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Do government policies during the COVID-19 pandemic affect investors' risk aversion, as proxied by the variance premium? To answer this question, this study examines data regarding government responses from thirteen countries. The empirical analysis indicates that government interventions were not able to substantially reduce variance risk premium in international equity markets. The results also show that economic support policies, containment, and closure regulations, and health system interventions all played a significant role in shaping equity variance risk price. [ABSTRACT FROM AUTHOR]

Current implementations of hybrid life cycle assessment (LCA) mostly do not fully exploit valuable information from multi‐regional input–output databases by aggregating regional supply chains to the lower geographical resolution of process LCA databases. We propose a method for sampling the various individual regions within the aggregated regional scope of LCA processes. This sampling maximizes the information content of hybrid LCA footprint results by preserving the regional variance, and it allows for regional price distributions from BACI/UN‐COMTRADE international trade statistics to be used to simultaneously improve the accuracy of the hybrid model. This work makes the previously hidden regional and price variance explicit and analyzes uncertainty of the hybrid carbon (global warming potential 100, GWP100) and land use footprints arising from these variances, both separately and in combination. We find that the median process footprint intensity increases by 7−3+18%$7^{+18}_{-3}\%$ for the GWP100 due to hybridization, and 90−23+143%$90^{+143}_{-23}\%$ for the land use footprint. Results show that the magnitude of the footprint uncertainty strongly depends on the product sector of the LCA process and environmental impact considered. In a case study of Swiss household consumption, we find truncation error estimates of 8.4−2.7+9.2%$8.4^{+9.2}_{-2.7}\%$ for the GWP100 and 36−14+64%$36^{+64}_{-14}\%$ for the land use footprint. Our results highlight the importance of regionalization of process LCA databases, as it has the potential to significantly improve both the precision and accuracy of derived hybrid LCA models. This article met the requirements for a gold/gold JIE data openness badge described at http://jie.click/badges. [ABSTRACT FROM AUTHOR]

Options pricing remains a critical aspect of finance, dominated by traditional models such as Black-Scholes and binomial tree. However, as market dynamics become more complex, numerical methods such as Monte Carlo simulation are accommodating uncertainty and offering promising alternatives. In this paper, we examine how effective different options pricing methods, from traditional models to machine learning algorithms, are at predicting KOSPI200 option prices and maximizing investment returns. Using a dataset of 2023, we compare the performance of models over different time frames and highlight the strengths and limitations of each model. In particular, we find that machine learning models are not as good at predicting prices as traditional models but are adept at identifying undervalued options and producing significant returns. Our findings challenge existing assumptions about the relationship between forecast accuracy and investment profitability and highlight the potential of advanced methods in exploring dynamic financial environments. [ABSTRACT FROM AUTHOR]

Abstract Background Introducing more generics has been a successful strategy for lowering pharmaceutical prices and expenditure. However, the effect of the strategy depends on the pricing schemes for generics. We aimed to update the South Korean generic markets in terms of effective competition, and to examine the effects of number of manufacturers and price variance on pharmaceutical expenditure. Methods We constructed balanced panel data provided by the Health Insurance Review and Assessment Service covering 726 reimbursed substances from 2019 to 2023. We developed original indicators to analyze the generic markets: the maximum-minimum price variance (MMPV) and the maximum-weighted price variance (MWPV). Panel regression with fixed and time-fixed effects was used. Results Over the study period, the number of manufacturers increased from 17.81 in 2019 to 20.98 in 2020 and then decreased to 18.70 in 2023. The MMPV increased from 204.70 in 2019 to 230.07 in 2022 and then decreased slightly to 225.34 in 2023. The MWPV increased from 59.70 in 2019 to 72.58 in 2023. Two types of segmented markets were noteworthy: low use of low-cost generics with sufficient manufacturers and high use of low-cost generics with insufficient manufacturers. In the fixed and time-fixed effects panel analyses, the MWPV presented a negative association with the number of manufacturers and a positive association with the MMPV. Conclusions A newly introduced tiered pricing scheme, designed to differentiate generic prices, was associated with a decrease in the number of manufacturers and an increase in price dispersion. The pricing schemes for generics should be designed with price variance in mind and limit the number of too many generics in South Korea.

Background: Introducing more generics has been a successful strategy for lowering pharmaceutical prices and expenditure. However, the effect of the strategy depends on the pricing schemes for generics. We aimed to update the South Korean generic markets in terms of effective competition, and to examine the effects of number of manufacturers and price variance on pharmaceutical expenditure. Methods: We constructed balanced panel data provided by the Health Insurance Review and Assessment Service covering 726 reimbursed substances from 2019 to 2023. We developed original indicators to analyze the generic markets: the maximum-minimum price variance (MMPV) and the maximum-weighted price variance (MWPV). Panel regression with fixed and time-fixed effects was used. Results: Over the study period, the number of manufacturers increased from 17.81 in 2019 to 20.98 in 2020 and then decreased to 18.70 in 2023. The MMPV increased from 204.70 in 2019 to 230.07 in 2022 and then decreased slightly to 225.34 in 2023. The MWPV increased from 59.70 in 2019 to 72.58 in 2023. Two types of segmented markets were noteworthy: low use of low-cost generics with sufficient manufacturers and high use of low-cost generics with insufficient manufacturers. In the fixed and time-fixed effects panel analyses, the MWPV presented a negative association with the number of manufacturers and a positive association with the MMPV. Conclusions: A newly introduced tiered pricing scheme, designed to differentiate generic prices, was associated with a decrease in the number of manufacturers and an increase in price dispersion. The pricing schemes for generics should be designed with price variance in mind and limit the number of too many generics in South Korea. [ABSTRACT FROM AUTHOR]

