Showing papers on "Spot contract published in 2021"
TL;DR: In this article, the performance of an ensemble-based technique for forecasting short-term electricity spot prices in the Italian electricity market (IPEX) is examined, based on three standard accuracy measures, the results indicate that the ensemble-Based model outperforms the others, while the random forest and ARMA are highly competitive.
Abstract: Efficient modeling and forecasting of electricity prices are essential in today’s competitive electricity markets. However, price forecasting is not easy due to the specific features of the electricity price series. This study examines the performance of an ensemble-based technique for forecasting short-term electricity spot prices in the Italian electricity market (IPEX). To this end, the price time series is divided into deterministic and stochastic components. The deterministic component that includes long-term trends, annual and weekly seasonality, and bank holidays, is estimated using semi-parametric techniques. On the other hand, the stochastic component considers the short-term dynamics of the price series and is estimated by time series and various machine learning algorithms. Based on three standard accuracy measures, the results indicate that the ensemble-based model outperforms the others, while the random forest and ARMA are highly competitive.
37 citations
TL;DR: The findings generally show that market participants could perceive and assimilate market changes and adjust their expectations, which restrained the impacts that should have occurred within the oil price war.
36 citations
TL;DR: Wang et al. as discussed by the authors investigated dynamic correlations between Chinese crude oil futures and spot prices of its two main underlying assets, OPEC and Oman, as well as the hedging effectiveness.
33 citations
TL;DR: In this article, a hybrid forecasting model of monthly Henry Hub natural gas prices based on variational mode decomposition (VMD), particle swarm optimization (PSO), and deep belief network (DBN) is proposed.
27 citations
TL;DR: In this paper, the impact of renewable energy resources in the Colombian wholesale electricity market is evaluated using a counterfactual scenario based on a structural model of an energy firm's behavior that offers 1000MW in 2018.
25 citations
TL;DR: In this article, the authors examined the performance of deep learning models for predicting the spot prices of five major agricultural commodities (cotton seed, castor seed, rape mustard seed, soybean seed, and guar seed) on the National Commodity and Derivatives Exchange.
Abstract: Food price fluctuations can impact both producers and consumers. Forecasting the prices of the agricultural commodities is of prime concern not only to the government but also to farmers and agribusiness firms. In developing countries like India, management of food security needs competent and efficient forecasting of food prices. With the availability of data, recent innovation in deep‐learning models provides a feasible solution to accurately forecast the prices. In this study, we examine the superiority of these models using the daily spot prices of five major commodities traded on the National Commodity and Derivatives Exchange: cotton seed, castor seed, rape mustard seed, soybean seed, and guar seed. The results were obtained from the application of the traditional univariate autoregressive integrated moving average model and deep‐learning techniques like the time‐delay neural network (TDNN) and long short‐term memory (LSTM) network. The empirical results indicate that the LSTM model is indeed suitable for the financial domain and captures the directional movement of the spot price changes with high accuracy compared with the TDNN and other linear models. Accuracy of the performance of these models has been compared using out‐of‐sample performance measure. The overall objective of this paper is to demonstrate the utility of spot price forecasting for farmers and traders in offering them the best predictions of the price movements. Our results provide a possibility of developing pricing models that can help in fairly regulating agricultural commodity prices.
20 citations
TL;DR: In this article, the authors compare the responses of G20 stock markets to the double blow of COVID-19 and the historic oil price shock, by considering two different periods with distinct anxiety levels, namely the pre- and the post- COVID19, and explore the volatility spillovers between the spot price of crude oil and the stock markets.
19 citations
TL;DR: In this article, the authors examined the time-varying price spillovers and dependencies between iron ore, scrap steel, carbon emissions, seaborne transportation, and China's steel stock by using price spillover and copula models to cover the period 2011-2020.
17 citations
TL;DR: In this paper, a brief history of the Chilean electricity market and explain its main limitations going forward is provided. But the main limitations of the original electricity market design remain unchanged, including the use of a cost-based mechanism for spot transactions based on a merit-order curve, low temporal granularity of spot prices, missing forward markets to settle deviations from day-ahead commitments, inefficient pricing of greenhouse gas emissions due to administrative rules and a capacity mechanism that does not reflect a clear resource adequacy target.
