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We propose a novel model-free approach to obtain the joint risk-neutral distribution among several assets that is consistent with options on these assets and their weighted index. We implement this approach for the nine industry sectors comprising the S&P 500 index and find that their option-implied dependence is highly asymmetric and time-varying. We then study two conditional correlations: when the market moves down or up. The risk premium is strongly negative for the down correlation but positive for the up correlation. Intuitively, investors dislike the loss of diversification when markets fall, but they actually prefer high correlation when markets rally. [ABSTRACT FROM AUTHOR]

The cross section of options holds great promise for identifying return distributions and risk premia, but estimating dynamic option valuation models with latent state variables is challenging when using large option panels. We propose a particle Markov Chain Monte Carlo framework with a novel filtering approach and illustrate our method by estimating index option pricing models. Estimates of variance risk premiums, variance mean reversion, and higher moments differ from the literature. We show that these differences are due to the composition of the option sample. Restricting the option sample's maturity dimension has the strongest impact on parameter inference and option fit in these models. [ABSTRACT FROM AUTHOR]

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We document the causal effects of single-name options trading on the absolute level of information content of prices (stock price informativeness) by exploiting the Penny Pilot Program as an exogenous shock to options trading volume. We find that options trading increases underlying stock price informativeness and information acquisition by both option and stock investors, consistent with the framework of Goldstein and Yang (2015). The findings are driven by firms for which options are more important sources of information and firms with more efficiently priced options. Options market introduction in a sample of 25 other economies also leads to higher price informativeness. [ABSTRACT FROM AUTHOR]

This work introduces a new stochastic volatility model with delay parameters in the volatility process, extending the Barndorff–Nielsen and Shephard model. It establishes an analytical expression for the log price characteristic function, which can be applied to price European options. Empirical analysis on S&P500 European call options shows that adding delay parameters reduces mean squared error. This is the first instance of providing an analytical formula for the log price characteristic function in a stochastic volatility model with multiple delay parameters. We also provide a Monte Carlo scheme that can be used to simulate the model. [ABSTRACT FROM AUTHOR]

The Black–Scholes equation is one of the most important partial differential equations governing the value of financial derivatives in financial markets. The Black–Scholes model for pricing stock options has been applied to various payoff structures, and options trading is based on Black and Scholes' principle of dynamic hedging to estimate and assess option prices over time. However, the Black–Scholes model requires severe constraints, assumptions, and conditions to be applied to real-life financial and economic problems. Several methods and approaches have been developed to approach these conditions, such as fractional Black–Scholes models based on fractional derivatives. These fractional models are expected since the Black–Scholes equation is derived using Ito's lemma from stochastic calculus, where fractional derivatives play a leading role. Hence, a fractional stochastic model that includes the basic Black–Scholes model as a special case is expected. However, these fractional financial models require computational tools and advanced analytical methods to solve the associated fractional Black–Scholes equations. Nevertheless, it is believed that the fractal nature of economic processes permits to model economical and financial markets problems more accurately compared to the conventional model. The relationship between fractional calculus and fractals is well-known in the literature. This study introduces a generalized Black–Scholes equation in fractal dimensions and discusses its role in financial marketing. In our analysis, we consider power-laws properties for volatility, interest rated, and dividend payout, which emerge in several empirical regularities in quantitative finance and economics. We apply our model to study the problem of pricing barrier option and we estimate the values of fractal dimensions in both time and in space. Our model can be used to obtain the prices of many pay-off models. We observe that fractal dimensions considerably affect the solutions of the Black–Scholes equation and that, for fractal dimensions much smaller than unity, the call option increases significantly. We prove that fractal dimensions are a powerful tool to obtain new results. Further details are analyzed and discussed. [ABSTRACT FROM AUTHOR]

