Future Spot Research Articles

Nominal exchange rates and heterogeneous beliefs

We propose a two-country model with heterogeneous beliefs to understand dynamics of nominal exchange rate. Facing a shock to monetary policy, disagreement between domestic and foreign investors shifts the relative wealth of investors – an essential part of the stochastic discount factor in our model – which then moves the foreign exchange rate. Calibrated to U.S. and U.K. data, our model reveals that dispersion in beliefs predicts the future spot exchange rate, associates with the cross-section of currency risk premia, and comoves with the time-varying volatility in currency returns. Furthermore, our model suggests that domestic investors would hold fewer foreign currency-denominated bonds in countries with greater disagreements on monetary policy.

Analysis and forecasting of the dry bulk shipping market: structural VAR models using FFA-spot-time charter rates

ABSTRACT This study investigates dynamic interactive patterns of the dry bulk shipping markets through the lens of structural vector autoregression (VAR) models. There are two distinct hypotheses in the extant literature. One is that asymmetry between spot and forward freight agreement (FFA) markets causes FFA prices to respond more rapidly to new information than spot prices, termed the Physical-Paper Hypothesis in this paper. The other hypothesis is that there are some diverse shocks with different propagation mechanisms, termed the Shock-Diversity Hypothesis. This paper combines these two seemingly separate hypotheses at one time in two illustrative structural VAR models, based on a dataset of weekly spot, FFA, and time charter (TC) rates in the dry bulk shipping markets. The two structural VAR models show that spot, FFA, and TC rates have different, particularly dynamic responses to identified permanent shocks. Furthermore, they show that the reduced-form tri-variate FFA-Spot-TC VAR model produces more accurate forecasts of future spot rates than the bi-variate FFA-Spot and TC-Spot VAR models. Therefore, market participants can use VAR models as an information-processing tool for understanding of (e.g. data descriptions and structural inferences), and forecasting in, dry bulk shipping markets.

Improving Amazon EC2 Spot Instances Price Prediction using Machine Learning Algorithm

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, OOBError and speed. Evaluation results show that

Implementation of Zero Defect Manufacturing using quality prediction: a spot welding case study from Bosch

Product quality is a vital aspect in the operation of manufacturing systems, manufacturers need to implement at least one quality assurance method to assure the desired quality. The most recent approach for quality assurance is named Zero Defect Manufacturing (ZDM). The scope of the current paper is the implementation of ZDM approach in the automotive industry and specifically for the spot-welding process. Using a machine learning method that is utilizing linear regression and LSTM and consuming data from the production such as sensors and other engineering data to predict the quality of future spot welds. Using this quality prediction there is a root cause analysis to identify why the spot weld will fail and the appropriate prevention actions are proposed. The next step is the training and validation of the machine learning model and the calculation of the accuracy of the model. Once the accuracy of the model is validated a series of simulations, using a dynamic scheduling tool, are performed in order to calculate a series of KPIs to evaluate the impact of the proposed method to the production.

On the Bias of the Unbiased Expectation Theory

The unbiased expectation theory stipulates that long-term interest rates are determined by the market’s expectations of future short-term interest rates. According to this hypothesis, if investors have unbiased expectations about future interest rate movements, the forward interest rates should be good predictors of future spot interest rates. This hypothesis of the term structure of interest rates has long been a subject of debate due to empirical and theoretical challenges. Despite extensive research, a satisfactory explanation for the observed systematic difference between future spot interest rates and forward interest rates has not yet been identified. In this study, we approach this issue from an arbitrage theory perspective, leveraging on the connection between the expectation hypothesis and changes in probability measures. We propose that the observed bias can be explained by two adjustments: a risk premia adjustment, previously considered in the literature, and a stochastic adjustment that has been overlooked until now resulting from two measure changes. We further demonstrate that for specific instances of the Vasicek and Cox, as well as the Ingersoll and Ross, stochastic interest rate models, quantifying these adjustments reveals that the stochastic adjustment plays a significant role in explaining the bias, and ignoring it may lead to an overestimation of the required risk premia/aversion adjustment. Our findings extend beyond the realm of financial economic theory to have tangible implications for interest rate modelling. The capacity to quantify and distinguish between risk and stochastic adjustments empowers modellers to make more informed decisions, leading to a more accurate understanding of interest rate dynamics over time.

Can the Basis Lead to Arbitrage Profits on the MISO Exchange?

Previous research has shown the basis contains information about future spot prices in wholesale electricity markets. This paper examines the profitability of a trading rule that utilizes the basis on the Midcontinent Independent System Operator (MISO) exchange. 24 hourly time series regressions across each of MISO’s eight regional hubs are estimated to determine the rule’s effectiveness. 67 of the 192 total regressions yield statistically significant results. The number of significant results as well as the size of the returns tend to be highest during the afternoon through early evening hours, which calls into question the efficiency of the market during peak demand times.

