Showing papers on "Spot contract published in 2019"
TL;DR: In this paper, the Chicago Board Options Exchange and the Chicago Mercantile Exchange introduced futures contracts on bitcoin and investigated to what extent they provide useful information for the price discovery of bitcoin.
Abstract: In December 2017, both the Chicago Board Options Exchange and the Chicago Mercantile Exchange introduced futures contracts on bitcoin. We investigate to what extent they provide useful information for the price discovery of bitcoin. We rely on the information share methodology of Hasbrouck (1995, J Finance, 50, pp. 1175–1199) and Gonzalo and Granger (1995, J Bus Econ Stat, 13, pp. 27–35) and find that the spot price leads the futures price. We attribute this result to the higher trading volume and the longer trading hours of the globally distributed bitcoin spot market, compared to the relatively restricted access to the US‐based futures markets.
82 citations
TL;DR: In this paper, the authors analyzed the Bitcoin price discovery process and found that the Bitcoin futures market dominates the price discovery, and that both prices are driven by a common factor that is given by a weighted combination of the futures and spot market.
69 citations
TL;DR: In this article, the stock market and EUA prices seem to be connected, with causality going from the stock markets to EUA price, and the causality effectively runs from stock market to the European Climate Exchange market.
59 citations
TL;DR: In this article, the authors investigated the effect of wind and solar energy forecasts errors on imbalance volumes and subsequent spot electricity prices in the German electricity market and found that wind forecast errors in Germany impact spot prices more than solar forecasting errors.
58 citations
46 citations
TL;DR: In this paper, the authors assess the drivers of electricity prices in spot and futures markets, focusing on the German electricity markets, taking into account nonlinearities in electricity prices by means of structural breaks and threshold regressions.
43 citations
TL;DR: In this article, the authors studied strategic default on forward sale contracts in the international coffee market and found that roughly half of the observed defaults are strategic, and that strategic default introduces a trade-off between insurance and counterparty risk.
Abstract: This article studies strategic default on forward sale contracts in the international coffee market. To test for strategic default, we construct contract-specific measures of unanticipated changes in market conditions by comparing spot prices at maturity with the relevant futures prices at the contracting date. Unanticipated rises in market prices increase defaults on fixed-price contracts but not on price-indexed ones. We isolate strategic default by focusing on unanticipated rises at the time of delivery after production decisions are sunk and suppliers have been paid. Estimates suggest that roughly half of the observed defaults are strategic. We model how strategic default introduces a trade-off between insurance and counterparty risk: relative to indexed contracts, fixed-price contracts insure against price swings but create incentives to default when market conditions change. A model calibration suggests that the possibility of strategic default causes 15.8% average losses in output, significant dispersion in the marginal product of capital, and sizable negative externalities on supplying farmers.
40 citations
TL;DR: A hybrid model that combines the results from multiple linear regression (MLR) model with an auto-regressive integrated moving average (ARIMA) and Holt–Winters models for better forecasts is presented.
Abstract: Accurate forecast of the hourly spot price of electricity plays a vital role in energy trading decisions. However, due to the complex nature of the power system, coupled with the involvement of multi-variable, the spot prices are volatile and often difficult to forecast. Traditional statistical models have limitations in improving forecasting accuracies and reliably quantifying the spot electricity price under uncertain market conditions. This paper presents a hybrid model that combines the results from multiple linear regression (MLR) model with an auto-regressive integrated moving average (ARIMA) and Holt-Winters models for better forecasts. The proposed method is tested for the Iberian electricity market data set by forecasting the hourly day-ahead spot price with dataset duration of 7, 14, 30, 90, and 180 days. The results indicate that the hybrid model outperforms the benchmark models and offers promising results under most of the testing scenarios.
35 citations
TL;DR: In this article, the causal relationship between agricultural products and oil markets was investigated for the period January 2000-July 2018, monthly spot prices of 15 commodities are examined, including Brent crude oil, biofuel-related agricultural commodities, and other agricultural commodities.