16 citations
TL;DR: In this article, the authors examined the relationship between spot and futures prices in the Indian commodity market for the period 2009 to 2020 and used ARDL bounds-testing technique to explore the long-run relationship between these two prices.
16 citations
TL;DR: Surprisingly, it is observed that risk aversion increases the profit and reduces firms’ risk, and that the consumer utility is higher in the scenarios in which retailers behave a la Cournot in the wholesale market.
TL;DR: In this article, the authors investigated the existence of bubbles in the shale gas sector and found that the bubbles are inconsistent with the natural gas spot price and WTI crude oil price.
TL;DR: In this paper, an optimal dispatch model is proposed to examine two different business cases for wind operators alongside the existence (or not) of subsidised Contracts for Difference (CDF).
TL;DR: In this paper, the effects of different market rules on wind power producer behavior were examined to identify the policy implications for a renewable-friendly spot market, for which a market equilibrium model was developed to assess power transactions in the day-ahead and balancing markets, with the upper level being wind power producers seeking the best profits, and the lower level being the market clearing process to minimize generation costs.
TL;DR: In this article, the linear and nonlinear causal relationship between economic policy uncertainty and energy prices (oil and gas future prices) was examined using wavelet coherence and wavelet phase angle tests.
TL;DR: In this article, the spot price of emission futures with a pricing model based on an arbitrage-free interval, and constructed a dual-objective optimization model that accounts for both GDP and the pollutant removal cost.
TL;DR: In this paper, a price-maker company extracts an exhaustible commodity from a reservoir, and sells it in the spot market in absence of any actions of the company, the commodity's spot price evolves as an Ornstein-Uhlenbeck process.
Abstract: A price-maker company extracts an exhaustible commodity from a reservoir, and sells it in the spot market. In absence of any actions of the company, the commodity’s spot price evolves as an Ornstein–Uhlenbeck process. While extracting, the company’s actions have an impact on the commodity’s spot price. The company aims at maximizing the total expected profits from selling the commodity, net of the total expected proportional costs of extraction. We model this problem as a two-dimensional degenerate singular stochastic control problem with finite fuel. The optimal extraction rule is triggered by a strictly decreasing smooth curve that depends on the current level of the reservoir, and for which we provide an explicit expression. Finally, our study is complemented by a theoretical and numerical analysis of the dependency of the optimal extraction strategy and value function on the model’s parameters.
TL;DR: In this article, the authors developed a commodity price model and showed that the volatility of price changes can be positively or negatively related to demand shocks and found that an inverse leverage effect was found in more than half of the daily spot prices.
TL;DR: The authors examined the role of Chinese agricultural futures markets in the price discovery process based on three well-established measurements of average price discovery contribution, and more importantly, the dynamic price discovery measurement.
TL;DR: In this article, the authors attempt to forecast the natural gas spot price 1, 3, 5, and 10 days ahead using machine learning methods: support vector machines (SVM), regression trees, linear regression, Gaussian process regression (GPR), and ensemble of trees.
Abstract: The ability to accurately forecast the spot price of natural gas benefits stakeholders and is a valuable tool for all market participants in the competitive gas market. In this paper, we attempt to forecast the natural gas spot price 1, 3, 5, and 10 days ahead using machine learning methods: support vector machines (SVM), regression trees, linear regression, Gaussian process regression (GPR), and ensemble of trees. These models are trained with a set of 21 explanatory variables in a 5-fold cross-validation scheme with 90% of the dataset used for training and the remaining 10% used for testing the out-of-sample generalization ability. The results show that these machine learning methods all have different forecasting accuracy for every time frame when it comes to forecasting natural gas spot prices. However, the bagged trees (belonging to the ensemble of trees method) and the linear SVM models have superior forecasting performance compared to the rest of the models.
TL;DR: This article used wavelet coherence analysis on global COVID-19 fear index and soft commodities spot and futures prices to investigate safe-haven properties of soft commodities over the period from January 28, 2020 to April 29, 2021.
