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The random walk, no-change forecast is a customary benchmark in the literature on forecasting commodity prices. We challenge this custom by examining whether alternative models are more suited for this purpose. Based on a literature review and the results of two out-of-sample forecasting experiments, we draw two conclusions. First, in forecasting nominal commodity prices at shorter horizons, the random walk benchmark should be supplemented by futures-based forecasts. Second, in forecasting real commodity prices, the random walk benchmark should be supplemented, if not substituted, by forecasts from the local projection models. In both cases, the alternative benchmarks deliver forecasts of comparable and, in many cases, of superior accuracy.
Marek Kwas; Michał Rubaszek. Forecasting Commodity Prices: Looking for a Benchmark. Forecasting 2021, 3, 447 -459.
AMA StyleMarek Kwas, Michał Rubaszek. Forecasting Commodity Prices: Looking for a Benchmark. Forecasting. 2021; 3 (2):447-459.
Chicago/Turabian StyleMarek Kwas; Michał Rubaszek. 2021. "Forecasting Commodity Prices: Looking for a Benchmark." Forecasting 3, no. 2: 447-459.
This study discovered market determinants of credit default swap (CDS) spreads in the North American oil and gas industry. Due to the limited theoretical background on market sources of CDS price fluctuations, we chose to alleviate model uncertainty and possible misspecification issues using Bayesian model averaging. This robust framework allowed us to aggregate results from a vast number of linear panel models estimated over the 2017–2020 period. We identified oil price volatility, major shifts in the OPEC+ supply policy, natural gas prices and industrial metal prices as the most robust determinants of CDS spreads. We show that following the onset of the COVID-19 pandemic, oil prices ceased to be a notably important determinant of credit risk, as factors indirectly related to oil prices, such as global and sectoral uncertainty, financial conditions and the macroeconomic stance became more influential. Additionally, we show that the CDS spreads of shale companies are determined by similar common factors, but they are more sensitive to the OPEC+ decisions on the global supply and are less affected by the domestic activity. Finally, we also prove that our modelling approach may help investors and risk officers to identify robust determinants behind the dynamics of credit risk.
Karol Szafranek; Marek Kwas; Grzegorz Szafrański; Zuzanna Wośko. Common Determinants of Credit Default Swap Premia in the North American Oil and Gas Industry. A Panel BMA Approach. Energies 2020, 13, 6327 .
AMA StyleKarol Szafranek, Marek Kwas, Grzegorz Szafrański, Zuzanna Wośko. Common Determinants of Credit Default Swap Premia in the North American Oil and Gas Industry. A Panel BMA Approach. Energies. 2020; 13 (23):6327.
Chicago/Turabian StyleKarol Szafranek; Marek Kwas; Grzegorz Szafrański; Zuzanna Wośko. 2020. "Common Determinants of Credit Default Swap Premia in the North American Oil and Gas Industry. A Panel BMA Approach." Energies 13, no. 23: 6327.
This study examines whether threshold models allow to better understand the dynamic relationship between spot and futures prices for crude oil and natural gas. Our findings are threefold. First, we show that the futures curve delivers relatively accurate forecasts for energy commodity prices. Second, we provide evidence that the relationship between spot and futures prices is regime dependent but accounting for this property does not improve the quality of out-of-sample forecasts. Third, we demonstrate that using information on the dynamics of financial variables (exchange rates, stock and uncertainty indices, interest rates or industrial and precious metal prices) does not contribute to the quality of futures-based forecasts. This suggests that the predictive content of these variables is already contained in futures prices.
Michal Rubaszek; Zuzanna Karolak; Marek Kwas; Gazi Salah Uddin. The role of the threshold effect for the dynamics of futures and spot prices of energy commodities. Studies in Nonlinear Dynamics & Econometrics 2020, 1 .
AMA StyleMichal Rubaszek, Zuzanna Karolak, Marek Kwas, Gazi Salah Uddin. The role of the threshold effect for the dynamics of futures and spot prices of energy commodities. Studies in Nonlinear Dynamics & Econometrics. 2020; ():1.
Chicago/Turabian StyleMichal Rubaszek; Zuzanna Karolak; Marek Kwas; Gazi Salah Uddin. 2020. "The role of the threshold effect for the dynamics of futures and spot prices of energy commodities." Studies in Nonlinear Dynamics & Econometrics , no. : 1.
We analyse the dynamics of real prices for main non-ferrous industrial metals: aluminium, copper, nickel and zinc. The estimates based on monthly data from 1980 to 2019 show that the prices are mean reverting and the pace of mean reversion is regime dependent. The results of the out-of-sample forecasting competition provide ample evidence that mean-reverting models deliver significantly better forecasts than the naive random walk. However, allowing for non-linearity by introducing threshold structure does not lead to further improvement in the quality of forecasts.