The effects of stochastic volatility, jump clustering, and regime switching are considered when pricing variance swaps. This study established a two-stage procedure that simplifies the derivation by first isolating the regime switching from other stochastic sources. Based on this, a novel probabilistic approach was employed, leading to pricing formulas with time-dependent and regime-switching parameters. The formulated solutions were easy to implement and differed from most existing results of variance swap pricing, where Fourier inversion or fast Fourier transform must be performed to obtain the final results, since they are completely analytical without involving integrations. The numerical results indicate that jump clustering and regime switching have a significant influence on variance swap prices. [ABSTRACT FROM AUTHOR]

The CIR stochastic volatility model is modified to introduce nonlinear mean reversion, with the long-run volatility average as a random variable controlled by two parts being modeled through a Brownian motion and a Markov chain, respectively. This model still possesses an analytical formulation of the forward characteristic function, from which we establish variance swap prices as well as volatility swap ones with a nonlinear payoff in closed form. The numerical implementation of the two formulae demonstrates the significant impact of regime switching. [ABSTRACT FROM AUTHOR]

We investigate information processing in the stochastic process driving stock's volatility (volatility discovery). We apply fractionally cointegration techniques to decompose the estimates of the market-specific integrated variances into an estimate of the common integrated variance of the efficient price and a transitory component. The market weights on the common integrated variance of the efficient price are the volatility discovery measures. We relate the volatility discovery measure to the price discovery framework and formally show their roles on the identification of the integrated variance of the efficient price. We establish the limiting distribution of the volatility discovery measures by resorting to both long span and in-fill asymptotics. The empirical application is in line with our theoretical results, as it reveals that trading venues incorporate new information into the stochastic volatility process in an individual manner and that the volatility discovery analysis identifies a distinct information process than that based on the price discovery analysis. [ABSTRACT FROM AUTHOR]

We examine the clustering behavior of price and variance jumps using high-frequency data, modeled as a marked Hawkes process (MHP) embedded in a bivariate jump-diffusion model with intraday periodic effects. We find that the jumps of both individual stocks and a broad index exhibit self-exciting behavior. The three dimensions of the model, namely positive price jumps, negative price jumps, and variance jumps, impact one another in an asymmetric fashion. We estimate model parameters using Bayesian inference by Markov Chain Monte Carlo, and find that the inclusion of the jump parameters improves the fit of the model. When we quantify the jump intensity and study the characteristics of jump clusters, we find that in high-frequency settings, jump clustering can last between 2.5 and 6 hours on average. We also find that the MHP generally outperforms other models in terms of reproducing two cluster-related characteristics found in the actual data. [ABSTRACT FROM AUTHOR]

In this study, we propose a new formula for spread option pricing with the dependence of two assets described by a copula function. The proposed method's advantage lies in its requirement of solely computing one-dimensional integrals. Any univariate stock price process, admitting an affine characteristic function, can be used in our formula to get an efficient numerical pricing procedure for a spread option. In the numerical analysis we present a comparison with the Monte Carlo simulation method to assess the performance of our approach, assuming that the univariate stock price follows three widely applied models: variance gamma, Heston's stochastic volatility and affine Heston–Nandi GARCH(1,1) models. [ABSTRACT FROM AUTHOR]

This article studies exchange-traded funds' (ETFs) price impact in the most ETF-dominated asset classes: volatility (VIX) and commodities. I propose a new way to measure ETF-related price distortions based on the specifics of futures contracts. This allows me to isolate a component in VIX futures prices that is strongly related to the rebalancing of ETFs. I derive a novel decomposition of ETF trading demand into leverage rebalancing, calendar rebalancing, and flow rebalancing, and show that trading against ETFs is risky. Leverage rebalancing has the largest effects on the ETF-related price component. This rebalancing amplifies price changes and exposes ETF counterparties to variance. [ABSTRACT FROM AUTHOR]

This research proposed machine learning forecasting models to support bitcoin investment decisions based on bitcoin price and trade volume from 2019 to 2021. The moving average crossovers of 5, 30, and 90 daily closing prices and their variances were inputs loaded into decision tree, random forest, and extreme gradient boosting (XGBoost) techniques to forecast bitcoin investment strategies, including market trends, actions, and holding amounts. The research also measured the models' performance based on accuracy, precision, recall, F1-score, and area under the curve-receiver operating characteristics (AUC-ROC). The results indicated that the XGBoost is the most efficient model: (1) trend (0.930 accuracy, 0.930 precision, 0.930 recall, 0.929 F1-score, and 0.983 AUC-ROC); (2) action (0.985 accuracy, 0.985 precision, 0.985 recall, 0.985 F1-score, and 0.998 AUC-ROC); and (3) amount (0.987 accuracy, 0.987 precision, 0.987 recall, 0.987 F1-score, and 0.997 AUC-ROC). The random forest achieved the second most efficient model, while the decision tree provided the lowest forecasting results. Since the bitcoin investment market in 2022 is significantly different from the previous two years due to several negative factors, the research further validated the models' performance with an unseen data set comprising 275 days of bitcoin market prices from January 1 to October 2, 2022. All the models suggested that investors hold with half the investment consistent with the investment market in 2022. Furthermore, although the decision tree and XGBoost models forecasted the investment trend for most days as up, the random forest forecasted the trend as sideway, consistent with the 2022 trend. [ABSTRACT FROM AUTHOR]

