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The aim of this study was to determine if there are asymmetries in the pass-through of West Texas Intermediate (WTI) crude oil prices to its derivatives (diesel and gasoline) in the Brazilian market.

Initially, the future WTI oil price series was analyzed using the self-exciting threshold autoregressive (SETAR) and logistic smooth transition autoregressive (LSTAR) non-linear models. Subsequently, the threshold autoregressive error-correction model (TAR-ECM) and Markov-switching model were used.

The findings indicated high prices throughout 2008 due to the subprime crisis. The findings indicated high prices throughout 2008 due to the subprime crisis. The results indicated that there is long-term pass-through of oil prices in both methods, suggesting an equilibrium adjustment in the prices of diesel and gasoline in the analyzed period. Regarding the short term, the variations in contemporary crude oil prices have positive effects on the variations in fuel prices. Lastly, this behavior can partly be explained by the internal price management structure adopted during almost all of the analyzed period.

This paper contributes to the literature at some points. The first contribution is the modeling of the oil price series through non-linear models, further enriching the literature on the recent behavior of this time series. The second is the simultaneous use of the TAR-ECM and Markov-switching model to capture possible short- and long-term asymmetries in the pass-through of prices, as few studies have applied these methods to the future price of oil. The third and main contribution is the investigation of whether there are asymmetries in the transfer of oil prices to the price of derivatives in Brazil. So far, no work has investigated this issue, which is very relevant to the country.

The purpose of this paper is to investigate for symmetries – in sign and size – between spot and futures prices in the markets of energy commodities.

The aforementioned objective is pursued using daily observations of spot and futures prices for the commodities of crude oil, Brent, heating oil, gasoline and natural gas, along with local nonlinear regression.

Symmetry in sign and size cannot be rejected. This means that, shocks of the same absolute magnitude, but of different sign, are transmitted from futures prices to spot prices with the same intensity. In addition, larger absolute value price shocks in the futures are transmitted to the spot markets with the same intensity compared with smaller ones. The findings of symmetry in the comovements among prices reveal a lack of those commodities on diversifying the investors’ investment risk.

To the best of the authors’ knowledge, this is the first study to use local nonlinear regression to test for sign and size symmetry between futures and spot prices in the energy commodities markets.

Companies affected by California’s cap-and-trade legislation are allotted certain credits for production that can be used or sold and can purchase additional credits from the state, which become a revenue source to be used for activities that reduce carbon emissions. The purpose of this paper is to investigate who ultimately pays for this program, its effectiveness in reducing carbon emissions in accordance with established goals, and the related effectiveness to advance social, economic, and environmental equity.

The methodology used for this research is secondary data analysis, triangulating three sources: California’s Climate Change Investment Reports, 2019-2021; repositories maintained by the California High-Speed Rail Authority and the California Air Resources Board; and a review of the literature and websites from other professional sources which addressed, directly and indirectly, the topics and questions explored in the study.

Key findings include evidence of enhancing social and environmental equity but ineffectiveness in reducing carbon emissions in accordance with state goals. Furthermore, the program displays evidence of economic inequity as it demonstrates characteristics of regressive taxation and an inability of low-income persons to acquire electric vehicles due to high costs.

The research effort is unique in that no other academic efforts were located which attempt to examine the cap-and-trade program’s effectiveness in attaining its goals.

In this paper, we study the effect of global energy and food inflation on household inflation expectations during the period 1988M01–2020M03 for a set of European economies.

We use multifractal de-trended cross-correlation analysis to estimate the non-linear and time-varying cross-correlation. We provide additional robustness tests using the Autoregressive-Distributed Lag method.

We find that household inflation expectations, global energy inflation and global food inflation are all multifractal. We also find that the household inflation expectations, global energy inflation and global food inflation are positively correlated (i.e., they are persistent). However, household inflation expectations respond more when the volatility of the global energy inflation is lower than when the volatility is higher. The correlation between household inflation expectations and global food inflation does not depend on the level of volatility.

First, paying attention to the global commodity inflation might help anchor inflation expectations better. It is so because Central Bank's efficacy in achieving price stability may be weakened if there is a relationship between commodity inflation and inflation expectation. This task would become even more difficult in the average inflation targeting regime than inflation targeting regime if actual inflation is persistently different from the target inflation. Second, our results also emphasize the importance of effective strategy for communicating to households about actual inflation, inflation target and keep them updated about how monetary policy functions.

We contribute to the literature by estimating the cross-correlation between household inflation expectations with the global commodity inflation, conditional to the volatility of the commodity inflation under consideration.

This study explores the direct relationship between social media user-generated content (UGC), online search traffic and offline light vehicle sales of different models.

