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This paper examines supplier encroachment in a scenario where the supplier procures an input in spot and processes it into a final product, while the retailer chooses between contingent and committed pricing schemes for the contract price. We find that when the correlation between the input spot price and final product demand is relatively small, it is optimal for the retailer to adopt the contingent pricing scheme regardless of the supplier's encroachment decision. This is because this scheme enables the supplier to share the input price risk, resulting in a lower contract price. Furthermore, the likelihood of the retailer adopting the contingent pricing scheme increases as demand variability decreases or spot price volatility increases. However, supplier encroachment reduces the retailer's inclination to use the contingent pricing scheme. We further demonstrate that the supplier should encroach when the correlation is sufficiently small, because encroachment alleviates double marginalisation in the retail channel and brings the additional responsiveness in the direct channel. By contrast, a large correlation compels the supplier to encroach even with a sufficiently small market size. In this case, encroachment improves the responsiveness of both retail and direct selling quantities and enables the quantities to match the demand better. [ABSTRACT FROM AUTHOR]

This study investigates the role of free cash flows and (cross-sectional and time-series) price momentum in predicting future stock returns. Past returns and free cash flows each positively predict future stock returns after controlling for the other, suggesting that cash flows and momentum both contain valuable and distinctive information about future stock returns. A strategy of buying past winners with high free cash flows and shorting past losers with low free cash flows significantly outperforms the traditional momentum trading strategy. The enhanced performance is not sensitive to investor sentiment, time variations, or transaction costs. Further analysis shows that the incremental cash flow effects are largely attributable to net distributions to equity/debt holders. Overall, our findings shed light on the role of corporate fundamentals in technical trading strategies. [ABSTRACT FROM AUTHOR]

This article investigates the impact of heterogeneous foreign investment on home market stock price informativeness. Evidence from China's nascent A-share market shows non-segmented foreign investment reduces firms' stock return synchronicity, while segmented foreign investment does not. Using the Shanghai (Shenzhen)–Hong Kong Stock Connect program as a natural experiment that exogenously increases non-segmented foreign ownership, we find that synchronicity drops significantly for program stocks relative to the control stocks. Our results are most consistent with an "informed trading" explanation, rather than a "learning" or "governance" explanation. These results have policy implications for stock market liberalization programs. [ABSTRACT FROM AUTHOR]

The Brazilian energy spot price is obtained through a chain of dynamic stochastic optimization models that works with the uncertainties related to its continental hydrogeneration-based power system. In that sense, this paper presents an information theoretic learning neural forecasting model for daily streamflow prediction of Brazilian hydroelectric power plants. More precisely, the maximum correntropy criterion was used as the error function of a multilayer perceptron. After the prediction stage, the generated outputs were used as one of the inputs of the model chain that is used to compute the hourly energy spot price in Brazil. To the best of the authors' knowledge, it is the first paper that aims to analyze the impact of the streamflow prediction on the Brazilian hourly energy spot price formation. In terms of streamflow forecast, results indicated that the predictions originated from the proposed forecasting model were equivalent to the ones from the official models, especially in the first predicted day. In the spot price graph analysis, the main result pointed that the curves provided from the modeled structure were closer to the values obtained using the actual flows than the official prices, which shows that the proposed work could produce prices more aligned to the real hydrological system conditions. From that, the study's relevance is due to the conclusion that the official process of streamflow forecasting can be improved to generated outputs more consistent with the actual system conditions, to avoid further expenses in the system operation due to potential unscheduled hydrothermal dispatches. [ABSTRACT FROM AUTHOR]

This study investigates the drivers of farmers' dissatisfaction with market-specification contracts for finger millet crop in Kenya. The researcher analysed data from 336 farmers contracted by USOMI, out of which a sub-sample of 146 farmers who grew millet in 2021 was analysed using a probit model and descriptive analysis approach, which showed that asymmetric information related to the role of the contracting firm, lack of adequate understanding of the contract, poor pricing, and input market imperfections are some of the critical constraints and sources of dissatisfaction in the commercialisation of millet farming in Western Kenya. On the other hand, farmers' satisfaction is driven by access to market and reasonable prices offered by the spot market traders. While the study found out that productivity and pricing were not gendered, it was observed that women were generally satisfied with contract farming. Consequently, contractual schemes should integrate complementary factors such as input provision. While the results do not show explicit benefits to women farmers, they do allow for the backing of contract farming in rural development programs to reduce gender inequalities. [ABSTRACT FROM AUTHOR]

We propose and develop a robust score-driven multiplicative dynamic factor model for the conditional volatility of electricity prices. This model allows us to identify both common and idiosyncratic conditional volatility factors in the spot price of electricity across key Nord Pool European markets. Both factors are extracted using a single observation-driven filter that is simple and parsimonious. The common factor is robust by construction as it averages information over the cross-section of the high-dimensional vector of electricity price changes. The idiosyncratic factors are designed to capture the erratic shocks in prices and therefore rely on a robust updating equation which allows for outliers and fat-tailed innovations. Our model is suitable for high-dimensional applications and does not necessarily suffer from the curse of dimensionality. The parsimonious nature of the observation-driven filter leads to simple computations for the estimation of parameters and the signal extraction of factors. We derive the stochastic properties of the filter and its underlying factor model, including bounded moments, stationarity, ergodicity, and filter invertibility. We further establish the consistency and asymptotic normality of the maximum likelihood estimator. The finite sample properties of the estimator and the filter are explored in a Monte Carlo study. Finally, we apply our model and filter to the Nord Pool spot price data and find evidence that net-import and net-export electricity markets exhibit substantially different common and idiosyncratic volatilities. In particular, we confirm that net-import markets are considerably more exposed to cross-market volatility spillovers, and we quantify the relevant degree of exposition. [ABSTRACT FROM AUTHOR]