How can national policies to promote food self-sufficiency also enable agroecological transitions? Mexico’s recent agricultural policy priorities shifted from larger, irrigated farmers to focus mainly on rainfed smallholders. Recent national policies emphasize both food self-sufficiency and agroecological goals. Yet government funding for conventional agriculture also continued – including the national scaling up of a program that delivered free chemical fertilizer to smallholders. This study uses institutional analysis to analyze this program from the perspective of a state-wide farmer-led oversight campaign led by advocates of sustainable agriculture. Community-based policy monitoring combined with multi-level advocacy broadened access to the program and reduced corruption, while also calling for biofertilizer options and investment in soil health. [ABSTRACT FROM AUTHOR]

The authors of Kaur and Natesan 2023 [A novel numerical scheme for time-fractional Black-Scholes PDE governing European options in mathematical finance, (Numerical Algorithms, 94, (2023) 1519–1549)] proposed a numerical scheme, which is based on a combination of L1 scheme for time discretization and spine method for spatial discretization, for solving a Caputo temporal-fractional Black-Scholes (TBS) equation governing European options. This method is (2 - α) -th order accurate in time and second-order accurate in space. The present work deals with the construction of a high-order numerical scheme for solving the same time-fractional problem. In this method, the L 2 - 1 σ scheme is employed to approximate the Caputo temporal-fractional derivative, while a high-order compact finite difference (CFD) method is proposed for the approximation of space derivatives. The existence and uniqueness of the solution to the TBS model are investigated through the application of the maximum-minimum principle and the Sumudu decomposition technique. The stability and convergence of the method are investigated. The proposed method is shown to be second-order accuracy in time and fourth-order accuracy in space. Numerical experiments are performed to illustrate the accuracy of the suggested numerical scheme and validate the theoretical results. Moreover, the present numerical scheme is used for pricing the European double barrier knock-out (EDBK) option. The execution time of the present numerical algorithm is provided. In order to justify the advantage of present method, the computed results are compared with the results obtained by the methods in Kaur and Natesan 2023 [A novel numerical scheme for time-fractional Black-Scholes PDE governing European options in mathematical finance, (Numerical Algorithms, 94, (2023) 1519-1549)] and Zhang et al. 2016 [Numerical solution of the time-fractional Black-Scholes model governing European options, Comput. Math. Appl. 71 (2016) 1772-1783]. [ABSTRACT FROM AUTHOR]

We introduce a new identification strategy for uncertainty shocks to explain macroeconomic volatility in financial markets. The Chicago Board Options Exchange Volatility Index (VIX) measures the market expectations of future volatility, but traditional methods based on second-moment shocks and the time-varying volatility of the VIX often do not effectively to capture the non-Gaussian, heavy-tailed nature of asset returns. To address this, we constructed a revised VIX by fitting a double-subordinated Normal Inverse Gaussian Lévy process to S&P 500 log returns, to provide a more comprehensive measure of volatility that captures the extreme movements and heavy tails observed in financial data. Using an axiomatic framework, we developed a family of risk–reward ratios that, when computed with our revised VIX and fitted to a long-memory time series model, provide a more precise identification of uncertainty shocks in financial markets. [ABSTRACT FROM AUTHOR]

The present work demonstrates a comparative study of hydrogen fuel cells and combustion aircraft to investigate the potential of fuel cells as a visionary propulsion system for radically more sustainable medium- to long-range commercial aircraft. The study, which considered future airframe and propulsion technologies under the Se2A project, was conducted to quantify potential emissions and costs associated with such aircraft and to determine the benefits and drawbacks of each energy system option for different market segments. Future technologies considered in the present work include laminar flow control, active load alleviation, new materials and structures, ultra-high bypass ratio turbofan engines, more efficient thermal management systems, and superconducting electric motors. A multi-fidelity initial sizing framework with coupled constraint and mission analysis blocks was used for parametric airplane sizing and calculations of all necessary characteristics. Analyses performed for three reference aircraft of different sizes and ranges concluded that fuel-cell aircraft could have operating cost increases in the order of 30% compared to hydrogen combustion configurations and were caused by substantial weight and fuel burn increases. In-flight changes in emissions of fuel cell configurations at high altitudes were progressively reduced from medium-range to long-range segments from being similar to hydrogen combustion for medium-range to 24% for large long-range aircraft, although fuel cell aircraft consume 22–30% more fuel than combustion aircraft. Results demonstrate a positive environmental impact of fuel cell propulsion for long-range applications, the possibilities of being a more emission-universal solution, if desired optimistic technology performance metrics are satisfied. The study also demonstrates progressively increasing technology requirements for larger aircraft, making the long-range application's feasibility more challenging. Therefore, substantial development of fuel cell technologies for long-range aircraft is imperative. The article also emphasizes the importance of airframe and propulsion technologies and the necessity of green hydrogen production to achieve desired emissions. [ABSTRACT FROM AUTHOR]