Commodity prices are extremely volatile, and volatility itself fluctuates over time. Using data from 1959 to 2022, we estimate a 3-state Markov-switching model to identify expansions and contractions in oil and copper price volatility. We found a transition from a low to a medium variance regime for the oil price, in 1979, reflecting changes in the oil market structure. In addition, we identify infrequent and short-lived episodes of unusually high oil price volatility. For copper, there is no transition across regimes, and episodes of high volatility are not synchronized with the periods of high volatility in oil prices. We found that oil prices are much more volatile than copper prices in all states. Oil prices react more strongly to market cartelization, war episodes, and global demand shifts, like the 2008 Great Recession and the COVID-19. [ABSTRACT FROM AUTHOR]

This study investigates the potential of natural gas-based plants designed to produce blue hydrogen and decarbonized electric power, conceived to operate flexibly depending on the electricity price. This paper considers plants based on fired-tubular reforming (FTR) and auto-thermal reforming (ATR) technologies, with MDEA-based pre-combustion CO 2 capture process and partial electrification of the reformer, designed to achieve CO 2 capture efficiency higher than 95 %. Heat and mass balances for the chemical and power island are evaluated at both full and part load to define the corresponding operating maps. With pre-combustion CO 2 capture only, FTR plants can achieve CO 2 capture rates higher than 90 %, H 2 production efficiency of 73–74 % and power generation efficiency of around 47 %. Reformer electrification allows increasing overall capture efficiency to 95 %. Plants based on ATR can approach 95 % capture efficiency without electrification and achieve H 2 efficiency similar to FTR but higher electric efficiency, close to 51 %. An economic analysis is performed to assess profitability of the plants under electricity price scenarios with different penetration of renewables. The economic analysis shows that flexible plants may be profitable in future scenarios with high penetration of renewables and high price variance, resulting in IRR around 10–17 % for hydrogen selling prices of 2.0–2.5 €/kg, natural gas price of 9 €/GJ and carbon tax of 100 €/t CO2. [Display omitted] • Techno-economic study of low-emission H 2 & power plants with CO 2 capture. • Fired tubular reformers and autothermal reformers compared. • Reformer electrification increases capture rate and gives operational flexibility. • 71.3–75.2 % hydrogen production efficiency and 47.4–50.9 % electric efficiency estimated. • IRR 10–17 % in future high wind electricity market with hydrogen prices of 2.0–2.5 €/kg. [ABSTRACT FROM AUTHOR]

Agricultural product costs play a significant part in the horticultural market. In India, vegetables, for example, tomatoes have the biggest supply and price variances among farming items. As tomatoes are grown around the year, outdoor and indoor, their yields change because of various factors, it is hard to settle tomatoes' inventory and costs. Although the Government puts numerous efforts to balance out the supply and costs of vegetables, continuous meteorological changes have prompted unstable supply and price fluctuations of vegetables. Accordingly, the right anticipating of vegetable costs is a significant issue. To oblige these, in this paper, an attempt has been made to dissect the costs and yield of tomatoes in India by utilizing a Machine Learning approach. This will unquestionably help the farmers and the Government if the anticipated costs are getting higher in the forthcoming months, then appropriate strategies can be made to diminish the costs of tomatoes. [ABSTRACT FROM AUTHOR]

The overlook of some economic scenarios may result in unforeseen negative outcomes for investors. In this paper, we consider an order-driven financial market in which a fraction of the traders is only partially aware of the possible payoffs of a risky asset, but is aware of the possibility of facing unknown contingencies. Investors decide whether to acquire a costly signal about the payoff of the risky asset and whether to buy such asset given their awareness level and their perceived relations among signals, order flows, and prices. We show that as unawareness becomes more severe, the value of the signal to the partially aware traders diminishes. In turn, through its impact on the price, the reduced number of partially aware informed investors increases the incentives of the fully aware to acquire the signal. In the aggregate, the latter effect does not outweigh the former, so that the overall proportion of informed investors in the market is (weakly) decreasing in the unawareness level. As for the equilibrium price, a lower amount of informed traders makes it more difficult for market makers to distinguish between good and bad signals, and this brings the conditional expectations of the price closer to the unconditional one and reduces the price variance. • In a financial market, part of the traders is unaware of some asset payoffs. • Such traders are, however, aware of their unawareness. • More unawareness reduces the value of signals to such traders. • This increases the incentives of the fully aware to get informed. • Price expectations become more restricted, and the price variance diminishes. [ABSTRACT FROM AUTHOR]

The price of a house is affected by both the subjective and objective factors of the street environment in a neighborhood. However, the relationships between these factors and housing prices are not fully understood. Street view imagery (SVI) has recently emerged as a new data source for housing price studies. The SVI contains both objective and subjective information and can be used to extract objective measurements describing the physical environment and subjective measurements depicting human perceptions. Compared to conventional methods, there is consistency between subjective and objective information extracted from SVIs, and the two types of information are acquired from the perspective of the human visual perceptual system. Therefore, using both objective and subjective information extracted from street view images to study their relationship with housing prices has several advantages. In this study, focusing on the city of Suzhou, China, we extracted subjective perception and objective view indices from SVIs and systematically assessed their effects on housing prices. The global ordinary least squares (OLS) regression model and the local geographically weighted regression (GWR) model were used to model the correlations between these measures and housing prices. The OLS reveals that overall objective measures have stronger explanatory power, and built environment factors have a greater impact on housing prices. GWR shows that subjective factors can explain more variance in housing prices on the local scale and that home buyers care more about the subjective perceptions of the neighborhood's surroundings. The map of the GWR local coefficients demonstrates that the perception indicators have both positive and negative effects on housing prices in different places. In addition, a Monte Carlo test was performed to verify the spatially varying relationships between these measures. Our findings provide important references for urban designers and guide various applications, such as safe neighborhood design and sustainable city planning. [ABSTRACT FROM AUTHOR]