Background: One of several logistics contexts in which pricing decisions are made involves truckload carriers using reverse auctions to bid for prices they want for their transportation services while operating under uncertainty about factors such as their (i) operations costs and (ii) rivals' bids. This study's main purpose is to explore humans' use of fast and frugal heuristics (FFHs) to navigate those uncertainties. In particular, the study clarifies the logic, theoretical underpinnings, and performance of human FFHs. Methods: The study uses behavior experiments as its core research method. Results: The study's key findings are that humans use rational FFHs, yet, despite the rationality, human decisions yield average profits that are 35% below profits from price optimization models. The study also found that human FFHs yield very unstable outcomes: the FFH coefficient of variation in profit is twice as large as price optimization. Novel contributions inherent in these findings include (a) clarifying connections between spot market auction pricing and behavioral theories and (b) adding truckload spot markets to the literature's contexts for measuring performance gaps between human FFHs and optimization models. Conclusions: The contributions have implications for practical purposes that include gauging spot pricing decisions made under constraints such as limited access to price optimization tools. [ABSTRACT FROM AUTHOR]

A multi-stage planning method for independent energy storage (IES) based on dynamically updating key transmission sections (KTS) is proposed to address issues such as uneven power flow distribution and transmission congestion resulting from the high penetration of renewable energy sources and load growth. First, an IES planning model considering KTS constraints is established by searching and updating the KTS and associated constraints. This model takes into account IES investment and operation costs, maintenance costs, revenue from spot price differences, and penalties for system congestion. Then, a multi-stage planning method for energy storage is proposed based on the dynamic updating of KTS and the annual planning results. To verify the effectiveness and feasibility of the proposed method, a case study simulation is conducted using the improved IEEE 39-bus New England standard system. The results demonstrate the ability of the method to achieve multi-stage rolling optimization of IES, providing a reference for future efforts toward multi-stage planning of IES. [ABSTRACT FROM AUTHOR]

We study production-and-order strategy in a two-echelon supply chain consisting of a single manufacturer and a single retailer in which both market demand and spot price are uncertain. The retailer updates early demand information through observing new market signal during long production season. The retailer may order product before the demand is updated by signing wholesale price contract (called a contract order), or order from the manufacturer in spot market after the demand is updated (called a spot order). In a retailer-led Stackelberg game, we construct a two-stage dynamic decision model to derive the optimal equilibrium solutions for the production and order quantity under both contract order strategy and spot order strategy. We characterize the conditions under which demand information updating benefits or hurts the members and a win-win is achieved. In sharp contrast to the existing findings in that the retailer always prefers to order late until demand information is updated, we discover that the retailer may prefers to order early when the spot price uncertainty is above a certain threshold. Then, we design a single-side payment contract to stimulate the manufacturer to choose order mode satisfying the retailer's preference. [ABSTRACT FROM AUTHOR]

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Learning the dynamics of power prices in a given market is important for a number of players (e.g., producers, consumers, and policy makers) at both macro- and microeconomic levels. This paper analyzes the recent behavior of spot prices in eight Western European countries. The sample period coincides with the COVID-19 pandemic for the most part: it starts in April 2020 and runs until May 2023; it includes the start of the Russia–Ukraine war. We introduce a new model for the hourly spot price of electricity. The deterministic component includes yearly, weekly, and daily seasonalities; the stochastic component accounts for volatility, mean reversion, and discrete jumps. We estimate the model with publicly available hourly data. Regarding the development of the internal market for electricity, we find that core mainland power markets now move closer in step with one another than before, but the integration process of the Iberian Peninsula seems to have kicked into reverse. As for the dynamics of power prices, in the last part of the sample period the speed of reversion falls everywhere, and price volatility increases noticeably; the expected number of jumps per hour decreases, but their average size turns to positive and they become more volatile. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Forward Freight Agreements (FFAs) are exchange-traded futures and the main means of risk management for shipping spot freight. The relationship between the FFA market and the shipping spot market has attracted much attention, but few studies focus on the dynamic FFA term structure and its correlation with the spot price. In this study, we establish the dynamic term structure of the FFA market by quantifying the level factor, the slope factor and the curvature factor of the FFA term structure to reveal the underlying information in the shipping derivatives market, and further scrutinize the impact of the FFA term-structure factors on the shipping spot market. The empirical results indicate that the term-structure factors have significant time-varying effects on the BDI and BPI: The effect of the term-structure factors on BDI and BPI changes with shipping market conditions and economic environment. The level factor has a positive effect, while the slope factor and the curvature factor both have negative effects, with the impacts on the BPI being about twice as large as the impacts on the BDI. The three factors all have their strongest effect in the short term and the weakest effect in the long term. [ABSTRACT FROM AUTHOR]

