Showing papers on "Spot contract published in 2020"

BitMEX bitcoin derivatives: Price discovery, informational efficiency, and hedging effectiveness

Abstract: BitMEX is the largest unregulated bitcoin derivatives exchange, listing contracts suitable for leverage trading and hedging. Using minute-by-minute data, we examine its price discovery and hedging effectiveness. We find that BitMEX derivatives lead prices on major bitcoin spot exchanges. Bid-ask spreads, inter-exchange spreads and relative trading volumes are important determinants of price discovery. Further analysis shows that BitMEX derivatives have positive net spillover effects, are informationally more efficient than bitcoin spot prices, and serve as effective hedges against spot price volatility. Our evidence suggests that regulators prioritise investigation of the legitimacy of BitMEX and its contracts.

Amazon EC2 Spot Price Prediction Using Regression Random Forests

Abstract: Spot instances were introduced by Amazon EC2 in December 2009 to sell its spare capacity through auction based market mechanism. Despite its extremely low prices, cloud spot market has low utilization. Spot pricing being dynamic, spot instances are prone to out-of bid failure. Bidding complexity is another reason why users today still fear using spot instances. This work aims to present Regression Random Forests (RRFs) model to predict one-week-ahead and one-day-ahead spot prices. The prediction would assist cloud users to plan in advance when to acquire spot instances, estimate execution costs, and also assist them in bid decision making to minimize execution costs and out-of-bid failure probability. Simulations with 12 months real Amazon EC2 spot history traces to forecast future spot prices show the effectiveness of the proposed technique. Comparison of RRFs based spot price forecasts with existing non-parametric machine learning models reveal that RRFs based forecast accuracy outperforms other models. We measure predictive accuracy using MAPE, MCPE, OOB Error and speed. Evaluation results show that $MAPE M A P E = 10 % for 66 to 92 percent and $MCPE M C P E = 15 % for 35 to 81 percent of one-day-ahead predictions with prediction time less than one second. $MAPE M A P E = 15 % for 71 to 96 percent of one-week-ahead predictions.

Modeling Co-Movement among Different Agricultural Commodity Markets: A Copula-GARCH Approach

Abstract: The aim of this research is to explore the volatility contagion among different agricultural commodity markets. For this purpose, this research make use of the copula-GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model for the daily spot prices of six major agriculture grain commodities including corn, wheat, soybeans, soya oil, cotton, and oat over the period from 2000 to 2019. Our results provide evidence that significant contagion effects and risk transmissions exist among different agricultural grain commodity markets, suggesting that potential speculation effects on one agricultural market could be contagious for another agricultural market and result an increase in volatility in agricultural product markets. Second, agricultural commodities appears to co-move symmetrically. We also find substantial extreme co-movements among agricultural commodity markets. This indicates that agricultural commodity markets tend to crash (boom) together during extreme events. Moreover, after the food crisis, contagion effects and risk transmissions among different agricultural commodity markets increased substantially. Fourth, we find that the strongest contagion effects and risk transmissions are between corn and soybeans, and the weakest contagion effects and risk transmissions are between soya oil cotton and between cotton and oat. Last, we document that the co-movement varies over time. Our findings hold important implications for modeling the co-movement by the copula-GARCH approach.

Crowdfunding, Financing Constraints, and Real Effects

Abstract: We study the feasibility and optimal design of presale crowdfunding contracts where participating consumers pay a premium above the future expected spot price and financially constrained entreprene...

An Adjusted Forward Curve for Spot Rate Forecasting

Abstract: In this paper, we provide adjustments for liquidity and credit risk to the forward Libor rate in order to improve accuracy of the forward rate in forecasting the 3-month Libor rate. In particular, we introduce the adjusted forward curve (AFC) that models the update in the forward curve from one period to the next. A direct modeling of the dynamic process of the forward curve facilitates the specification of adjustment factors to the forward curve, and it underscores the role of mean reversion (stationarity) in the nexus between the forward rate and the future spot rate. The AFC factors that underpin the forward curve bias are statistically relevant with p-values that are less than .00001. The upward bias in the forward curve (i.e., when the forward curve exceeds the expected future spot rate) positively correlates with the steepness of the yield curve in the AFC model. A downward bias positively correlates with the credit spread and industrial capacity utilization. Furthermore, the effect of the instantaneous forward curve on the future spot rate tempers off with time. The predictive power of the AFC model, however, hinges on the forecastability of the underlying factors. The testing indicates that all the AFC model factors have a mean reversion component. Overall, our model effectively anticipates movements in the forward curve that tend to yield a better forecast of the future spot rate.