We develop an arbitrage pricing theory framework extension to study the pricing of squared returns/volatilities. We analyze the interplay between factors at the return level and those in idiosyncratic variances. We confirm the presence of a common idiosyncratic variance factor, but do not find evidence that this represents a missing risk factor at the (linear) return level. Thereby, we consistently identify idiosyncratic returns. The price of the idiosyncratic variance factor identified by squared returns is small relative to the price of market variance risk. The quadratic pricing kernels induced by our model are in line with standard economic intuition. [ABSTRACT FROM AUTHOR]

This study conducted a comprehensive analysis of multiple supervised machine learning models, regressors and classifiers, to accurately predict diamond prices. Diamond pricing is a complex task due to the non-linear relationships between key features such as carat, cut, clarity, table, and depth. The analysis aimed to develop an accurate predictive model by utilizing both regression and classification approaches. To preprocess the data, the study employed various techniques. The work addressed outliers, standardized the predictors, performed median imputation of missing values, and resolved multicollinearity issues. Equal-width binning on the cut variable was performed to handle class imbalance. Correlation-based feature selection was utilized to eliminate highly correlated variables, ensuring that only relevant features were included in the models. Outliers were handled using the inter-quartile range method, and numerical features were normalized through standardization. Missing values in numerical features were imputed using the median, preserving the integrity of the dataset. Among the models evaluated, the RF regressor exhibited exceptional performance. It achieved the lowest root mean squared error (RMSE) of 523.50, indicating superior accuracy compared to the other models. The RF regressor also obtained a high R-squared ( R 2 ) score of 0.985, suggesting it explained a significant portion of the variance in diamond prices. Furthermore, the area under the curve with RF classifier for the test set was 1.00 (100 %) , indicating perfect classification performance. These results solidify the RF's position as the best-performing model in terms of accuracy and predictive power, both in regression and classification. The MLP regressor showed promising results with an RMSE of 563.74 and an R 2 score of 0.980, demonstrating its ability to capture the complex relationships in the data. Although it achieved slightly higher errors than the RF regressor, further analysis is needed to determine its suitability and potential advantages compared to the RF regressor. The XGBoost Regressor achieved an RMSE of 612.88 and an R 2 score of 0.972, indicating its effectiveness in predicting diamond prices but with slightly higher errors compared to the RF regressor. The Boosted Decision Tree Regressor had an RMSE of 711.31 and an R 2 score of 0.968, demonstrating its ability to capture some of the underlying patterns but with higher errors than the RF and XGBoost models. In contrast, the KNN regressor yielded a higher RMSE of 1346.65 and a lower R 2 score of 0.887, indicating its inferior performance in accurately predicting diamond prices compared to the other models. Similarly, the Linear Regression model performed similarly to the KNN regressor, with an RMSE of 1395.41 and an R 2 score of 0.876. The Support Vector Regression model showed the highest RMSE of 3044.49 and the lowest R 2 score of 0.421, indicating its limited effectiveness in capturing the complex relationships in the data. Overall, the study demonstrates that the RF outperforms the other models in terms of accuracy and predictive power, as evidenced by its lowest RMSE, highest R 2 score, and perfect classification performance. This highlights its suitability for accurately predicting diamond prices. The study not only provides an effective tool for the diamond industry but also emphasizes the importance of considering both regression and classification approaches in developing accurate predictive models. The findings contribute valuable insights for pricing strategies, market trends, and decision-making processes in the diamond industry and related fields. [ABSTRACT FROM AUTHOR]

In 2013, the Mexican Constitution was amended to allow private firms to participate in the energy sector market. Consequently, the energy reform opened the energy market to private investors, ending the state monopoly of PEMEX and CFE. This article aims to assess the impact of the 2013 Mexican Energy Reform on energy household consumption and, if proven effective, explore its potential to help achieve SDG 7. This longitudinal study gathered data before and after the energy bill reform, from 2012 to 2018, with a non-experimental design. Data analysis to determine the effect of the price variance was estimated through price elasticities of demand, and a logarithmic model was used to determine the relationship between the price and cost of electricity, gas, and fuel. Findings suggest that the 2013 Mexican Energy Reform led to an increase in energy prices that, on the one hand, reduced the consumption of energy generated using fossil hydrocarbons but, on the other hand, affected the portion of the population with less income. Consequently, it is possible to conclude that the 2013 Mexican Energy Reform is irreconcilable with SDG 7 unless substantial additional efforts are made to leave no one behind. [ABSTRACT FROM AUTHOR]

This paper introduces an asymmetric robust weighted least squares (ARLS) approach to improve the forecasting performance of the heterogeneous autoregressive model for realized volatility. The ARLS approach down‐weights extreme observations to limit the bad influence of outliers on the estimated parameters. Compared with existing robust regression methods, our model further takes into account the asymmetry of outliers using a class of kernel functions. Out‐of‐sample results show the ARLS approach can generate more accurate forecasts of the S&P 500 index realized volatility in the statistical and economic senses. The model that considers the asymmetry of outliers gains superior performance among various robust regression competitors. The forecasting improvements also hold in other international stock markets. More importantly, the source of the predictive ability of the ARLS model comes from the less biased and more efficient parameter estimation. [ABSTRACT FROM AUTHOR]