Commodities are usually used to diversify portfolios. However, their prices are affected by uncertain phenomena such as economic crises, political events, climate risk, etc. This paper examines how Climate and Economic Policy Uncertainty (measured by the CPU and GEPU indices, respectively) affect to wheat prices in the spot and future markets. By combining the wavelet transform and coherence analysis it is possible to see, graphically, the relationship between two time series and also to analyze it at different time frequencies. Although wheat spot and future prices have a similar behavior, the main results obtained in this paper show that the relationships between CPU and GEPU with future prices are stronger than with spot prices. Furthermore, futures and CPU exhibit synchronous movements, whereas futures and GEPU demonstrate antiphase behavior. However, in both cases (futures and spots) their relationship with CPU/GEPU is bounded to several time periods and some frequencies. [ABSTRACT FROM AUTHOR]

We employed a non-parametric causality test based on Singular Spectrum Analysis (SSA) and used the Vector Error Correction Model (VECM) and Information Share Model (IS) to measure the relationship between the futures and spot prices for seven major agricultural commodities in China from 2009 to 2017. We found that the agricultural futures market has potential leading information in price discovery. The results of an Impulse Response Function (IRF) analysis also showed that the spot prices react to shocks from the future market and have a lasting impact. This confirms our findings reported for the causality test and information share analysis. [ABSTRACT FROM AUTHOR]

We address the stock predictability puzzle, a challenge in the stock market often discussed in behavioral finance. Our approach formulates a statistical model within rational finance, avoiding reliance on behavioral finance assumptions, and integrates stock return predictability into the Black–Scholes option pricing framework. Empirical analysis focuses on the predictability of stock prices by option and spot traders, introducing a forward‐looking measure we term "implied excess predictability." Results show that option traders' predictability of stock returns positively correlates with moneyness, whereas for spot traders, this relationship is inverse. These findings suggest a potential asymmetry in stock price predictability between spot and option traders. Additionally, we demonstrate the importance of incorporating stock return predictability into option pricing formulas, particularly for options with strike prices significantly different from the stock price. Conversely, when moneyness is close to unity, predictability is not integrated into option pricing, indicating equal information among spot and option traders. Comparison of volatility measures reveals the difference between implied and realized variances or variance risk premia as potential predictors of stock returns. [ABSTRACT FROM AUTHOR]

This paper investigates how the introduction of foreign exchange futures has an impact on spot volatility and considers the contemporaneous and dynamic relationship between spot volatility and foreign exchange futures trading activity, including trading volume and open interest in the Thailand Futures Exchange context, with the examples of the EUR/USD futures and USD/JPY futures. The results of the EGARCH (1,1) model show that the introduction of foreign exchange futures decreases spot volatility. It also increases the rate at which new information is impounded into spot prices but decreases the persistency of volatility shocks. A positive effect of unexpected trading volume and a negative effect of unexpected open interest on contemporaneous spot volatility are in line with the VAR(1) model results of the dynamic relationship between spot volatility and foreign exchange futures trading activity. With the impact on spot volatility caused by unexpected open interest rate being stronger than by unexpected trading volume, foreign exchange futures trading stabilizes spot volatility. [ABSTRACT FROM AUTHOR]

In a context of illiquidity, the reservation price is a well-accepted alternative to the usual martingale approach which does not apply. However, this price is not available in closed form and requires numerical methods such as Monte Carlo or polynomial approximations to evaluate it. We show that these methods can be inaccurate and propose a deterministic decomposition of the reservation price using the Lambert function. This decomposition allows us to perform an improved Monte Carlo method, which we name Lambert Monte Carlo (LMC) and to give deterministic approximations of the reservation price and of the optimal strategies based on the Lambert function. We also give an answer to the problem of selecting a hedging asset that minimizes the reservation price and also the cash invested. Our theoretical results are illustrated by numerical simulations. [ABSTRACT FROM AUTHOR]

This study firstly applied a Bayesian symbolic regression (BSR) to the forecasting of numerous commodities' prices (spot-based ones). Moreover, some features and an initial specification of the parameters of the BSR were analysed. The conventional approach to symbolic regression, based on genetic programming, was also used as a benchmark tool. Secondly, various other econometric methods dealing with variable uncertainty were estimated including Bayesian Model Averaging, Dynamic Model Averaging, LASSO, ridge, elastic net, and least-angle regressions, etc. Therefore, this study reports a concise and uniform comparison of an application of several popular econometric models to forecasting the prices of numerous commodities. Robustness checks and statistical tests were performed to strengthen the obtained conclusions. Monthly data beginning from January 1988 and ending in August 2021 were analysed. [ABSTRACT FROM AUTHOR]

For a general stochastic volatility framework with correlation between the spot price and the instantaneous volatility, an analytical approximation for single barrier options with continuous monitoring is given. The approximation is expressed only in terms of market observable implied volatilities and prices. As such the approximation is independent of the specific form and number of parameters of the skew‐generating stochastic volatility model. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to comprehensively review the literature on the value and impact of the US Department of Agriculture (USDA) reports and discuss theoretical issues and empirical evidence. Only a handful of studies provide direct estimates of the welfare benefits of USDA information using a rational expectations framework in storable commodity markets. Most studies examine partial evidence of welfare benefits associated with market response to USDA information. These studies include examinations of the impacts of USDA report releases on cash and futures price volatility and other market dynamics, impacts of report releases on options implied volatility, impacts of unanticipated information in USDA reports, as well as informational value, returns, and survey‐based studies. We discuss the methodological and empirical contributions of these studies as well as their shortcomings and potential opportunities for future work. [ABSTRACT FROM AUTHOR]