Abstract: The food-energy nexus has attracted great attention from policymakers, practitioners, and academia since the food price crisis during the 2007–2008 Global Financial Crisis (GFC), and new policies that aim to increase ethanol production. This paper incorporates aggregate demand and alternative oil shocks to investigate the causal relationship between agricultural products and oil markets. For the period January 2000–July 2018, monthly spot prices of 15 commodities are examined, including Brent crude oil, biofuel-related agricultural commodities, and other agricultural commodities. The sample is divided into three sub-periods, namely: (i) January 2000–July 2006, (ii) August 2006–April 2013, and (iii) May 2013–July 2018. The structural vector autoregressive (SVAR) model, impulse response functions, and variance decomposition technique are used to examine how the shocks to agricultural markets contribute to the variance of crude oil prices. The empirical findings from the paper indicate that not every oil shock contributes the same to agricultural price fluctuations, and similarly for the effects of aggregate demand shocks on the agricultural market. These results show that the crude oil market plays a major role in explaining fluctuations in the prices and associated volatility of agricultural commodities.
34 citations
TL;DR: Even though commodity-pricing models have been successful in fitting the term structure of futures prices and its dynamics, they do not generate accurate true distributions of spot prices as discussed by the authors, which is a limitation of the model.
Abstract: Even though commodity-pricing models have been successful in fitting the term structure of futures prices and its dynamics, they do not generate accurate true distributions of spot prices. This pap...
27 citations
TL;DR: In this paper, the break-even price for rigs used to drill oil wells is found, and the effect of price volatility on rig activity declines as the price for crude oil or natural gas moves above or below this BEP, firms use futures prices (not spot prices) to plan exploration and development, and new rig productivity affects both drilling activity and oil prices.
Abstract: Exploration of tight oil and gas formations has significantly increased US oil and gas production in recent years. However, detailed economic analysis of this production, including identification of the break-even price (BEP), the measure of price used to plan exploration and development, a synergy between price volatility and the BEP, and a feedback effect of tight oil production on oil prices, has yet to be carried out. Here we show that the BEP for rigs used to drill oil wells is $20 (~$50 nominal), the effect of price volatility on rig activity declines as the price for crude oil or natural gas moves above or below this BEP, firms use futures prices (not spot prices) to plan exploration and development, and new rig productivity affects both drilling activity and oil prices. The latter indicates that increases in new rig productivity can account for much of the 2014 oil price decline. Tight oil and gas extraction is costly and low prices lead to reduction in investments and eventually production. Here, studying the effects of price volatility on active rigs, researchers find the break-even price and show that firms use future and not spot prices to plan exploration and development investment.
TL;DR: In this paper, the authors investigated the long and short-run relationship between spot and futures prices of the energy, precious metals, and base metals markets and found that the spot prices of energy and metals assets have long-run relationships with their futures prices.
TL;DR: It is demonstrated that the proposed hybrid method can accurately forecast electricity prices containing spikes, and the price variations in the training process have been smoothened using the wavelet technique.
Abstract: Forecasting spot prices of electricity is challenging because it not only contains seasonal variations, but also random, abrupt spikes, which depend on market conditions and network contingencies. In this paper, a hybrid model has been developed to forecast the spot prices of electricity in two main stages. In the first stage, the prices are forecasted using autoregressive time varying (ARXTV) model with exogenous variables. To improve the forecasting ability of the ARXTV model, the price variations in the training process have been smoothened using the wavelet technique. In the second stage, a kernel regression is used to estimate the price spikes, which are detected using support vector machine based model. In addition, mutual information technique is employed to select appropriate input variables for the model. A case study is carried out with the aid of price data obtained from the Australian energy market operator. It is demonstrated that the proposed hybrid method can accurately forecast electricity prices containing spikes.
TL;DR: A risk aversion planning model for ADN with respect to uncertainties of wind power and electricity price is proposed and simulation studies are carried out for several IEEE test systems to validate the effectiveness of the proposed model in obtaining the optimal trade-off solution between profit and risk.