Research on options models is still relevant to help buyers determine the fairness of option prices. The Black- Scholes model assumes that the stock price is lognormally distributed, whereas, in real applications, the stock price data does not match this assumption because it has different skewness and kurtosis values from the normal distribution. This condition is more suitable to be solved by non-normal models such as the Gram-Charlier and Variance Gamma. To reduce the variances, there are Antithetic Variate and Importance Sampling techniques. In this paper, we discuss an empirical study of option prices under skewness and kurtosis conditions using reduced variance techniques from two options of automotive stock (NIO) and technology stock (INTC), where we want to investigate the performance of those methods in the estimation of the stock call option price model in those two stocks. From the analysis, we found that those techniques can reduce the option price variance and give a more accurate price, where the Variance Gamma models produce the smallest MAPE compared to the other models used. [ABSTRACT FROM AUTHOR]

In this paper, we compute residual variance of art prices to examine asset pricing in contemporary art market. Our empirical work shows a few interesting results. First, we discover that the residual variance is significantly and positively related to the average price level achieved by an artist. Second, the residual variance has additional explanatory power in terms of how often the artist's works are cited and exhibited, even after we control for artist fixed (reputation) effects. Third, collectors tend to value more those artworks with higher residual variance. Artworks by those artists with high residual variance tend to outperform the market within and out of sample. One possible explanation of our results is that residual variance could be a proxy for creative risk taking by contemporary artists. The most creative artists dare to take more risks, which results in higher residual price volatility of their artworks. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of pricing variance, volatility, covariance and correlation swaps for financial markets with semi-Markov volatilities. The paper's motivation derives from the fact that in many financial markets, the inter-arrival times between book events are not independent or exponentially distributed but instead have an arbitrary distribution, which means they can be accurately modelled using a semi-Markov process. Through the results of the paper, we hope to answer the following question: Is it possible to calculate averaged swap prices for financial markets with semi-Markov volatilities? This question has not been considered in the existing literature, which makes the paper's results novel and significant, especially when one considers the increasing popularity of derivative securities such as swaps, futures and options written on the volatility index VIX. Within this paper, we model financial markets featuring semi-Markov volatilities and price-averaged variance, volatility, covariance and correlation swaps for these markets. Formulas used for the numerical evaluation of averaged variance, volatility, covariance and correlation swaps with semi-Markov volatilities are presented as well. The formulas that are detailed within the paper are innovative because they provide a new, simplified method to price averaged swaps, which has not been presented in the existing literature. A numerical example involving the pricing of averaged variance, volatility, covariance and correlation swaps in a market with a two-state semi-Markov process is presented, providing a detailed overview of how the model developed in the paper can be used with real-life data. The novelty of the paper lies in the closed-form formulas provided for the pricing of variance, volatility, covariance and correlation swaps with semi-Markov volatilities, as they can be directly applied by derivative practitioners and others in the financial industry to price variance, volatility, covariance and correlation swaps. [ABSTRACT FROM AUTHOR]

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The existence of a negative variance risk premium on agricultural futures contracts suggests that market participants pay to hedge unexpected increases in the volatility of these contracts. In this paper, we decompose the variance risk premium in corn and soybeans markets into jump and diffusive components using options and futures data from 2009 to 2021. We find that market participants on average only pay to hedge unexpected increases in jump volatility but not those in diffusive volatility. Furthermore, growing season uncertainty and the arrival of United States Department of Agriculture (USDA) announcements play important roles in driving the market's fear of unexpectedly large price jumps. [ABSTRACT FROM AUTHOR]

In this study, we document the accrual-based earnings management of Old Economy firms and New Economy firms (firms in the technology industry) and loss-making firms (firms with negative earnings in the pre-pandemic year, 2019) and profit firms in each economy, respectively, before, during, and in the recovery year of the COVID-19 pandemic. Using both univariate and difference-in-difference regression analyses, we find that old and new economy firms adopt different accrual-based earnings management, and Old Economy Loss firms changed their accrual-based earnings management the most during and in the recovery of the pandemic. During the 2020 pandemic, Old Economy Loss reported the lowest amount of accrual-based discretionary accruals. This suggests that Old Economy Loss firms are engaged in the most conservative approach to reporting their earnings, consistent with the big bath proposition. In the recovery year of the pandemic, 2021, we find that accrual-based earnings management reversed, with the old economy losing firms reporting the highest amounts of discretionary accruals. However, we do not find that the explanatory power of earnings on the variance of stock prices for the old economy loss firms is affected by their discretionary change in accounting accruals. [ABSTRACT FROM AUTHOR]

State monopoly hinders market integration through rising interregional trade barriers. This article provides an account of the market reform on dismantling state monopoly as a natural experiment to investigate the extent to which the reform affects domestic market integration. The state monopoly on China's edible salt market was dismantled by implementing a market reform on the first of January 2017 that terminated the 2000‐year state monopoly on edible salt. Using a set of unique retailing price datasets on edible salt across cities over 10‐day periods every month, we take advantage of the regional and temporal variances of edible salt retailing price between origins of production and other cities to demonstrate market integration nationwide in China. The regression discontinuity estimation is leveraged based on an apparently sharp discontinuity in inter‐city price differences. The results suggest that the dissolution of state monopoly on edible salt market leads to a 3.14 percent decrease in price differences of edible salt between origins and other cities, thus ultimately resulting in the promoted market integration. The results also suggest that the varied iodine contents in edible salt, the market power of state‐owned enterprises and the government‐firm relationships exert promising roles in the influence. [ABSTRACT FROM AUTHOR]