The Impact of COVID-19 on Gold Price Volatility

Abstract: Purpose: This article investigates the implications of the spread of COVID-19 on gold spot prices. Design/Methodology/Approach: We use GARCH and GJR-GARCH models based on daily gold returns over the period 2012-2020 to analyze the impact of the coronavirus on the volatility of gold returns. Findings: We find a positive correlation between the increasing number of global coronavirus cases and increases in gold price. Using GARCH and GJR-GARCH models, we find a significant positive impact of COVID-19 on the conditional variance equation, indicating that the coronavirus may indeed increase the volatility of gold returns. This relates to the fact that the spread of the virus increases uncertainty with regard to the future of economic and financial markets, causing the demand for gold to increase and in turn pushing prices upwards, a trend which may be likely to continue until a vaccine or other treatments begin to stabilize the global economic outlook. Practical Implications: The issue of volatility is of significant concern to both investors and policymakers who base decisions on the relative stability of both individual financial markets and the world economy. Furthermore, volatility estimation is an essential factor in many models and has broad application to the market risk management practices of firms. Finally, understanding the volatility of the gold market is crucial for any analysis of current and future expectations regarding the risks associated with coronavirus which apply to global markets. Originality/Value: The lockdown restrictions which have been widely implemented across the globe to curb the spread of the virus have included travel prohibitions and border closures, stay-at-home and work-from-home orders, and extensive business closures, all causing immense fallout for the global economy. In the current study, we analyze for the first time the impact of the coronavirus on gold spot prices by examining their correlation with the number of cumulative global cases and daily new cases.

Price discovery and volatility spillover: an empirical evidence from spot and futures agricultural commodity markets in India

Abstract: Price discovery and spillover effect are prominent indicators in the commodity futures market to protect the interest of consumers, farmers and to hedge sharp price fluctuations. The purpose of this paper is to investigate empirically the price discovery and volatility spillover in Indian agriculture spot and futures commodity markets.,This study uses Granger causality, vector error correction model (VECM) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) to examines the price discovery and spillover effects for nine most liquid agricultural commodities in spot and futures markets traded on National Commodity and Derivatives Exchange (NCDEX).,The VECM results show that price discovery exists in all the nine commodities with futures market leading the spot in case of six commodities, namely soybean seed, coriander, turmeric, castor seed, guar seed and chana. Whereas in case of three commodities (cotton seed, rape mustard seed and jeera), price discovery takes place in the spot market. The Granger causality tests indicate that futures markets have stronger ability to predict spot prices. Supporting these, the results from EGARCH volatility test reveal that there exist mutual spillover effects on futures and spot markets. Thus, it could be inferred that futures market is more efficient in price discovery of agricultural commodities in India.,These results can help the market participants to benefit by hedging out the uncertainty and the policymakers to design futures contracts to improve the efficiency of the agricultural commodity derivatives market.,The findings provide fresh view on lead–lag relationship between future and spot prices using the latest data confirming that futures market indeed is dominant in price discovery.,There are very few studies that have explored the efficiency of the agricultural commodity spot and futures markets in India using both price discovery and volatility spillover in a detailed manner, especially at the individual agriculture commodity level.

Stock Index Futures Trading Impact on Spot Price Volatility. The CSI 300 studied with a TGARCH model

Abstract: A TGARCH modeling is argued to be the optimal basis for investigating the impact of index futures trading on spot price variability. We discuss the CSI-300 index (China-Shanghai-Shenzhen-300-Stock Index) as a test case. The results prove that the introduction of CSI-300 index futures (CSI-300-IF) trading significantly reduces the volatility in the corresponding spot market. It is also found that there is a stationary equilibrium relationship between the CSI-300 spot and CSI-300-IF markets. A bidirectional Granger causality is also detected. “Finally”, it is deduced that spot prices are predicted with greater accuracy over a 3 or 4 lag day time span.

Futures market hedging efficiency in a new futures exchange: Effects of trade partner diversification

Abstract: This paper uses transaction data to examine hedging efficiency in a new futures exchange; the Fish Pool salmon futures exchange in Norway. The paper utilizes data on firm‐level exporter/importer transaction prices to quantify firm‐level futures hedging efficiency. This allows us to address heterogeneity in hedging efficiency and basis risk at the firm level. The main result of this paper shows that larger firms with greater trade partner diversification have lower basis risk. Such firms align their internal transaction price closer to the common spot price in the market, which encourages greater futures market participation. Results are discussed in light of recent declines in participation in the salmon futures exchange.