Michał Rubaszek; Zuzanna Karolak; Marek Kwas. Mean-reversion, non-linearities and the dynamics of industrial metal prices. A forecasting perspective. Resources Policy 2019, 65, 101538 .
AMA StyleMichał Rubaszek, Zuzanna Karolak, Marek Kwas. Mean-reversion, non-linearities and the dynamics of industrial metal prices. A forecasting perspective. Resources Policy. 2019; 65 ():101538.
Chicago/Turabian StyleMichał Rubaszek; Zuzanna Karolak; Marek Kwas. 2019. "Mean-reversion, non-linearities and the dynamics of industrial metal prices. A forecasting perspective." Resources Policy 65, no. : 101538.
Marek Kwas. An optimal Monte Carlo algorithm for multivariate Feynman–Kac path integrals. Journal of Mathematical Physics 2005, 46, 103511 .
AMA StyleMarek Kwas. An optimal Monte Carlo algorithm for multivariate Feynman–Kac path integrals. Journal of Mathematical Physics. 2005; 46 (10):103511.
Chicago/Turabian StyleMarek Kwas. 2005. "An optimal Monte Carlo algorithm for multivariate Feynman–Kac path integrals." Journal of Mathematical Physics 46, no. 10: 103511.
We study the quantum summation (QS) algorithm of Brassard et al. (see Brassard et al. (in: S.J. Lomonaco, H.E. Brandt (Eds.) Quantum Computation and Information, American Mathematical Society Providence, RI, 2002)) that approximates the arithmetic mean of a Boolean function defined on N elements. We improve error bounds presented in Brassard et al. (2002) in the worst-probabilistic setting, and present new error bounds in the average-probabilistic setting. In particular, in the worst-probabilistic setting, we prove that the error of the QS algorithm using M - 1 quantum queries is 3/4 πM-1 with probability 8/π2, which improves the error bound πM-1 + π2M-2 of Brassard et al. (2002). We also present error bounds with probabilities p ∈ (½,8/π2 and show that they are sharp for large M and NM-1. In the average-probabilistic setting, we prove that the QS algorithm has error of order min{M-1,N-1/2} iff M is divisible by 4. This bound is optimal, as recently shown in Papageorgiou (Average case quantum lower bounds for computing the Boolean means, this issue). For M not divisible by 4, the QS algorithm is far from being optimal if M ≪ N1/2 since its error is proportional to M-1.
Marek Kwas; Henryk Woźniakowski. Sharp error bounds on quantum Boolean summation in various settings. Journal of Complexity 2004, 20, 669 -698.
AMA StyleMarek Kwas, Henryk Woźniakowski. Sharp error bounds on quantum Boolean summation in various settings. Journal of Complexity. 2004; 20 (5):669-698.
Chicago/Turabian StyleMarek Kwas; Henryk Woźniakowski. 2004. "Sharp error bounds on quantum Boolean summation in various settings." Journal of Complexity 20, no. 5: 669-698.
We study the quantum summation (QS) algorithm of Brassard, Høyer, Mosca and Tapp, see [1], which approximates the arithmetic mean of a Boolean function defined on N elements. We present sharp error bounds of the QS algorithm in the worst-average setting with the average performance measured in the L q norm, q∈ [l,∞].
Stefan Heinrich; Marek Kwas; Henryk Woźniakowski. Quantum Boolean Summation with Repetitions in the Worst-Average Setting. Monte Carlo and Quasi-Monte Carlo Methods 2002 2004, 243 -258.
AMA StyleStefan Heinrich, Marek Kwas, Henryk Woźniakowski. Quantum Boolean Summation with Repetitions in the Worst-Average Setting. Monte Carlo and Quasi-Monte Carlo Methods 2002. 2004; ():243-258.
Chicago/Turabian StyleStefan Heinrich; Marek Kwas; Henryk Woźniakowski. 2004. "Quantum Boolean Summation with Repetitions in the Worst-Average Setting." Monte Carlo and Quasi-Monte Carlo Methods 2002 , no. : 243-258.
Marek Kwas; Youming Li. Worst case complexity of multivariate Feynman–Kac path integration. Journal of Complexity 2003, 19, 730 -743.
AMA StyleMarek Kwas, Youming Li. Worst case complexity of multivariate Feynman–Kac path integration. Journal of Complexity. 2003; 19 (6):730-743.
Chicago/Turabian StyleMarek Kwas; Youming Li. 2003. "Worst case complexity of multivariate Feynman–Kac path integration." Journal of Complexity 19, no. 6: 730-743.