The standard model, which determines option pricing, is the well-known Black-Scholes formula. Heston in addition to Cox-Ingersoll-Ross which is called CIR, respectively, implemented the models of stochastic volatility and interest rate to the standard option pricing model. The cost of transaction, which the Black-Scholes method overlooked, is another crucial consideration that must be made when trading a service or production. It is acknowledged that by employing the log-normal stock diffusion hypothesis with constant volatility, the Black-Scholes model for option pricing departs from reality. The standard log-normal stock price distribution used in the Black-Scholes model is insufficient to account for the leaps that regularly emerge in the discontinuous swings of stock prices. A jump-diffusion model, which combines a jump process and a diffusion process is a type of mixed model in the Black-Scholes model belief. Merton developed a jump model as a modification of jump models to better describe purchasing and selling behavior. In this study, the Heston-Cox-Ingersoll-Ross (HCIR) model with transaction costs is solved using the alternating direction implicit (ADI) approach and the Monte Carlo simulation assuming the underlying asset adheres to the jump-diffusion case, then the outcomes are compared to the analytical solution. In addition, the consistency of the numerical method is proven for the model. [ABSTRACT FROM AUTHOR]

Purpose: This paper investigates whether trading activity in the derivative markets, specifically options, can offer incremental information and insights into the underlying stock's valuation beyond what is reflected in the company's current stock price. By incorporating options market data into the analysis of an economic event, the proposed multivariate model aims to evaluate whether certain derivative investors may have capitalized on information not yet incorporated into the stock market price. Design/methodology/approach: The existing capital markets' literature typically considers stock prices and (abnormal) returns in response to economic events; thus, this paper examines alternative measures. This analysis introduces a multivariate regression model incorporating the put-call ratio as an independent variable and examining its association with stock prices. Findings: This paper investigates whether trading activity in the derivative markets, specifically options, can offer incremental information and insights regarding the underlying stock's valuation beyond what is reflected in the company's current stock price. By incorporating options market data into the analysis of an economic event, the proposed multivariate model aims to evaluate whether certain derivative investors may have capitalized on information not yet incorporated into the stock market price. Research limitations/implications: Access to historical option market data is not readily available and is relatively expensive. Thus, the analysis was limited to the sole event of the 2010 BP Deepwater Horizon oil spill. Originality/value: The introduction of including deviate market movements could be especially beneficial for an industry like oil and gas, where the product is a commodity, and the firm's value is largely driven by its identifiable assets and proven oil reserves. In addition, this may provide management insight into how the market perceives actions during an economic event. Based on the put-call ratio, management may be able to predict how their actions will impact future company share prices in the market. [ABSTRACT FROM AUTHOR]

ABSTRACT Adding an option is a neglected mechanism for bringing about behavioural change. This mechanism is distinct from nudges, which are changes in the choice architecture, and instead makes it possible to pursue republican paternalism, a unique form of paternalism in which choices are changed by expanding people's set of options. I argue that this is truly a form of paternalism (albeit a relatively soft one) and illustrate some of its manifestations in public policy, specifically public options and market creation. Furthermore, I compare it with libertarian paternalism on several dimensions, namely respect for individuals' agency, effectiveness, and efficiency. Finally, I consider whether policymakers have the necessary knowledge to successfully change behaviour by adding options. Given that adding an option has key advantages over nudges in most if not all of these dimensions, it should be considered indispensable in the behavioural policymaker's toolbox. [ABSTRACT FROM AUTHOR]