On entry cost dynamics in Australia’s National Electricity Market

Abstract: In theory, well designed electricity markets should deliver an efficient mix of technologies at least-cost. But energy market theories and energy market modelling are based upon equilibrium analysis and in practice electricity markets can be off-equilibrium for extended periods. Near-term spot and forward contract prices can and do fall well below, or substantially exceed, relevant entry cost benchmarks and associated long run equilibrium prices. However, given sufficient time higher prices, on average or during certain periods, create incentives for new entrant plant which in turn has the effect of capping longer-dated average spot price expectations at the estimated cost of the relevant new entrant technologies. In this article, we trace generalised new entrant benchmarks and their relationship to spot price outcomes in Australia’s National Electricity Market over the 20-year period to 2018; from coal, to gas and more recently to variable renewables plus firming, notionally provided by—or shadow priced at—the carrying cost of an Open Cycle Gas Turbine. This latest entry benchmark relies implicitly, but critically, on the gains from exchange in organised spot markets, using existing spare capacity. As aging coal plant exit, gains from exchange may gradually diminish with ‘notional firming’ increasingly and necessarily being met by physical firming. At this point, the benchmark must once again move to a new technology set…

Scheduling Flexible Demand in Cloud Computing Spot Markets

Abstract: The rapid standardization and specialization of cloud computing services have led to the development of cloud spot markets on which cloud service providers and customers can trade in near real-time. Frequent changes in demand and supply give rise to spot prices that vary throughout the day. Cloud customers often have temporal flexibility to execute their jobs before a specific deadline. In this paper, the authors apply real options analysis (ROA), which is an established valuation method designed to capture the flexibility of action under uncertainty. They adapt and compare multiple discrete-time approaches that enable cloud customers to quantify and exploit the monetary value of their short-term temporal flexibility. The paper contributes to the field by guaranteeing cloud job execution of variable-time requests in a single cloud spot market, whereas existing multi-market strategies may not fulfill requests when outbid. In a broad simulation of scenarios for the use of Amazon EC2 spot instances, the developed approaches exploit the existing savings potential up to 40 percent – a considerable extent. Moreover, the results demonstrate that ROA, which explicitly considers time-of-day-specific spot price patterns, outperforms traditional option pricing models and expectation optimization.

Cloud spot instance price prediction using k NN regression

Abstract: Cloud computing can provide users with basic hardware resources, and there are three instance types: reserved instances, on-demand instances and spot instances. The price of spot instance is lower than others on average, but it fluctuates according to market demand and supply. When a user requests a spot instance, he/she needs to give a bid. Only if the bid is not lower than the spot price, user can obtain the right to use this instance. Thus, it is very important and challenging to predict the price of spot instance. To this end, we take the most popular and representative Amazon EC2 as a testbed, and use the price history of its spot instance to predict future price by building a k-Nearest Neighbors (kNN) regression model, which is based on our mathematical description of spot instance price prediction problem. We compare our model with Linear Regression (LR), Support Vector Machine Regression (SVR), Random Forest (RF), Multi-layer Perception Regression (MLPR), gcForest, and the experiments show that our model outperforms the others.

The Correlation Analysis of Futures Pricing Mechanism in China’s Carbon Financial Market

Abstract: China, taking the concept of sustainable development as the premise, puts forward Intended Nationally Determined Contributions (INDC) to reduce the greenhouse gas emissions in response to climate change. In this context, with the purpose of seeking solutions to a carbon financial market pricing mechanism to build China’s carbon finance market actively and thus achieving the goal of sustainable development, this paper, based on the autoregressive integrated moving average (ARIMA) model, established a carbon price prediction model for the carbon financial market, and studied the relationship between Certified Emission Reduction (CER) futures prices and spot prices, as well as the relationship between European Union allowances (EUA) futures prices and CER futures prices in an empirical manner. In this paper, EUA and CER futures prices of the European Climate Exchange (ECX) and EUA and CER spot prices of the BlueNext Environmental Exchange were selected as research objects. Granger causality test, co-integration test, and ECM were used to form a progressive econometric analysis framework. The results show that firstly, the ARIMA model can effectively predict carbon futures prices; secondly, CER futures prices cannot guide spot price, and the futures pricing function does not play a role in this market; thirdly, EUA futures price can, in the short term, effectively guide the trend of CER futures prices, with the futures pricing function between the two markets. In the long run, however, the future pricing function of the two markets is not obvious. Therefore, great differences among maturity of the two markets, degree of policy influence, and market share lead to the failure of long-run futures pricing functions.