Can Forward Commodity Markets Improve Spot Market Performance? Evidence from Wholesale Electricity

Forward markets are believed to aggregate information about future spot prices and reduce the cost of producing the commodity. We develop a measure of the extent to which forward and spot prices agree in markets with transaction costs. Using this measure, we show that day-ahead prices better reflect real-time prices at all locations in California’s electricity market after the introduction of financial trading. We then present evidence suggesting that operating costs and input fuel use fell after the introduction of financial trading on days when the nonconvexities inherent to the production and transmission of electricity are especially relevant. (JEL D23, D24, G13, L94, L98, Q48)

AN EMPIRICAL ANALYSIS OF PRICEDISCOVERY EFFICIENCY OF THE MAIZE FUTURESMARKET IN INDIA

Purpose - The present study examines the efficacy of the maize futures market of India in forecasting future spot pricesfor April 2020-March 2022. Design/methodology/approach - In this study, Granger causality tests and the Vector Auto Regression (VAR) model are used. The initial application of the Augmented Dickey-Fuller (ADF) test was to observe the stationarity in the spot and futures price series. Findings - The findings specified that allthe variables are stationary at the point of the first difference.According to the VAR model, neither the lag value of futures nor the same spot price of maize has a substantial impact on each other. Furthermore, the Granger Causality test suggested that the futures market has an insignificant ability to predict subsequent spot prices of maize in India. Originality/value - The results of this study will be beneficial for different players namely hedgers, speculators, commodity exchanges, policymakers, researchers, etc. who have a noteworthy interest in the agricultural commoditymarkets.

Cost Minimizing Reservation and Scheduling Algorithms for Public Clouds

Cloud Service Providers offer various pricing schemes to charge for their computational resources. Cloud Service Users can opt for on-demand, reserved, or spot instances for their requirements. The overall cost of an application depends on the instances chosen to execute the job. Finding the optimal reservation amount is a significant research problem, and it is more challenging when one uses spot instances with unpredictable prices. We have proposed two algorithms to determine the reservation amount, and for both algorithms, the future spot prices are unknown. But the first algorithm assumes that future demands are known. The second algorithm does not make any such assumption and yet can ensure that the cost of reservation and usage of cloud resources is within a factor 2 - u_h/e_c of the optimal cost where u_h is the usage cost per hour of the reserved instance and e_c is the average cost per hour for unreserved instances. We have compared our findings with that of some recent works in the literature. We have also given an Integer Linear Programming (ILP) formulation of the problem. Experimental results show that our algorithm differs in cost from ILP by less than 21%.

Considering Forward Electricity Prices for a Hydro Power Plant Risk Analysis in the Brazilian Electricity Market

The Brazilian Power System is mainly composed of renewable generation from hydroelectric and wind. Hence, spot and forward electricity prices tend to represent the inherently stochastic nature of these resources, while risk management is a measure taken by agents, especially hydro power plants (HPPs) to hedge against deep financial losses. A HPP goal is to maximize its profit considering uncertainties in forward electricity prices, spot prices, and generation scaling factor (GSF) for years ahead. Therefore, the objective of this work is to simulate the real decision-making process of a HPP, where they need to have a perspective of the forward market and future spot price assessment to negotiate forward electricity contracts. To do so, the present work models the uncertainty in electricity forward prices via two-stage stochastic programming, assessing the benefits of the stochastic solution in comparison to the deterministic one. In addition, different risk aversion levels are assessed using conditional value at risk (CVaR). An important conclusion is that the results show that the greater the HPP risk aversion is, the greater the energy selling via electricity forward contracts. Moreover, the proposed model has benefits in comparison to a deterministic approach.

A Review of Methods, and Analytical and Experimental Studies on the Use of Coal–Water Suspensions

Research in the field of building mathematical models for combustion processes has been ongoing ever since the chemical reactions of combustion were first discovered. The authors of this review have systematized mathematical models of coal–water suspension (CWS) combustion processes, the sequence of analytical and experimental studies, and have also shown the global genesis of the CWS use. In addition, this review touches upon a topic that is inextricably linked with the combustion of CWS, namely their transportation from the place of coal mining to their place of thermal utilization. For developing countries, their own energy independence is in the foreground, as it is the basis for their economic independence and also a means for other sectors of their economy to be protected from the impact of market changes in fuel prices in the future spot world market. The authors of this review explored the possibility of using Kyrgyz brown coal and transporting it through a coal pipeline from a mountainous area to an industrial site for thermal utilization in specialized steam boiler units. As the economic analysis showed, for the conditions of the Republic of Kyrgyzstan, the use of CWS and coal pipelines with rising prices for natural gas is economically justified. The recommendations of the authors are used in scientific reports and methodological recommendations for the energy and mining sectors of the Republic of Kyrgyzstan, how the recommendations can also be applied to similar conditions in the highlands of Russia, China, and India.