Due to significant implications for resource and food sectors that directly influence social well-being, commodity price comovements represent an important issue in agricultural economics. In this study, we approach this issue by concentrating on daily prices of the corn futures market and 496 cash markets from 16 states in the United States for the period of July 2006 – February 2011 through correlation based hierarchical analysis and synchronization analysis, which allow for determining interactions and interdependence among these prices, heterogeneities in price synchronization, and their changing patterns over time. As the first study of the issue focusing on prices of the futures and hundreds of spatially dispersed cash markets for a commodity of indubitable economic significance, empirical findings show that the degree of comovements is generally higher after March 2008 but no persistent increase is observed. Different groups of cash markets are identified, each of which has its members exhibit relatively stable price synchronization over time that is generally at a higher level than the synchronization among the futures and all of the 496 cash markets. The futures is not found to show stable price synchronization with any cash market. Certain cash markets have potential of serving as cash price leaders. Results here benefit resource and food policy analysis and design for economic welfare. The empirical framework has potential of being adapted to network analysis of prices of different commodities. [ABSTRACT FROM AUTHOR]

Purpose: The intent of this Practice Briefing is to provide clarity on drivers of property pricing in a changing economic environment. The principal basis of this analysis is to investigate how properties have been priced relative to interest rates over the long haul. Such an insight may help investors navigate the world of property investment in a post zero interest-rate policy (ZIRP) world. Design/methodology/approach: This practice briefing is an overview of the role of economic drivers in pricing property in different economic eras pre- and post-ZIRP. It looks at returns over time relative to risk criteria and growth. Findings: This briefing is a review of property pricing and its relationship to economic drivers and discusses the concept of return premiums as a market indicator to spot under/over-priced property assets in the market. Practical implications: This briefing considers the implications of identifying salient and pertinent market indicators over time as bellweathers for property pricing. Good property investment is grounded in understanding when assets are under and overpriced relative to investors' expectations of growth and returns going forward. An understanding of markets and the current indicators thereof can provide investors with insights into those criteria. Originality/value: This provides guidance on how to interpret markets and get an understanding of property pricing over time. [ABSTRACT FROM AUTHOR]

This article discusses the auctioning of financial contracts by aggregations of consumers who aim to reduce the spot price of a concentrated industry's product; this is a frequent arrangement in electricity markets. The contracts' underlying asset is the product; the auctions' bidding variable is the strike price; and the bidders are the producers. Using a three-stage complete-information game, we show that when all consumers belong to some group, in the subgame perfect Nash equilibrium, each group fully hedges its consumption, and total output reaches its efficient level. Otherwise, each group over-hedges its consumption, and total production is below the efficiency level. [ABSTRACT FROM AUTHOR]

Forecasting electricity spot prices holds paramount significance for informed decision-making among energy market stakeholders. This study introduces a methodology utilizing a multilayer perceptron (MLP) neural network for multivariate electricity spot price prediction. The model underwent a feature selection process to identify the most influential predictors. In the validation phase, the model's performance was evaluated using key metrics, including trend accuracy percentage index (TAPI), normalized root mean squared error (NRMSE), and mean absolute percentage error (MAPE). The results were obtained for a four-week forecast horizon in order to serve as an auxiliary tool to facilitate decision-making processes in the short-term energy market. The relevance of short-term electricity spot price forecasting lies in its direct impact on pricing strategies during energy contract negotiations, which allows for the making of assertive decisions in the energy trading landscape. [ABSTRACT FROM AUTHOR]

In this work, we highlight three different techniques for automatically constructing the dataset for a time-series study: the direct multi-step, the recursive multi-step, and the direct–recursive hybrid scheme. The nonlinear autoregressive with exogenous variable support vector regression (NARX SVR) and the Gaussian process regression (GPR), combined with the differential evolution (DE) for parameter tuning, are the two novel hybrid methods used in this study. The hyper-parameter settings used in the GPR and SVR training processes as part of this optimization technique DE significantly affect how accurate the regression is. The accuracy in the prediction of DE/GPR and DE/SVR, with or without NARX, is examined in this article using data on spot gold prices from the New York Commodities Exchange (COMEX) that have been made publicly available. According to RMSE statistics, the numerical results obtained demonstrate that NARX DE/SVR achieved the best results. [ABSTRACT FROM AUTHOR]