TL;DR: In this paper, the authors analyse empirically the drivers of freight market volatility and demonstrate that the relation between the volatility of futures prices and the slope of the forward curve is non-monotonic and convex, that is, it has a V-shape.
Abstract: We analyse empirically the drivers of freight market volatility. We use several macroeconomic and shipping-related factors that are known to affect the supply and demand for shipping and examine their impact on the term structure of freight options implied volatilities (IV). We find that the level of IVs is affected by the level of the spot rate, the slope of the forward curve, as well as by both demand and supply factors, especially the former. We demonstrate that the relation between the volatility of futures prices and the slope of the forward curve is non-monotonic and convex, that is, it has a V-shape. In general, anticipation of economic growth and of a stronger freight market reduces IV whereas higher uncertainty and anticipation of excess shipping capacity may increase IV. Panel regressions as well as a series of robustness tests produce strong validation of the results.
TL;DR: In this article, the authors proposed a de-seasonalised HOHMC model to capture the stylised behaviors of price evolution, especially during periods of sudden spikes driven by the abrupt changes of market sentiments.
TL;DR: In this article, a parsimonious fundamental model for the German day-ahead market is introduced, which approximates the supply stack by a piecewise linear function and considers fundamental information, e.g. power plant availabilities, must-run production and cross-border exchange.
TL;DR: This study proposes an alternative calibration procedure for the seasonality shape, where the level of futures as well as historical spot prices are simultaneously taken into account in a joint optimization approach.
Abstract: There are several approaches in the literature for the derivation of price forward curves (PFCs) which distinguish among each other by the procedure employed for the derivation of seasonality shapes, smoothing technique and by the design of the optimization procedure. However, a comparative study to highlight the strengths and weaknesses of different methods is missing. For the construction of PFCs we typically incorporate the information about market expectation from the observed futures prices and the deterministic seasonal effects of electricity prices. In most existing approaches, the seasonality shape is fitted to historically observed spot prices, and it is an exogenous input to the optimization procedure. As seasonal effects on electricity prices differ between markets, our model allows a more general and flexible definition of the seasonality shape. In this study, we propose an alternative calibration procedure for the seasonality shape, where the level of futures as well as historical spot prices are simultaneously taken into account in a joint optimization approach. We discuss comparatively the features of existing methods for PFCs, and highlight the advantages of our optimization procedure.
TL;DR: In this paper, the authors examined the transmission mechanisms and dynamic spillover effects between gold spot prices and US equity prices following the 2007 Global Financial Crisis (GFC) and examined the impact of the spillover effect on gold prices.
Abstract: Purpose—The purpose of this paper is to examine the transmission mechanisms and dynamic spillover effects between gold spot prices and US equity prices following the 2007 Global Financial Crisis. I...
TL;DR: A power portfolio optimization methodology is proposed considering the bidding behaviours in spot markets, independent system operator (ISO) centralized dispatching, and the cross-region GRTs and the numerical results illustrate the effect of GRTs for income assurance of Gencos and the necessity to consider the stochastic contingencies in portfolio decisions.
TL;DR: In this article, the authors test the consistency of gas futures and spot prices for unbiasedness and conclude that the scheduled deregulation may improve the dominance of Gazprom in the domestic market.
TL;DR: In this article, the authors review and synthesize the empirical evidence on several factors related to petroleum product prices: (1) the general distributional characteristics of product prices, (2) the influence of refinery outages, extreme weather, and similar circumstances on product prices; (3) the way that price discovery occurs for petroleum products; (4) the predictive accuracy of petroleum product futures prices for future spot prices; and (5) the impact of speculation on product price.
TL;DR: In this paper, the impacts of electricity market variations on the Nordic stock market returns using hourly observations of electricity spot prices pairwise in aggregate market index and some sector indexes were investigated.
TL;DR: Wang et al. as mentioned in this paper proposed a new round of revolution in power sector to introduce electricity futures into China with the expectation of perfecting the market and providing a proper hedging tool for renewable plants.