We provide the methods for searching for the unique Nash equilibrium using mixed integer programming techniques. We also simulate the model parameters using this technique and derive the numerical solution to show the characteristics of a key player providing both public goods as suggested by Bergstrom et al. (1986). We present two key results. First, when the number of players decreases, the appearance rate of the unique equilibrium accompanying a key player increases. Second, when the variance of the preference or price parameters decreases, the appearance rate of the unique equilibrium accompanying a key player increases. [ABSTRACT FROM AUTHOR]

In this paper, we creatively price the discretely sampled variance swaps under the mean-reverting Gaussian model (MRG model in short) with regime-switching asymmetric double exponential jump diffusion. We extend the traditional MRG model by further considering the trend of the financial market as well as a sudden and unexpected event of the market. This new model is meaningful because it uses observable Markov chains that represent market states to adjust its parameters, which helps capture the movement of the market and fluctuations in asset prices. By utilizing the characteristic function and the conditional transition characteristic function, we obtain analytical solutions for pricing formulae. Note that this is our first effort to provide the analytical solution for the ordinary differential equations satisfied by the Feynman–Kac theorem. To achieve this, we have developed a new methodology in Proposition 2 that involves dividing the sampling interval into more detailed switching and non-switching intervals. One significant advantage of our closed-form solution is its high computational accuracy and efficiency. Subsequent semi-Monte Carlo simulations will provide specific validation results. [ABSTRACT FROM AUTHOR]

Supply chain price variability, also known as the "Bullwhip effect in Pricing (BP)," refers to the absorption or amplification of the variability of prices from one stage to another in a supply chain. This article derives analytical conditions that result in BP considering a buyback contract and conducts numerical simulations to gain further insights. For this, a joint price and replenishment setting newsvendor model with a wholesale-Stackelberg game is considered. Two demand types (linear and isoelastic) are analyzed along with uniformly and normally distributed additive and multiplicative uncertainties. The outcome of this research reveals that the main influential factors that affect BP are the structure and error type of the demand functions. Absorption (amplification) in price fluctuations occurs for linear (isoelastic) demand cases. Moreover, the price variances and BP ratios differ under the buyback and wholesale-price-only cases. The overall results help understand the fluctuation of market prices under various conditions. [ABSTRACT FROM AUTHOR]

In this paper, we extend the popular Barndorff–Nielsen–Shephard stochastic volatility model to the case of a pure-jump Ornstein–Uhlenbeck equation with non-vanishing stochastic mean-reversion level. Based on this setup, we derive representations for the squared VIX process and related VIX futures prices. Having these results at hand, we introduce an initially enlarged filtration which models the view of a VIX market insider who has knowledge about the future behavior of the stochastic mean-reversion level of the squared volatility process available. In this enlarged filtration framework, we infer an explicit representation for the anticipative VIX process and obtain the associated time dynamics. We finally investigate the pricing of variance swaps under both backward- and forward-looking information. [ABSTRACT FROM AUTHOR]

The duration times the credit spread of a bond, denoted DTS, is an effective proxy for its price variance. On an aggregate level, the measure is key to specifying the covariance between bond prices as well. Using a sample of government bond market indices, the author shows that the duration and spread, both on an index level, explain the largest share of the price variance and covariance between government bond markets. The bonds in the indices are denominated in local currency and are hedged against exchange-rate risk. The findings provide new insights for managing bond risk in globally invested portfolios. [ABSTRACT FROM AUTHOR]

In January of 2021, GameStop Corp., a struggling brick and mortar retail video game company, saw its share price increase by 2,700 percent. This Note looks at the prevailing forces that caused this meteoric rise and how the law should respond. Ultimately, such price volatility is detrimental to the stability of the securities market, so regulators should bring action against any bad actors that cause this type of volatility. This Note concludes that the price increase was the result of market manipulation on the part of retail investors who were communicating through social media. Under the current approach in most jurisdictions, however, these retail investors fail to satisfy a claim for market manipulation; therefore, this Note argues that courts and regulators need to rethink the approach to market manipulation by expanding the scope of unlawful manipulative behavior. Specifically, this Note argues that courts must universally recognize that openmarket manipulation violates securities law. [ABSTRACT FROM AUTHOR]

Masstige or mass prestige luxury brands are considered one of the principal drivers behind increased luxury consumption. Literature has proposed increasing globalization as one of the primary reasons behind the sales of these masstige brands. Given the impact of globalization, this research proposes and examines the impact of consumers' global and local identities on their propensity to purchase traditional luxury and masstige brands. Results from a survey of 278 Indian respondents show a differential impact of global and local identities of consumers on these preferences. Further, the results show the serial mediation of luxuriousness variances and price luxuriousness inferences behind this impact. The results also indicate that the mediating effect of price luxuriousness inference is moderated by consumer flexibility, where consumer flexibility is a facet of consumer wisdom. The traditional luxury and masstige brand managers can use these results to formulate segmenting, targeting, and positioning strategies for their brands. [ABSTRACT FROM AUTHOR]