This research created an estimator for a near-real-time US truckload spot market price index. This work applied an ARX-GARCH model to assess freight volatility by incorporating explanatory variables in the mean-equation to address the sector-specific behavioural patterns. The results suggest that daily rate volatility can be better specified with such an extended version of standard GARCH. It was also observed the reduction of immediate shock effects in freight rate variation, especially when controlling for fuel surcharge and truck capacity. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for defining the spot price of temporary water allocation rights for trading zones within the water market in the southern Murray–Darling Basin situated in Australia. The historical spot price is then used to calibrate a stochastic process depicting the dynamics of the water price, allowing the computation of prices of options on the underlying water price with the aim of producing reference prices to catalyse an options trading market. The most suitable stochastic model representing the water price dynamics is selected through comparing the option prices generated from four different models. Using the selected stochastic model, the most liquid trading zone in the Murray–Darling Basin water market (Zone 7) is used to demonstrate how the methodologies developed in the paper are used to calibrate the log‐mean stochastic model representing the stochastic spot price dynamics and compute prices for call and put options on the underlying water spot prices. Sensitivities of the water options prices to market input data can be calculated from the formulae provided in the paper. The results presented in this work can serve as a reference tool by industry practitioners and the farming community in using options for effective risk management of water resources. [ABSTRACT FROM AUTHOR]

Volatility is a matter of concern for time series modeling. It provides valuable insights into the fluctuation and stability of concerning variables over time. Volatility patterns in historical data can provide valuable information for predicting future behaviour. Nonlinear time series models such as the autoregressive conditional heteroscedastic (ARCH) and the generalized version of the ARCH model, i.e. generalized ARCH (GARCH) models are popularly used for capturing the volatility of a time series. The realization of any time series may have significant statistical dependencies on its distant counterpart. This phenomenon is known as the long memory process. Long memory structure can also be present in volatility. Fractionally integrated volatility models such as the fractionally integrated GARCH (FIGARCH) model can be used to capture the long memory in volatility. In this paper, we derived the out-of-sample forecast formulae along with the forecast error variances for the AR (1) -FIGARCH (1, d , 1) model by recursive use of conditional expectations and conditional variances. For empirical illustration, the modal spot prices of onion for Delhi, Lasalgaon and Bengaluru markets, India and S&P 500 index (close) data are used. [ABSTRACT FROM AUTHOR]

The existence of chaos is particularly relevant, as the identification of a chaotic behavior in a time series could lead to reliable short-term forecasting. This paper evaluates the existence of nonlinearity and chaos in the underlying process of the spot prices of the Spanish electricity market. To this end, we used daily data spanning from 1 January 2013, to 31 March 2021 and we applied a comprehensive framework that encompassed a wide range of techniques. Nonlinearity was analyzed using the BDS method, while the existence of a chaotic structure was studied through Lyapunov exponents, recurrence plots, and quantitative recurrence analysis. While nonlinearity was detected in the underlying process, conclusive evidence supporting chaos was not found. In addition, the generalized autoregressive conditional heteroscedastic (GARCH) model accounts for part of the nonlinear structure that is unveiled in the electricity market. These findings hold substantial value for electricity market forecasters, traders, producers, and market regulators. [ABSTRACT FROM AUTHOR]

To investigate the variations in price volatility spillover effects on different time scales across the LME, SMM, and Chinese spot nickel markets. This study uses data from the London Metal Exchange (LME), Shanghai Nonferrous Metals (SMM) nickel daily closing prices, and Chinese nickel daily market prices from April 1, 2015, to September 30, 2022, to categorize the above three markets' yield series into four-time scales: very short, short, medium, and long term. The multivariate BEKK-GARCH approach is then used to analyze the spillover effects at different time scales. The study's findings reveal that (1) the LME, SMM, and spot nickel prices are all impacted by external shocks and inherent volatility at all time scales; (2) at the very short time scale, there is a two-way volatility spillover effect between the SMM nickel and the other two markets, in addition, a one-way volatility spillover effect between the LME and spot nickel price; (3) at the short time scale, the volatility spillover effect between the LME and spot nickel price disappears; (4) at the medium-term time scale, there is no volatility spillover effect between the LME, SMM, and spot nickel price; (5) at the long-term time scale, the SMM has a volatility spillover effect on spot nickel price. [ABSTRACT FROM AUTHOR]

Proved undeveloped reserves (PUDs) are typically assigned a value of zero when the cost to produce them is greater than the prevailing forward curve. The zero valuation occurs despite the option value contained in even the most expensive PUDs. While PUDs are worthless if spot and forward prices forever remain below the cost to produce them, they have positive value if either the spot price or a contracted futures price exceeds the cost at any time. Since the probability that future prices exceed cost is positive, PUDs have positive option value despite the industry practice. Zero valuation occurs primarily because financing is unavailable when hedging is contingent on uncertain future outcomes where probabilities cannot be modeled. The failure of models to recognize contingent hedging is a limitation that leads to chronically undervalued PUDs in marginal and sub-marginal price environments. The literature is silent on contingent hedging where financing is dependent on forward curves that will not exist until some future date. This paper introduces a model that addresses these limitations. [ABSTRACT FROM AUTHOR]