TL;DR: In this article, the authors examined the time varying correlation and nonlinear causality among Google Search Trends for gold, gold spot price in India, the Indian stock market index Nifty and the USDINR exchange rate.
TL;DR: In this article, a transition in pricing mechanism from the annual negotiated price to the one based on spot market price was discussed. But the authors did not consider the impact of the BDI on the price of iron ore.
Abstract: Baltic Dry Index (BDI) is often included in the iron ore spot price. Iron ore market experienced a transition in pricing mechanism from the annual negotiated price to the one based on spot market p...
TL;DR: In this article, weekly data from June 1986 to October 2018 was used to investigate the asymmetric relationship between oil prices (WTI Spot Price) and gasoline prices (New York Harbor Convent).
Abstract: In this paper, weekly data from June 1986 to October 2018 is used to investigate the asymmetric relationship between oil prices (WTI Spot Price) and gasoline prices (New York Harbor Convent...
TL;DR: Wang et al. as mentioned in this paper proposed a novel definition of time delay based on multiscale composite complexity synchronization analysis, and applied it to investigate whether stock markets have delayed reaction to crude oil markets' large volatility or not.
Abstract: Crude oil markets play an important role in the international economy, and shocks of crude oil prices have significant effects on various economic activities. In this paper, a novel definition of time delay based on multiscale composite complexity synchronization analysis is proposed in this work, and it is applied to investigate whether stock markets have delayed reaction to crude oil markets’ large volatility or not. The effect of crude oil energy markets on stock markets is investigated, and the data of West Texas Intermediate crude oil spot price, Europe Brent spot price, China Daqing spot price, Shanghai Stock Exchange Composite Index and Standard & Poor’s 500 Index are selected for the empirical research. Then, based on the multiscale time delay between crude oil markets and financial markets, the linkage synchronization and correlation relationship between crude oil markets and stock markets are measured by cross recurrence quantification analysis, and deep canonical correlation analysis which is firstly utilized in this field.
01 May 2019
TL;DR: An Autoregressive Neural Network model is proposed for forecasting daily spot gas prices and shows an improvement of around 33% over ARIMA in terms of mean squared error when price forecasts are used in gas purchase decisions.
Abstract: Natural gas is a major global energy commodity. Gas prices around the world face substantial volatility, inducing major downside market risks. Forecasting accuracy is thus a major concern for the consumers. Traditional econometrics models do not perform well due to inherent nonlinear and nonstationary gas price data. We thus propose an Autoregressive Neural Network (ARNN) model for forecasting daily spot gas prices. The model is benchmarked against the traditional Autoregressive Integrated Moving Average (ARIMA) model. Using a cross validation study, the ARNN model showed an improvement of around 33% over ARIMA in terms of mean squared error. This improvement is significant when price forecasts are used in gas purchase decisions.
TL;DR: In this paper, a statistical test of the null hypothesis that expected forward/spot price spreads cannot be arbitraged even after accounting for transaction costs is developed, which supports the hypothesis that the introduction of purely financial participants into the California wholesale electricity market decreased the average difference and volatility of the difference between day-ahead and real-time prices, which ultimately lowered the total variable cost of serving demand.
Abstract: The introduction of purely financial participants into commodity markets is thought to yield forward prices that better reflect future spot prices, and ultimately, more efficient future production and consumption decisions. However, there are sizable transaction costs associated with trading in most commodity markets. This paper develops a statistical test of the null hypothesis that expected forward/spot price spreads cannot be arbitraged even after accounting for these transactions costs. We apply this test to hourly, location-specific day-ahead and real-time prices from California's wholesale electricity market. The implied trading cost required to reject the null hypothesis of no profitable arbitrage opportunities falls significantly after California allowed purely financial participation. Moreover, variable input costs per MWh of electricity produced fell by 3.6% in high demand hours after the introduction of purely financial participants. Combined, our evidence supports the hypothesis that the introduction of purely financial participants into the California wholesale electricity market decreased the average difference and the volatility of the difference between day-ahead and real-time prices, which ultimately lowered the total variable cost of serving demand