Optimal Spot Trading Integrated with Quantity Flexibility Contracts

Abstract: In this study, we consider a company that uses two channels for trading: long‐term contracts and spot markets. With a quantity flexibility long‐term contract, the buyer commits to purchasing at least as much as the minimum order quantity and is able to reserve capacity with the forward contract supplier up to the maximum order quantity in each period at a predetermined price. Through the spot market, the buyer can order or sell inventory at an uncertain future spot price without quantity limitations. A fixed setup cost is incurred in each instance of buying or selling in the spot market. This study considers very general inventory‐related (not necessarily convex) costs, and demand is random following a one‐sided Polya distribution. We introduce a concept called “non‐(K1, K2)‐decreasing with changeovers” and characterize the optimal trading policy in the spot market. We show that the optimal policy can be characterized by five critical points. We also characterize optimal procurement from the contracted supplier under certain conditions. Two simple, yet efficient, heuristics that yield near‐optimal results in many cases are presented as well in order to calculate procurement quantities. Moreover, we show that our results also apply to uniform demand, which is not Polya, and numerical tests assuming uniform demand are performed to discern which factors are most important and to gain managerial insights.

High-Resolution Electricity Spot Price Forecast for the Danish Power Market

Abstract: Energy markets with a high penetration of renewables are more likely to be challenged by price variations or volatility, which is partly due to the stochastic nature of renewable energy. The Danish electricity market (DK1) is a great example of such a market, as 49% of the power production in DK1 is based on wind power, conclusively challenging the electricity spot price forecast for the Danish power market. The energy industry and academia have tried to find the best practices for spot price forecasting in Denmark, by introducing everything from linear models to sophisticated machine-learning approaches. This paper presents a linear model for price forecasting—based on electricity consumption, thermal power production, wind production and previous electricity prices—to estimate long-term electricity prices in electricity markets with a high wind penetration levels, to help utilities and asset owners to develop risk management strategies and for asset valuation.

Beyond Basis Basics: Liquidity Demand and Deviations from the Law of One Price

Abstract: We argue that deviations from the law of one price between futures and spot prices, known as bases, capture important information about liquidity demand for equity market exposure in global equity index futures markets. We show that bases (1) co-move with dealer and investor futures positions, (2) are contemporaneously positively correlated with spot and futures markets with the same sign, and (3) negatively predict futures and spot market returns with the same sign. These findings are uniquely consistent with our liquidity demand model and distinct from other explanations for bases, such as arbitrage opportunities or intermediary balance sheet costs. We show persistent supply-demand imbalances for equity index exposure reflected in bases, where compensation for meeting liquidity demand for that exposure is large (5-6% annual premium).

Determinants of the Forward Premium in the Nord Pool Electricity Market

Abstract: Nord Pool is the leading power market in Europe. It has been documented that the forward contracts traded in this market exhibit a significant forward premium, which could be a sign of market inefficiency. Efficient power markets are important, especially when there is a goal to increase the share of the power mix stemming from renewable energy sources. We therefore contribute to the understanding of this topic by examining how the forward premium in the Nord Pool market depend on several economic and physical conditions. We utilise two methods: ordinary least squares and quantile regression. The results show that the reservoir level and the basis (the difference between the forward and spot price) have a significant impact on the forward premium. The realised volatility of futures prices and the implied volatility of the stock market have strong effects on both the conditional lower and upper tails of the forward premium. We also find that, as the market has matured, the forward premium has decreased, indicating an increase in market efficiency.

Electricity Spot Prices Forecasting for MIBEL by using Deep Learning: a comparison between NAR, NARX and LSTM networks

Abstract: In the latest times, power markets in Europe, including the Spanish one called MIBEL (Mercado Iberico de Electricidad), are being deregulated and coupled. As a result, electricity can be easily purchased and sold across further areas and countries. On the other hand, trying to guarantee renewable projects profitability, Power Purchase Agreements and Options contracts are arising as a feasible solution. The problem arises when the power plant owners have to negotiate the purchase electricity price in order to optimize risks and profits, as well as make future plans. Thus, several methods for Electricity Price Forecasting (EPF) have been developed and presented, showing different results, as market spot prices suffer from strong seasonality, spikes and high volatility. In this paper, three methods, based on Deep Learning Dynamic Neural Networks (NAR, NARX and LSTM) applied to forecast MIBEL electricity spot prices are discussed in order to evaluate their adequacy, accuracy and reliable horizon.