This paper is the first attempt to provide a comprehensive account of pricing and informational efficiency of the US and EU natural gas markets. We rely on Maximum Overlap Discrete Wavelet decomposition (MODWT) of daily data of US Henry Hub, British NBP and Dutch TTF natural gas physical and futures returns at different maturities between 2013 and 2019. Multiscale linear and nonlinear Granger causality and random walk testing are investigated. We find that futures prices and spot prices of Henry Hub, NBP, and TTF are cointegrated. Moreover, multiscale causality testing shows that Henry Hub markets exhibit strong bidirectional causality between spot and futures markets. EU markets are globally efficient in terms of pricing despite some inconsistencies on the causality direction across time scales and maturities of the futures contracts. Finally, for the three selected futures markets, informational efficiency is reached only in the long-run. The results make it possible to establish an evaluation of the hubs in terms of their capacity to provide reliable reference prices for the quantities of gas under contract through a discussion on the crucial role of liquidity and storage capacity. [ABSTRACT FROM AUTHOR]

This study compares Medicare and patient spending for dual over-the-counter and prescription drugs with their over-the-counter cash prices. [ABSTRACT FROM AUTHOR]

Commodity spot prices tend to revert to some long-term mean level and most commodity derivatives are based on futures prices, not on spot prices. So, we consider spread options on futures instead of spot or spot index, where the log spot price follows a mean-reverting process. The volatility of the mean-reverting process is driven by two different (fast and slow) scale factors. We use asymptotic analysis to obtain a closed-form approximation of the futures prices and a closed-form formula for the approximate prices of spread options on the futures. The overall improvement of our analytic formula over the classical Kirk-Bjerksund-Sternsland (KBS) formula is discussed via numerical experiments. [ABSTRACT FROM AUTHOR]

To perform a cost analysis of generic and brand-name Phosphodiesterase Type 5 (PDE5) inhibitors at different dosages and pharmacies across the US. Using an all-payer retail pharmacy-claims database, we analyzed prescription drug data for three generic and six brand-name oral PDE5 inhibitors at different dosages across US chain and independent pharmacies in 2019. We obtained cash price data from 60,186 pharmacies (35,976 chain and 24,210 independent). The nationwide mean cash price per unit (PPU) ranged from $8.6 ± 5.2 (sildenafil 20 mg at chain pharmacies) to $107.1 ± 71 (Adcirca 20 mg at independent pharmacies) equal to 1145.3% difference. Chain pharmacies provided significantly lower average prices for one brand-name and six generic PDE5 inhibitors. Tadalafil PPU was cheaper at higher quantities, however, PPU increased with quantity prescribed for sildenafil. Looking at the top 10 metropolitan statistical areas, the highest PPUs were observed for tadalafil (Cialis) 10 mg and sildenafil (Viagra) 50 mg in Atlanta ($67.4 ± 8.7) and Los Angeles ($50.3 ± 24.0), while New York ($9.7 ± 2.6) and Miami ($27.9 ± 16.4) had the lowest PPUs for tadalafil (Cialis) 5 mg and sildenafil (Viagra) 100 mg, respectively. A substantial variability in PDE5 inhibitor cash prices exists across manufacturer, dosage, quantity, pharmacy type, and location. In addition, the pricing does not necessarily correlate with the regional socioeconomic factors. This highlights the importance of provider awareness and patient counseling on drug price including potentially assisting patients in identifying opportunities for cost savings. [ABSTRACT FROM AUTHOR]

Over the period 2016--2021 Australia's National Electricity Market (NEM) experienced an investment supercycle with 16,000MW of new utility-scale renewable plant commitments in a power system with a peak demand of 35,000MW, and the disorderly loss of 5,000MW of synchronous coal-fired plant. This placed strains on system security, most visibly in the distribution of the power systems' frequency, requiring material changes to the NEM's suite of Frequency Control Ancillary Service (FCAS) markets. Utility-scale batteries are ideally suited for FCAS duties, but there is no forward price curve for FCAS markets, nor is there any systematic framework for determining equilibrium prices that might otherwise be used for investment decision-making. In this article, we develop an approach for quantifying long run equilibrium costs and stochastic spot prices in the markets for Frequency Control Ancillary Services, with the intended application being to guide the suitability of utility-scale battery investments under conditions of uncertainty and missing forward FCAS markets. [ABSTRACT FROM AUTHOR]

The purpose of this article is to estimate the hedging effectiveness of the Indian Gold Derivative Market from both the statistics of Static hedge methodology of Vector Error Correction Method and Time-Varying hedge methodology of DVEC-GARCH, covering the time span of 1st April 2005 till 31st March 2019, to account for any difference existence in both. The study is conducted to judge the performance of gold futures as a tool of risk management. The result reveals the fact that hedging effectiveness from both the methodologies are found to be 35%. The reason behind low hedging efficiency might be due to the fact that gold itself is used as a hedge instrument and always in demand by the Indian investors for varied reasons as well as its accessibility in various forms (Gold ETFs and Gold Bonds issued by Government). The hedgers’ participation may be less as compared to speculators with respect gold future hedging. The results of phase-wise analysis, that is, before crisis (2005–2007), the hedging efficiency comes out to be 26%, during the crisis (2008–2012) 38.7% and after the crisis (2013–2018) 40% from both the static and dynamic hedge ratio models. The results showed that with the outbreak of Global Financial crisis in 2007–2009 and European debt crisis in 2010–2012 and thereafter, gold futures are increasing use as a risk management tool, but still at a minimal level. [ABSTRACT FROM AUTHOR]