This paper investigates the valuation of vulnerable exchange options with two underlying assets that follow a two-factor volatility model. We employ a reduced-form model incorporating a Poisson process with stochastic intensity. The proposed reduced-form model depends on a stochastic intensity process that is guaranteed to remain positive and includes both systemic and idiosyncratic risks. Using measure change techniques and characteristic functions, we obtain an explicit pricing formula for vulnerable exchange options within the proposed framework. We also provide numerical examples demonstrating the sensitivity of option prices to significant parameters. [ABSTRACT FROM AUTHOR]

This study considers the American VIX option pricing problem under the models of mixing the 3/2, and 1/2 classes of volatility processes. The pricing formulas for the perpetual American VIX call are provided for the generalized 1/2 volatility model, the generalized 3/2 volatility model and the model mixing these two processes. For these models, we derive approximate formulas for the finite-lived American VIX call option by the quadratic approximation approach. Then, we illustrate the influence of the varied parameters on the free boundaries and the corresponding continuation regions through numerical analysis. Finally, in the real application, we obtain the price for the finite-lived American VIX call option using the CBOE volatility index. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic disrupted routine healthcare services, including testing and treatment for sexually transmitted infections (STIs). At the same time, it fostered optimism about the potential of direct-to-consumer (DTC) self-testing solutions, fueled by remarkable progress in self-sampling practices, rapid testing technologies, and the adaptation of regulatory frameworks. Direct-to-consumer (DTC) self-testing and self-sampling for STIs became then still a more critical alternative, offering privacy, accessibility, and the potential to address testing gaps, especially for underserved, at-risk groups. This review critically analyzed the literature published since the onset of the COVID-19 pandemic. DTC testing has evolved significantly since the pandemic, with notable advances in technology, availability, and disease and geographical coverage. It has increased STI testing uptake among underserved or hesitant populations, including men who have sex with men and young adults. However, issues around cost, data reporting, and follow-up care persist, and many at-risk groups still lack access. While these solutions have improved testing uptake and public health outcomes, anticipated widespread adoption and advances have been slower than expected. Limited market options, weak research, and regulatory challenges have hindered growth. Research has often focused on potential user acceptance rather than real-world usage. Future directions should leverage trendy approaches in medical ethnography and transformational marketing to emphasize user- and data-driven innovation, affordability, and regulatory support based on private provider accountability. [ABSTRACT FROM AUTHOR]

Fractional Brownian motion has become a standard tool to address long-range dependence in financial time series. However, a constant memory parameter is too restrictive to address different market conditions. Here, we model the price fluctuations using a multifractional Brownian motion assuming that the Hurst exponent is a time-deterministic function. Through the multifractional Itô calculus, both the related transition density function and the analytical European Call option pricing formula are obtained. The empirical performance of the multifractional Black–Scholes model is tested by calibration of option market quotes for the SPX index and offers best fit than its counterparts based on standard and fractional Brownian motions. [ABSTRACT FROM AUTHOR]

Classic options can no longer meet the diversified needs of investors; thus, it is of great significance to construct and price new options for enriching the financial market. This paper proposes a new option pricing model that integrates the logarithmic investment strategy with the classic Black–Scholes theory. Specifically, this paper focus on put options, introducing a threshold-based strategy whereby investors sell stocks when prices fall to a certain value. This approach mitigates losses from adverse price movements, enhancing risk management capabilities. After deriving an analytical solution, we utilized mathematical software to visualize the factors influencing new option prices in three-dimensional space. The findings suggest that the pricing of these new options is influenced not only by standard factors such as the underlying asset price, volatility, risk-free rate of interest, and time to expiration, but also by investment strategy parameters such as the investment strategy index, investment sensitivity, and holding ratios. Most importantly, the pricing of new put options is generally lower than that of classic options, with numerical simulations demonstrating that under optimal parameters the new options can achieve similar hedging effectiveness at approximately three-quarters the cost of standard options. These findings highlight the potential of logarithmic investment strategies as effective tools for risk management in volatile markets. To validate our theoretical model, numerical simulations using data from Shanghai 50 ETF options were used to confirm its accuracy, aligning well with theoretical predictions. The new option model proposed in this paper contributes to enhancing the efficiency of resource allocation in capital markets at a macro level, while at a micro level, it helps investors to apply investment strategies more flexibly and reduce decision-making errors. [ABSTRACT FROM AUTHOR]