The long-run equilibrium relationship and short-run dynamic effects between the valence and volume of UGC, online search traffic and offline car sales are analyzed by applying the autoregressive distribution lag (ARDL) model.

The study found the following. (1) In the long-run relationship, the valence of online reviews on social media platforms is significantly negatively correlated with the sales of all models. However, in the short-run, the valence of online reviews has a significant positive correlation with all models in different lag periods. (2) The volume of online reviews is significantly positively correlated with the sales of all models in the long run. However, in the short run, the relationship between the volume of online reviews and the sales of lower-sales-volume cars is uncertain. There is a significant positive correlation between the volume of reviews and the sales of higher-sales-volume cars. (3) Online search traffic has a significantly negative correlation with the sales of all models in the long run. However, in the short run, there is no consistent conclusion on the relationship between online search traffic and car sales.

This study provides a reference for managers to use in their efforts to improve offline high-involvement product sales using online information.

This paper empirically investigates the demand for pastured eggs in the United States and evaluates the welfare consequences of Japan's egg import tariff reductions for the US consumers.

Using household-level Nielsen Homescan panel data, a fixed-effects Heckman two-stage sample selection model is estimated.

The estimation results ascertain the importance of a set of household socioeconomic characteristics, which are found to influence both the purchase probabilities and the consumption amounts associated with pastured eggs. In addition, demand for pastured eggs is estimated to be inelastic, and pastured eggs are found to be a normal good, more specifically a luxury.

The dataset used in this study reflect purchases only for at-home consumption, lacking information on away-from-home purchases.

Building upon previous research, this study makes the following distinct contributions to the current literature. To the best of our knowledge, it constitutes the first study to empirically examine the demand for pastured eggs, using household-level panel data and an estimation model that not only allows for left-censoring but also controls for regional and time fixed effects. Second, the present study reflects a unique effort in analyzing the adverse welfare consequences of the increased egg prices in the United States brought by a reduction of Japanese import tariffs on US-supplied eggs, focusing specifically on pastured eggs.

Few cases allow the student to understand the relationship between brand strategy, marketing strategy, implementation, and analysis. While some conceive of the process as being sequential, this case demonstrates that in fact, this process is more fluid, and that implementation and analysis impact subsequent strategy.

This field-based case provides a rare glimpse into the turnaround of a brand that was all but dead. After Buick suffered more than five decades of declining business results and an inferior brand image versus all rivals, few thought that the brand could be resuscitated. This case provides a valuable under-the-hood look at how the Buick team, over time, progresses through a series of marketing improvements all anchored on an evolved strategy. Specifically, Buick introduced a shift in brand strategy behind an evolved brand essence statement (i.e., brand positioning), improved product lineup, new-to-the-world innovation, enhanced dealership service, and more compelling advertising. The results led to a record number of product awards, significantly improved advertising measures, improved service ratings, and better business results.

Despite significant improvement across multiple dimensions of the business, Buick still trailed key competitors on one of the most important measures Buick tracked—the brand momentum rating—suggesting that there was still more work needed to complete the brand turnaround. The case introduces Molly Peck, the new marketing director on Buick, who is wondering what more, if anything, Buick should do. The material allows for instruction around marketing strategy and the process of converting it into implementation through the use of a creative brief.

This paper aims to examine the cross-quantile correlation and causality-in-quantiles between green investments and energy commodities during the outbreak of COVID-19. To be specific, the authors aim to address the following questions: Is there any distributional predictability among green bonds and energy commodities during COVID-19? Is there exist any directional predictability between green investments and energy commodities during the global pandemic? Can green bonds hedge the risk of energy commodities during a period of the financial crisis.

The authors use the nonparametric causality in quantile and cross-quantilogram (CQ) correlation approaches as the estimation techniques to investigate the distributional and directional predictability between green investments and energy commodities respectively using daily spot prices from January 1, 2020, to March 26, 2021. The study uses daily closing price indices S&P Green Bond Index as a representative of the green bond market. In the case of energy commodities, the authors use S&P GSCI Natural Gas Spot, S&P GSCI Biofuel Spot, S&P GSCI Unleaded Gasoline Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI, OPEC Oil Basket Price, Crude Oil Oman, Crude Oil Dubai Cash, S&P GSCI Heating Oil Spot, S&P Global Clean Energy, US Gulf Coast Kerosene and Los Angeles Low Sulfur CARB Diesel Spot.