Using the Bollinger Bands trading strategy (BBTS), investors are advised to buy (and then sell) Bitcoin and Ethereum spot prices in response to BBTS's oversold (overbought) signals. As a result of analyzing whether investors would profit from round-turn trading of these two spot prices, this study may reveal the following remarkable outcomes and investment strategies. This study first demonstrated that using our novel design with a heatmap matrix would result in multiple higher returns, all of which were greater than the highest return using the conventional design. We contend that such an impressive finding could be the result of big data analytics and the adaptability of BBTS in our new design. Second, because cryptocurrency spot prices are relatively volatile, such indices may experience a significant rebound from oversold to overbought BBTS signals, resulting in the potential for much higher returns. Third, if history repeats itself, our findings might enhance the profitability of trading these two spots. As such, this study extracts the diverse trading performance of multiple BB trading rules, uses big data analytics to observe and evaluate many outcomes via heatmap visualization, and applies such knowledge to investment practice, which may contribute to the literature. Consequently, this study may cast light on the significance of decision-making through the utilization of big data analytics and heatmap visualization. [ABSTRACT FROM AUTHOR]

Ahead of the Russia-Ukraine war, global natural gas production in 2021 rose nearly 5 percent to a record 4.04 trillion cubic meters. In 2021, Russian natural gas exports were 202 billion cubic meters of pipeline gas and 39.6 billion cubic meters of liquefied natural gas (LNG). [Russia Struggles to Make Up for Europe's Gap in Natural Gas Exports. https://www.aa.com.tr/en/europe/russia-struggles-to-make-up-for-europes-gap-in-natural-gas- exports/2717651]. Because of the war in Ukraine, Russia stopped exporting essential volumes of natural gas to Europe, which used to account for 40 percent of the EU's supply, causing gas prices in Europe to rise. Georgia, with its strategic location on the eastern coast of the Black Sea, has the potential to create a liquefied natural gas (LNG) hub for transporting gas to European markets bypassing Russia -- using existing port facilities in Anaklia, Poti, Batumi, Kulevi (owned by Sokar BST LLC); Recent developments show the Russian government's interest in the idea of a Turkish gas hub. At the end of September 2022, two days after the explosion of the "North Stream 1 and 2" gas pipelines on the bottom of the Baltic Sea, Presidents R.-T. Erdogan and Vladimir Putin agreed to add two more new gas pipelines along the TurkStream pipeline, which is apparently a rival project to the EU's Southern Gas Corridor (SGC). The SGC goes through Azerbaijan, Georgia and Turkey to the Balkans, and the TurkStream pipeline from Russia through the Black Sea and Turkey enters in the same region of Europe, where the European SGC also goes -- namely, South-Eastern Europe. Natural gas has recently become one of the main types of fuel since the 70s of the last century. At the beginning of this century, about 88-90% of natural gas was supplied to consumers by pipelines, long-term economic contracts, and the rest by tankers, in the form of liquefied natural gas (LNG). Global LNG imports in 2021, according to the report of the International Group of LNG Importers (GIIGNL), increased by 4.5% compared to the previous year and reached 513.7 billion cubic meters (372.3 million tons). The Group's research notes that in 2021, LNG will already account for approximately 40% of the global gas market, with the rest coming from gas pipelines. In 2021, about 73% (375 billion cubic meters, or 271.8 million tons) of LNG was imported by Asian countries. In addition, only 36.6% of the world's LNG volume was sold on the spot market, i.e. in small lots -- the rest was sold through long-term contracts, thereby neglecting the market mechanism of free price formation. Due to the Russia-Ukraine war, gas supplies to the EU through Gazprom pipelines have been almost completely cut off since the beginning of 2023, and Brussels is desperately looking for alternative routes. For the first time, the idea of such an alternative in the form of the Trans-Caspian gas pipeline was put forward by then US President Bill Clinton in 1996. However, due to the uncertainty of the status of the Caspian Sea -- the lack of delimitation of its shelf boundaries, the conflicting position of Azerbaijan, Iran and Turkmenistan, etc., this project was not implemented. Now Russia tries to use old Russia-Azrbaijan gas pipeline to connect his gas with EU SGC pipelines. Georgian government suggested in late 2022 to use his black sea Anaklia or Kulevi ports for constraction of LNG terminals and export the LNG tankers with Azeri (and Russian ?!) gas to the Balkan ports. [ABSTRACT FROM AUTHOR]

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Background: Diabetes medications place significant financial burden on patients but less is known about factors affecting cost variation. Objective: To examine pharmacy and neighborhood factors associated with cost variation for diabetes drugs in the US. Research design, subjects and measures: We used all-payer US pharmacy data from 45,874 chain and independent pharmacies reflecting 7,073,909 deidentified claims. We divided diabetes drugs into insulins, non-insulin generic medications, and brand name medications. Generalized linear models, stratified by pharmacy type, identified pharmacy and neighborhood factors associated with higher or lower cash price-per-unit (PPU) for each set of drugs. Results: Cash PPU was highest for brand name therapies ($149.4±203.2), followed by insulins ($42.4±25.0), and generic therapies ($1.3±4.4). Pharmacy-level price variation was greater for non-insulin generic therapies than insulins or brand name therapies. Chain pharmacies had both lower prices and lesser variation compared with independent pharmacies. Conclusions: Cash prices for diabetes medications in the US can vary considerably and that the greatest degree of price variation occurs in non-insulin generic therapies. [ABSTRACT FROM AUTHOR]