A hedging policy for seaborne forward freight markets based on probabilistic forecasts

Rate volatilities in seaborne freight markets lead charterers and ship owners to use financial agreements such as Forward Freight Agreements (FFA) for fixing freight rates in advance. The use of FFAs requires effective hedging policies since fixing freight rates in advance might cause both benefits and opportunity costs depending on future rate changes. We propose a data-driven hedging policy prescribing purchasing decisions for FFAs based on comparisons of FFA rates with future spot rate forecasts. The proposed approach is based on probabilistic forecasts instead of point forecasts because the most accurate forecasts in terms of predictive errors do not necessarily lead to the best decisions. We adjust spot rate forecasts by selecting percentiles that result in minimum prescriptive errors (i.e., cost) in cross-validation. Experiments on synthetic data show that the probabilistic forecast-based hedging policy outperforms the point forecast-based policies and benchmark policies, including data-driven policies from the literature. Experiments on Baltic Exchange data from 15 dry bulk and tanker routes confirm the performance of the proposed policy. Compared to two different point forecast-based policies defined in this study, the proposed approach achieves on average 3.31% and 3.02% total procurement cost reduction per route in 15 routes for four years testing period. It results in from 0.67% to 4.79% cost reductions against the benchmark policies.

The relative pricing of WTI and Brent crude oil futures: Expectations or risk premia?

This paper studies the spread of Brent-WTI futures prices using a no-arbitrage term structure model with one common and two latent idiosyncratic risk factors. We document more negative risk premia for WTI than for Brent, and the differences are more pronounced at longer maturities. The expectation of future spot price dominates the risk premium in determining the term structure of Brent-WTI futures spread, especially at short maturities. The common risk premia in both markets are negative and similar, while their corresponding idiosyncratic risk premia have opposite signs. The common risk prices of WTI and Brent are generally related to the US crude commercial stock, inflation, economic uncertainty, and hedging pressure; however, idiosyncratic risk prices are more related to their corresponding local production, short rate, and the term structure factors. The variance decomposition indicates that the idiosyncratic factors account for a considerable part at longer forecast horizons in both markets.

Antibacterial properties of lactoferrin: A bibliometric analysis from 2000 to early 2022.

BackgroundHere, a bibliometric and knowledge map analysis are used to analyze the research hot spots and development trends regarding the antibacterial effect of lactoferrin (LF). By looking for research hot spots and new topics, we provide new clues and research directions for future research.MethodsArticles and reviews regarding the antibacterial effect of LF were retrieved and from the Web of Science Core Collection (WoSCC) on 25 June 2022. CiteSpace and VOSviewer were used to conduct the bibliometric and knowledge map analysis.ResultsIn total, 8,292 authors at 2,151 institutions from 86 countries published 1,923 articles in 770 academic journals. The United States was the leader regarding research on the antibacterial effects of LF, while the Netherlands was a pioneer in conducting research in this field. The University of California system contributed the most publications. Bolscher JGM published most articles, while Wayne Bellamy had most cocitations. However, there was insufficient cooperation among the various institutions and authors. BioMetals published most LF-antibacterial activity-related articles, whereas Infection and Immunity was most commonly cocited journal. The most influential research hot spots about the antibacterial effect of LF focused on antimicrobial peptides, casein, human milk, expression, and Escherichia coli-related research. The latest hot spots and research frontier included COVID-19, antibiofilm activity, and immune defense.ConclusionsLF is a multifunctional protein with a broad spectrum of antimicrobial activities. The related field of antibacterial properties of LF will remain a research hot spot in future.

Hedging commodities in times of distress: The case of COVID‐19

This study examines the relation between the COVID‐19 pandemic and hedge efficiency in commodities futures markets. In particular, we first evaluate the informational content of commodity futures by investigating whether futures prices are accurate and unbiased predictors of future–spot prices, and then we identify key financial and real economy transmission channels associated with the pandemic. We use data of all contracts from all commodities traded at Brazilian futures markets from 2018 to 2020. We document market inefficiency and bias for all commodities. We also find that COVID‐19 has a negative correlation with hedge efficiency, and that liquidity, economic activity, export, and agriculture's employment share are transmission channels to hedge efficiency.