From the CQ correlation results, there exists an overall negative directional predictability between green bonds and natural gas. The authors find that the directional predictability between green bonds and S&P GSCI Biofuel Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI Spot, OPEC Oil Basket Spot, Crude Oil Oman Spot, Crude Oil Dubai Cash Spot, S&P GSCI Heating Oil Spot, US Gulf Coast Kerosene-Type Jet Fuel Spot Price and Los Angeles Low Sulfur CARB Diesel Spot Price is negative during normal market conditions and positive during extreme market conditions. Results from the non-parametric causality in the quantile approach show strong evidence of asymmetry in causality across quantiles and strong variations across markets.

The quantile time-varying dependence and predictability results documented in this paper can help market participants with different investment targets and horizons adopt better hedging strategies and portfolio diversification to aid optimal policy measures during volatile market conditions.

The outcome of this study will promote awareness regarding the environment and also increase investor’s participation in the green bond market. Further, it allows corporate institutions to fulfill their social commitment through the issuance of green bonds.

This paper differs from these previous studies in several aspects. First, the authors have included a wide range of energy commodities, comprising three green bond indices and 14 energy commodity indices. Second, the authors have explored the dependency between the two markets, particularly during COVID-19 pandemic. Third, the authors have applied CQ and causality-in-quantile methods on the given data set. Since the market of green and sustainable finance is growing drastically and the world is transmitting toward environment-friendly practices, it is essential and vital to understand the impact of green bonds on other financial markets. In this regard, the study contributes to the literature by documenting an in-depth connectedness between green bonds and crude oil, natural gas, petrol, kerosene, diesel, crude, heating oil, biofuels and other energy commodities.

This study aims to present the cost and revenue efficiency evaluation models in data envelopment analysis in the presence of fuzzy inputs, outputs and their prices that the prices are also fuzzy. This study applies the proposed approach in the energy sector of the oil industry.

This study proposes a value-based technology according to fuzzy input-cost and revenue-output data, and based on this technology, the authors propose an approach to calculate fuzzy cost and revenue efficiency based on a directional distance function approach. These papers incorporated a decision-maker’s (DM) a priori knowledge into the fuzzy cost (revenue) efficiency analysis.

This study shows that the proposed approach obtains the components of fuzzy numbers corresponding to fuzzy cost efficiency scores in the interval [0, 1] corresponding to each of the decision-making units (DMUs). The models presented in this paper satisfies the most important properties: translation invariance, translation invariance, handle with negative data. The proposed approach obtains the fuzzy efficient targets corresponding to each DMU.

In the proposed approach, by selecting the appropriate direction vector in the model, we can incorporate preference information of the DM in the process of evaluating fuzzy cost or revenue efficiency and this shows the efficiency of the method and the advantages of the proposed model in a fully fuzzy environment.

The purpose of the study is to investigate micro determinants for dynamic wine pricing in Argentina. We test whether attributes and time affect the price rate of change. The rate of change is selected given the inflationary context of the country. The analysis provides valuable information for wine marketing decisions.

The modeling approach relies on panel data analysis for exploiting the data cross-section and time dimension. The contribution explores a massive price dataset at a weekly frequency. The dependent variable is the weekly price variation rate for product/wine and covariates are attributes, time and nominal variables. Given that endogeneity issues arose, the estimations rely on a two-stage least squares and instrumental variables with cluster-robust errors.

Estimations show that attributes, time and cost variables are statistically significant, with clear seasonal patterns and quality segmentation affecting pricing: wines made out of specific grapes such as Chenin, Merlot and Seedling or composing a broad category such as red wine, exhibit price undershooting (price rate of change below average). On the other hand, wines out of grapes such as Bonarda, Margaux, Mistela, Moscatel, Oporto, Tannat and Sauvignon Blanc show price overshooting (rate of change above average). In summary, wine made from determined grapes and specific wineries show divergent pricing.

Covariates such as alcohol content, label descriptor information, winery history, substitute competition and vintage, among others, have not been considered given that the research analyzes more than 750 wine products. Another limitation is that the work does not explore many time-series covariates, such as promotions and idiosyncratic shocks.

The contribution presents new information on wine pricing patterns affected by weeks, months and years, including the effect of the prolonged 2020 Argentine lockdown. It also analyzes estimations on pricing at the level of grape/blend and wineries previously unknown in this market. The information can influence inventory decisions on the side of the sellers and purchase decisions on the side of consumers.

The analysis includes fine but also low-cost wines that form part of the diet of low-income families in the country. The work detects a divergent pattern in pricing divided by the quality/price of the wine. It also presents information on price timing that may help consumers in the best moment to buy.

The contribution analyzes unprecedented information on weekly wine prices and presents evidence of pricing tactics from a point-of-sale perspective: It identifies different adjustment speeds related to product features and time effects.