Emerging economies often establish commodity futures markets to discover price signals, manage price risks and improve market integration, but establishing a futures market may not be feasible for agricultural perishables. In this study, we evaluated the function of the world's first fresh fruit futures contract for apples. Combining partial cointegration with state-space modelling, we derived time-varying price discovery metrics for the apple futures market. Our findings revealed a limited and time-varying dominance of price discovery by the futures market, while a substantial share of price discovery occurred in the spot market. Moreover, poor convergence of disaggregated spot prices to the futures price suggests that commercial traders in the apple supply chain tended to focus more on the spot market than on the futures market. Thus, emerging economies should be cautious about the new establishment of futures markets for agricultural perishables. Future research using more specific data on the spot market may provide a better insight on the limited function of the futures market. [ABSTRACT FROM AUTHOR]

To realize Carbon Neutrality, renewable energy would be the main source of electricity, such as photovoltaic (PV) power generation in the Japanese power system. However, a decrease in the number of operating thermal generators, the main source of balancing power in the Japanese balancing market, is causing concerns regarding a shortage of balancing power in the future. PV can therefore provide balancing power in addition to energy; thus, the PV power economic value in electricity markets will become diversified. A solution examination and rationality analysis of technical issues for PV‐based balancing power creation are needed. Here, by applying the proposed headroom control method based on the PV output curtailment beforehand, PV generator optimizes benefit by bidding into the balancing market when the spot market price is very low. Optimizing the bidding strategy on the spot market and balancing market is conducted, and battery integration is also considered. The optimization simulation results validate the possibility of creating balancing power by a PV power producer, and the total revenue valuation verifies a more beneficial effect. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In this paper, I consider a doubly stochastic Poisson process with intensity λt=qXt$$ {\lambda}_t=q\left({X}_t\right) $$ where X$$ X $$ is a continuous Itô semi‐martingale. Both processes are observed continuously over a fixed period 0,1$$ \left[0,1\right] $$. I propose a local polynomial estimator for the function q$$ q $$ on a given interval. Next, I propose a method to select the bandwidth in a nonasymptotic framework that leads to an oracle inequality. Considering the asymptotic n$$ n $$, and q=nq˜$$ q=n\tilde{q} $$, the accuracy of the proposed estimator over the Hölder class of order β$$ \beta $$ is n−β2β+1$$ {n}^{\frac{-\beta }{2\beta +1}} $$ if the degree of the chosen polynomial is greater than ⌊β⌋$$ \left\lfloor \beta \right\rfloor $$ and it is optimal in the minimax setting. I apply those results to data on French temperature and electricity spot prices from which I infer the intensity of electricity spot spikes as a function of the temperature. [ABSTRACT FROM AUTHOR]

Australia is facing a historic investment challenge, with an estimated $1.2 to $1.5 trillion in newly invested capital required by 2030 in order to meet 2050 decarbonisation targets. In response policy makers have begun "re-entering" energy markets, which were deregulated and privatised in the 1990's. Public auctions for State-backed 'Contracts-for-Differences' (CfDs) now act as a key policy tool to "prime" energy markets for new variable renewable energy (VRE) capacity. In a phenomenon recently coined "market hybridisation", market participants form long-term investment decisions that are increasingly disconnected from short-run spot pricing dynamics and day-to-day operations. Historically, it has been common practice to underpin VRE projects with some form of power purchase agreement (PPA). However, increasing investor appetites for "semi-merchant" projects (i.e. projects partially exposed to spot prices) has become an emerging trend. This article examines the semi-merchant phenomenon, aiming to explain why the investment model is becoming popular for investors in VRE. Of particular interest are the foregone advantages attributable to highly contracted plant when securing bankable project finance. Results reveal that a revenue mix comprising 70%–80% PPA coverage and 20%–30% merchant exposure appears viable and tractable for investors in utility scale solar — whilst maintaining a typical project finance capital structure of c.60%–70% gearing. By implication, policymakers targeting 4000MW of new solar capacity via CfD auction may need only offer ∼ 3000MW of contracted capacity, thereby reducing taxpayer exposures and protecting scarce government balance sheet resources. [ABSTRACT FROM AUTHOR]

This study aims to predict Taiwan stock returns through gold and crude oil futures prices using monthly data from TAIEX and 19 stock sector indexes from January 1996 to December 2020. By using a 60-month rolling window horizon, we compare the forecast performances of various regression models, where the forecast performances are measured by MAE (mean absolute error) and R2 OS (out-of-sample R-square). In addition to using spot returns and the first principal component of futures returns on gold and crude oil, five traditional financial variables (dividend to price ratio, earnings to price ratio, market price to book value ratio, long-term yield, and short-term yield) are added to the regression model to explain and predict stock returns. Given that the regression models have included these traditional financial variables, the empirical results reveal that adding gold or crude oil price information to the model substantially improves its explanatory ability. Additionally, except during periods of high stock returns, the forecast ability of crude oil price information on stock returns is significantly better than traditional forecast variables. Furthermore, although gold prices are not as accurate as crude oil prices in predicting stock returns, their predictive capabilities are often better than the traditional financial variables. [ABSTRACT FROM AUTHOR]