Informational linkage and price discovery between China's futures and spot markets: Evidence from the US–China trade dispute

This study examines the effects of the US–China trade dispute on the informational linkages and price discovery between China's futures and spot markets. Using the daily price data of four assets representing the real and financial sectors in China during 2016–2019, empirical findings suggest that the futures–spot correlations for the stock index, copper, and corn markets have increased significantly during the trade dispute. In contrast, sharp declines in the dynamic correlations between gold futures and spot markets, as gold is a safe haven asset, are observed during the event window. During uncertainty disturbance (i.e., the trade dispute), the futures–spot cointegrated relationships in the gold and corn markets are found to adjust more quickly and efficiently, whereas the correction speeds of the market deviations for the stock index and copper market are moderately slower. With the intensive integration of market expectations with uncertainty shocks, the economic shocks of trade disputes tend to remarkably improve the pricing efficiency of China's futures markets, except for the gold futures market. China's spot markets, however, seem to be more sensitive to the noise trades and information disturbances arising from the trade dispute.

Distal-less and spalt are distal organisers of pierid wing patterns

Two genes, Distal-less (Dll) and spalt (sal), are known to be involved in establishing nymphalid butterfly wing patterns. They function in several ways: in the differentiation of the eyespot’s central signalling cells, or foci; in the differentiation of the surrounding black disc; in overall scale melanisation (Dll); and in elaborating marginal patterns, such as parafocal elements. However, little is known about the functions of these genes in the development of wing patterns in other butterfly families. Here, we study the expression and function of Dll and sal in the development of spots and other melanic wing patterns of the Indian cabbage white, Pieris canidia, a pierid butterfly. In P. canidia, both Dll and Sal proteins are expressed in the scale-building cells at the wing tips, in chevron patterns along the pupal wing margins, and in areas of future scale melanisation. Additionally, Sal alone is expressed in the future black spots. CRISPR knockouts of Dll and sal showed that each gene is required for the development of melanic wing pattern elements, and repressing pteridine granule formation, in the areas where they are expressed. We conclude that both genes likely play ancestral roles in organising distal butterfly wing patterns, across pierid and nymphalid butterflies, but are unlikely to be differentiating signalling centres in pierids black spots. The genetic and developmental mechanisms that set up the location of spots and eyespots are likely distinct in each lineage.

An empirical study on the relationship between canola oil futures and spot yields in China

This paper selects canola oil futures log returns and spot log returns from 2016 to March 2022 in China, and empirically analyzes the relationship between the two based on VAR model, Grand causality test, impulse response, and variance decomposition. The results show that: there is a regression relationship between Chinese rapeseed oil futures returns and spot returns, and they influence each other; the impact of rapeseed oil spot returns on futures returns is weak, and rapeseed oil futures returns have a stronger leading effect on spot returns; the impact between both rapeseed oil futures returns and spot returns is short-term, and the duration is not too long.

The information in global interest rate futures contracts

AbstractWe investigate information contained in the term structure of interest rate futures contracts in the United States, Eurozone, UK, and Switzerland. We find that current forward‐spot differentials often predict return premiums and, especially, future spot rates. This predictability follows time‐series patterns common to all four markets, except around crises. Macroeconomic indicators are important determinants of predictability within and between markets. One common factor captures a significant portion of variation in predictability. No single market has a dominant share of macroeconomic indicators linked with the common predictability factor. Inflation and exchange rates arise as the most important determinants of the common factor.

MAPPING APPLE TREES FUTURE LAND USE POTENTIAL AS A MEANS OF CLIMATE CHANGE ADAPTATION IN EAST-MEDITERRANEAN MOUNTAINS: MOUNT-LEBANON

Agricultural zonal migrations and altitudinal shifts of high chill requirements fruit trees such as apple trees is considered a way of adaptation to climate change in mountain agriculture. This study examines near and far future options (2050-2070) of this local adaptation method in four village clusters in Mount-Lebanon, involving the expansion of agricultural lands to suitable regions under different degrees of climate change scenarios of temperature increase and precipitations regime fluctuation. A Geographic Information System (GIS) mapping calculation model was established for agricultural land evaluation which aims to locate spaces where the agriculture development indicators such as soil type, slope, future temperatures, and future precipitations will be suitable for cultivation under different climate change scenarios and models. The model does not seek the exact delineation of plots as much as the location of areas with a trend of agricultural relevance in the next 30 to 50 years. This classification is a tool to help Mount-Lebanon farmers and apple growers in adapting locally to climate change by choosing the best future spots to migrate their crops to. Results showed that most lands in which agricultural development is viable, are already in use for apple production (mainly) in the 4 clusters, leaving small parcels of land with variable agro-potentials to be developed in the future under favorable climate conditions. The agriculture potential of plots of altitude exceeding 2000 meters is to be validated in the studied area, especially since the climatic and irrigation conditions of there can present serious challenges.