Previous literature suggests that daily changes in US natural gas (NG) futures prices and their variance can be explained by changes in oil futures prices and volatilities, storage announcements, and temperature shocks. These studies measure temperature shocks using the National Oceanic and Atmospheric Administration definitions of heating degree day (HDD) and cooling degree day (CDD). We show how hourly temperatures can be used to better measure how long as well as how much temperatures depart above and/or below the "comfort level" within a day, the effects of temperature variability within a day, and the effects of thermal inertia. Hourly temperatures also allow us to construct measures of extreme temperatures that better match the timing of release of other market information. Another innovation is that we use more economically relevant weights to convert regional temperature shocks to national averages, and measure expected values of variables via econometric models instead of historical averages. Our results show that the proposed measures of HDD and CDD and temperature changes are important for explaining weekly changes in NG in storage and daily changes in NG futures prices and their variance. [ABSTRACT FROM AUTHOR]

Crude Oil is one of the most important commodities in this world. We have studied the effects of Crude Oil inventories on crude oil prices over the last ten years (2011 to 2020). We tried to figure out how the Crude Oil price variance responds to inventory announcements. We then introduced several other financial instruments to study the relation of these instruments with Crude Oil variation. To undertake this task, we took the help of several mathematical tools including machine learning tools such as Long Short Term Memory(LSTM) methods, etc. The previous researches in this area primarily focussed on statistical methods such as GARCH (1,1) etc. (Bu (2014)). Various researches on the price of crude oil have been undertaken with the help of LSTM. But the variation of crude oil price has not yet been studied. In this research, we studied the variance of crude oil prices with the help of LSTM. This research will be beneficial for the options traders who would like to get benefit from the variance of the underlying instrument. [ABSTRACT FROM AUTHOR]

We build on recent work that analyzes consumers' ability to save by exploiting price dispersion in grocery stores. We show that store expensiveness varies across consumers depending on the basket they consume, meaning that consumers can save more by shopping at a store that is cheaper for their basket rather than at a store that is cheaper overall. We incorporate this insight into a new price variance decomposition that is a refinement of existing approaches. Our results show that the ability to buy products from the store where they are cheapest is much less important than previous work had found; rather, the ability to choose the cheapest stores for one's basket is a more important source of variation in the prices consumers pay. Our approach also provides an informal test for competing theories modeling consumers as either shopping for products or shopping for categories, and finds support for both. We conclude that the idea of consumers choosing the right store for their basket has substantial traction and is a useful addition to our arsenal of models of consumer search behavior. [ABSTRACT FROM AUTHOR]

The fluctuation of vegetable prices in recent years underscores the need to identify contributing factors and develop effective policies. In order to examine the factors affecting the fluctuation of vegetable prices, this paper uses a structural model constructed by demand, supply, import, and export functions to decompose price variance, and also performs a numerical simulation to generalize the results. We studied the Korean vegetable market, and selected cabbage, radish, dried red pepper, garlic, and onion as research objects. The results indicates that variability of domestic production is the primary factor that influences price fluctuations in the Korean vegetable market. In contrast, our analysis revealed that demand, import, and export had a limited impact on price fluctuations in the Korean vegetable market, except for dried red pepper and onion. [ABSTRACT FROM AUTHOR]

The restaurant industry has historically been limited in its ability to adopt traditional revenue management pricing practices (e.g., variable pricing across tables and times) because of three specific challenges: (a) inability to segment customers by willingness to pay prior to seating, (b) limited ability to price discriminate (i.e., prioritize limited seating for the highest paying customers), and (c) inability to communicate menu price variances in advance. This article reviews common restaurant pricing strategies and discusses how each strategy cannot sufficiently address these three challenges. This work proposes a new strategy, the Priority Mixed Bundle (PMB) Strategy, which addresses all three of these challenges. The PMB states that customers can make reservations if they are willing to commit to dining from a prix-fixe menu while walk-ins can dine a la carte. The article argues for why PMB is theoretically viable and could be superior to existing menu pricing strategies. A field study shows that the PMB generates more revenue than a la carte strategies. Survey results suggest that customers perceive PMB as fair. Overall, this research advances theory in restaurant revenue management and proposes a pricing strategy for restaurants. [ABSTRACT FROM AUTHOR]

In this paper, we propose dynamic, short-term, financial risk management strategies for small electricity producers and buyers that trade in the wholesale electricity markets. Since electricity is mostly nonstorable, financial risk coming from extremely volatile electricity prices cannot be reduced by using standard finance-based approaches. Instead, a short-term operational planing and a proper trade diversification might be used. In this paper, we analyze the price risk in terms of the Markowitz mean–variance portfolio theory. Hence, it is crucial to forecast properly the variance of electricity prices. To this end, we jointly model day-ahead and intraday or balancing prices from Germany and Poland using ARX-GARCH type models. We show that using heteroscedastic volatility significantly improves probabilistic price forecasts according to the pinball score, especially if variance stabilizing transformation is applied prior to a model estimation. The price forecasts are then used for construction of dynamic diversification strategies that are based on volatility-type risk measures. We consider different objectives as well as a buyer's and a seller's perspective. The proposed strategies are applied for the diversification of trade among different markets in Germany and Poland. We show that the objective of the strategy can be achieved using the proposed approach, but the risk minimization is usually related to lower profits. We find that risk minimization is especially important for a seller in both markets, while for a buyer a profit maximization objective leads to a more optimal risk–return trade-off. [ABSTRACT FROM AUTHOR]