TL;DR: In this article, the authors investigated the relationship between China's exchange rate, domestic crude oil price, and international crude oil prices using the MS-VAR model, and found that although China's oil price is strongly influenced by the international market, its effect on the international crudeoil price is weak; since the launch of INE crude oil futures in the new regime, the fluctuations in the US dollar against the RMB (USD/CNY) exchange rate has had a significant positive effect on China's crudeoil prices.
TL;DR: In this paper, Markov Chain Monte Carlo (MCMCMC) is used to model the continuous-time stochastic volatility jump-diffusion process in the context of pricing of futures contracts written on electricity spots.
TL;DR: Wang et al. as mentioned in this paper examined how news flow affects cross-market volatility spillovers and price discovery process in China's stock market and index futures market and found robust evidence confirming dominant predicting power of the stock market in the price discovery, and presence of asymmetric and persistent volatility effects.
TL;DR: Wang et al. as mentioned in this paper investigated the multiscale comovement of the dynamic correlations between copper futures and spot prices, which can provide a useful periodicity-based reference for a copper price adjustment strategy and portfolio management.
TL;DR: A game-theoretic model to value the bidirectional option in a one-manufacturer and one-component-supplier system finds that the manufacturer’s optimal firm and total order as well as expected profit under biddirectional options are larger than that of under call options, but the option quantity has the opposite tendency.
Abstract: This article develops a game-theoretic model to value the bidirectional option in a one-manufacturer and one-component-supplier system. The production process of the supplier is subject to random yield. The manufacturer contracts the supplier with bidirectional options to obtain components, and assembles them into end products to meet a stochastic demand. In addition, both firms can sell or/and buy the components on a spot market. First, the unique optimal order and production strategies of the decentralized system under bidirectional option contracts are derived. Second, resorting to numerical example and comparing the bidirectional option model with the call option model, we find that the manufacturer’s optimal firm and total order as well as expected profit under bidirectional options are larger than that of under call options, but the option quantity has the opposite tendency. The supplier’s optimal production quantity are larger under bidirectional options than that of under call options, but only when the option (exercise) price exceeds a certain value, the supplier’s expected profit under bidirectional options will be larger than that of under call options. Third, the coordination of the decentralized system under bidirectional option contracts is analyzed and a coordination mechanism with the contract is designed.
05 May 2021
TL;DR: In this article, the authors analyzed the factors that affect the price fluctuation and presented a prediction model based on the GRU(Gated Recurrent Unit) network, which can reflect recent data fluctuations better than MSE(mean square error).
Abstract: The Amazon cloud platform sells its idle resources to cloud users as Spot Instances, which provide an ultra-low discount compared to the price of on-demand instances. Unlike the pricing strategy of the on-demand and reserved instances that use fixed price, the price of Spot Instances is dynamically changed, which introduce an interesting research topic of price prediction. In this paper, we firstly analyze the actual price distribution on a 90 days Amazon spot price history data downloaded from the Amazon Cloud platform, by using the parameter k-AMSE to represent the price fluctuation of a spot instance which can reflect recent data fluctuations better than MSE(mean square error). Then, We analyzed the factors that affect the price fluctuation and presented a prediction model based on the GRU(Gated Recurrent Unit) network. We compare the proposed algorithm with others and evaluate it with RMSE (root mean square error) measurement. The experiment results show that the GRU network approach can perform over others with an accuracy rate of 1.58e-3.
TL;DR: This study finds that the optimal setting is for manufacturers to procure raw materials from dual sources if and only if the spot price uncertainty exceeds a threshold value.
TL;DR: In this article, the authors examine different scenarios with large amounts of intermittent generation to achieve close to a 100% renewable electricity market in New Zealand and use a cost based dispatch model to simulate market prices.
TL;DR: A new framework for modeling commodity forward curves that describes the dynamics of fundamental driving factors simultaneously under physical and risk-neutral probability measures, and applies the proposed modeling framework to derivatives pricing, risk management and counterparty credit risk.