Gaussian base signal to the model.

For the calibration of the models, we develop a tailor-made Markov Chain Monte Carlo method based on Gibbs sampling. To evaluate the model adequacy, we provide simulations of the spot price as well as a posterior predictive check for the 3- and the 4-factor model. We find that the 4-factor model outperforms the 3-factor model in times of non-crises. In times of crisis, the second Gaussian base signal does not lead to a better fit of the model. To the best of our knowledge, this is the first study regarding stochastic electricity spot price models in this new market environment. Hence, it serves as a solid base for future research.

The challenges posed by climate change on the agricultural market have become a pressing concern. An accurate reading of future agricultural commodity prices can be an invaluable planning instrument for diverse interested parties. Here, we explore asset pricing implications of climate risk for the agricultural commodity market from January 2005 to December 2021. Through introducing a composite climate risk index based on the four individual climate risk measures of Faccini et al. (2023), our findings provide valuable insights into the time-series predictability of aggregate climate risk on future agricultural commodity returns, both in- and out-of-sample. This powerful predictability conveys substantial economic benefits to mean–variance investors and cannot be subsumed by conventional economic predictor variables. The evidence further suggests that physical risk, especially global warming, exhibits much stronger return predictability than transition risk. Moreover, we emphasize the pivotal role of climate risk in shaping supply dynamics and capturing investor attention, thereby serving as potential drivers of return predictability. Overall, these predictive insights hold important implications for risk management, investment strategies, and policy formulation in the agricultural commodity market.

In this paper, we look at the role of various oil jump tail risk measures as drivers of both U.S. headline and core inflation. Those measures are first computed from high-frequency oil future prices and are then introduced into standard regression models in order to (i) assess in-sample determinants of inflation, (ii) assess overtime the evolution of inflation drivers, (iii) estimate impulse response functions and (iv) forecast inflation out-of-sample for various horizons. Empirical results suggest that oil jump tail risk measures contain useful information to describe inflation dynamics, generally leading to upward inflationary pressures. Even after controlling from standard variables involved in a Phillips curve, goodness-of-fit measures show evidence of a gain, in particular for headline inflation. Overall, we observe that oil jump tail risk measures are contributing more to inflation dynamics since the Covid-19 crisis.

To understand the diversification benefit of crude oil volatility, we examine the return-volatility relation in the crude oil market, given the interaction of the volatility (VOL) and the volatility-of-volatility (VOV). We develop a novel empirical model of the crude oil price and crude oil volatility index (OVX) returns incorporating both time-varying and state-dependent variances and correlations, thus allowing us to identify distinct market regimes of VOL and VOV. We find that the behavior of the return-volatility relation is contingent on the prevailing VOV regimes. Specifically, in a low (high) VOV regime, the relation becomes less (more) negative as VOL increases. These empirical results therefore imply that the diversification benefit of crude oil volatility is far from uniform across the different market states. Finally, using our proposed empirical model, we demonstrate the economic significance of recognizing both the time-varying and state-dependent variances/correlations in portfolio risk forecasting and construction.

The aim of this paper is to investigate the existence and the nature of seasonality in LNG freight rates of different duration contract, over different market conditions (peak and troughs) for the period from December 2010 to June 2023. We employ the HEGY method and seasonal dummy variables to test for stochastic and deterministic seasonality, respectively. Then we use Markov Switching models to test for asymmetries in seasonal fluctuations across different market conditions. We reject the existence of stochastic seasonality for all freight series while results on deterministic seasonality indicate increases in rates in June, October, and November. We also found that seasonal patterns vary across market conditions, revealing that seasonal rate movements are more pronounced when the market is in downturn. Moreover, we found that the seasonal movements present similar patterns across different trading routes. The results have implications for stakeholders across the LNG value chain.