This paper analyzes the price behavior of Phase III (2013–2020) EU-ETS emission allowances of CO2 by focusing on the dynamics of daily auction equilibrium prices and on the changes of the volatility of the underlying stochastic process. The paper derives the main testable statistical hypotheses (particularly that about the determinants of the conditional variance of prices) on the results derived in a model of optimal bidders' behavior given the ETS auction rules. Tests are conducted by estimating various versions of GARCH models for both mean and variance equations of price return. Results show that the price dynamics is affected by factors including a measure of excess demand/offer and the number of winning bidders and that, contrary to expectations, reforms of the auction rules introduced at the end of Phase III explain a great part of the increased price volatility. The increased volatility is also positively associated with the bid spread and negatively associated with the number of bidders active in each auction round. [ABSTRACT FROM AUTHOR]

We investigate price duration variance estimators that have long been neglected in the literature. In particular, we consider simple-to-construct non-parametric duration estimators, and parametric price duration estimators using autoregressive conditional duration specifications. This paper shows (i) how price duration estimators can be used for the estimation and forecasting of the integrated variance of an underlying semi-martingale price process and (ii) how they are affected by discrete and irregular spacing of observations, market microstructure noise, and finite price jumps. Specifically, we contribute to the literature by constructing the asymptotic theory for the non-parametric estimator with and without the presence of bid/ask spread and time discreteness. Further, we provide guidance about how our estimators can best be implemented in practice by appropriately selecting a threshold parameter that defines a price duration event, or by averaging over a range of non-parametric duration estimators. We also provide simulation and forecasting evidence that price duration estimators can extract relevant information from high-frequency data better and produce more accurate forecasts than competing realized volatility and option-implied variance estimators, when considered in isolation or as part of a forecasting combination setting. [ABSTRACT FROM AUTHOR]

As is well known, multi-factor stochastic volatility models are necessary to capture the market accurately in pricing financial derivatives. However, the multi-factor models usually require too many parameters to be calibrated efficiently and they do not lead to an analytic pricing formula. The double Heston model is one of them. The approach of this paper for this difficulty is to rescale the double Heston model to reduce the number of the model parameters and obtain a closed form analytic solution formula for variance swaps explicitly. We show that the rescaled double Heston model is as effective as the original double Heston model in terms of fitting to the VIX market data in a stable condition and yet the computing time is much less than that under the double Heston model. However, in a turbulent situation after the start of the COVID-19 pandemic in 2020, we acknowledge that even the double Heston model fails to capture the market accurately. [ABSTRACT FROM AUTHOR]

Then, starting from the original dataset, one can construct a coarse-grained version of it with a sampling scale Graph HT ht . One can "zoom-out" in time, by defining a new coarse-grained model with a sampling scale Graph HT ht . [Extracted from the article]

In this article, we focus on the pricing of exchange options when the risk-neutral dynamic of log-prices follows either the well-known variance gamma or the recent variance gamma++ process introduced in Gardini et al. (2022. "The Variance Gamma++ Process and Applications to Energy Markets." Applied Stochastic Models in Business and Industry 38 (2): 391–418. .). In particular, for the former model we can derive a Margrabe's type formula whereas for the latter one we can write an 'integral free' formula. Furthermore, we show how to construct a general multidimensional versions of the variance gamma++ processes preserving both the mathematical and numerical tractabilities. Finally we apply the derived models to German and French energy power markets: we calibrate their parameters using real market data and we accordingly evaluate exchange options with the derived closed formulas, Fourier based methods and Monte Carlo techniques. [ABSTRACT FROM AUTHOR]

• Introduces the Heteroscedastic Temporal Convolutional Network for day-ahead electricity price forecasting. • Employs a heteroscedastic output layer to represent variable uncertainty. • Utilizes a maximum likelihood estimation-based loss function to handle heteroscedasticity. • Integrates a multi-view feature selection algorithm to improve forecast precision. • Demonstrates state-of-the-art performance on multiple electricity datasets. Electricity price forecasting (EPF) is a complex task due to market volatility and nonlinearity, which cause rapid and unpredictable fluctuations and introduce heteroscedasticity in forecasting. These factors result in varying prediction errors over time, making it difficult for models to capture stable patterns and leading to poor performance. This study introduces the Heteroscedastic Temporal Convolutional Network (HeTCN), a novel Encoder-Decoder framework designed for day-ahead EPF. HeTCN utilizes a Temporal Convolutional Network (TCN) to capture long-term dependencies and cyclical patterns in electricity prices. A key innovation is the heteroscedastic output layer, which directly represents variable uncertainty, enhancing performance under fluctuating market conditions. Additionally, a multi-view feature selection algorithm identifies crucial factors for specific periods, improving forecast precision. The framework employs an improved loss function based on maximum likelihood estimation (MLE), which adjusts for the heteroscedastic nature of electricity prices by predicting both the mean and variance of the price distribution. This approach mitigates the impact of extreme price spikes and reduces overfitting, resulting in robust and reliable predictions. Comprehensive evaluations demonstrate HeTCN's superiority over existing solutions such as DeepAR and the Temporal Fusion Transformer (TFT), with average improvements of 25.3%, 24.9%, and 17.4% in the mean absolute error (MAE), symmetric mean absolute percentage error (sMAPE), and the root of mean squared error (RMSE) compared to DeepAR, and 17.6%, 14.4%, and 13.6% relative to TFT across five distinct electricity markets. These results underscore HeTCN's effectiveness in managing volatility and heteroscedasticity, marking a significant advancement in electricity price forecasting. [ABSTRACT FROM AUTHOR]