Addressing global challenges together with weather exchange and environmental degradation calls for progressive financing solutions. Green finance has emerged as a crucial device for mobilizing capital to support sustainable development. This research examines the function of inexperienced finance in accomplishing the Sustainable Development Goals (SDGs), drawing insights from case studies and empirical analysis. The study unearths that green finance can undoubtedly contribute to realizing the SDGs, especially in areas like climate action, low-priced and easy strength, and enterprise, innovation, and infrastructure. However, the research also identifies key challenges, including the lack of standardization in defining "green" and the want for higher alignment among domestic financial frameworks and country wide SDG priorities. The paper affords policy recommendations for governments to create an allowing surroundings for green finance. This includes adopting a proactive method to fostering legal and regulatory frameworks that assist the mobilization of monetary resources for ecological, social, and monetary sustainability. The findings of this research can inform policymakers, financial institutions, and other stakeholders of their efforts to leverage green finance for sustainable improvement. [ABSTRACT FROM AUTHOR]

Carbon emissions trading is pivotal for advancing China's low-carbon goals. As the primary tradable asset in the carbon market, carbon emission allowances inevitably experience price fluctuations. However, numerous empirical studies show that the frequency of real-world data is highly unstable, which results in the failure of probabilistic modeling. Therefore, this paper aims to model the dynamics of carbon emission allowance prices in China using four mainstream uncertain differential equations. The optimal model is chosen through rolling window cross-validation using the criterion of minimizing average testing errors. Parameters of the optimal model are determined by moment estimation based on residuals, and the model's effectiveness is also assessed through uncertain two-sided hypothesis testing. Additionally, we forecast carbon emission allowance prices and their 95% confidence intervals for the next 14 business days. To manage trading risks, we propose a customized carbon option contract for pricing European carbon options and conduct sensitivity analysis on key parameters. Finally, we present a paradox of stochastic differential equations for modeling carbon emission allowance prices. [ABSTRACT FROM AUTHOR]

This paper considers a hidden action agency model in which an agent can be incentivized to simultaneously work and exert effort to invest in human capital or process innovation so as to increase productivity in a future period. We characterize the conditions under which the principal would and would not want to incentivize such multitasking and the conditions under which the agent would want to participate. The firm's support for investment is contingent on the agent's outside market option not being too high, and sufficient conditions for a separate contingent payment for productivity increase are presented. [ABSTRACT FROM AUTHOR]

We offer analytic formulae for valuing finite maturity profit caps and floors that are contingent on continuous flows without the need for subtracting the risk‐neutral expectation of the forward starting perpetual solution from the corresponding perpetual solution. The related price caps, floors, and collars are easily obtained from any analytic representation of profit caps and floors using some arbitrage‐free relations. Finally, we offer two novel methods for calculating the optimal triggers of investment projects in the presence of price floors and collars regimes in a way that is much simpler than the ones currently used. [ABSTRACT FROM AUTHOR]

Zoysiagrass (Zoysia spp. Willd.) is a popular choice for commercial and residential lawns, as well as golf course fairways and tees because it requires less light, fertilizer, and mowing compared to bermudagrass (Cynodon spp.). However, zoysiagrass' slow growth rate compared to other putting green options presents a challenge for both rapid establishment from sprigs and its wider adoption as a putting green surface. It is currently unknown if very frequent irrigation, commonly used in horticultural propagation, can be applied to turfgrass and accelerate establishment from sprigs. Irrigation was applied to Prizm zoysiagrass sprigs across either four or 192 irrigation events from 06:00 a.m. to 10:00 p.m. Prizm zoysiagrass establishment was unaffected by irrigation frequency and averaged 40% turfgrass coverage after 21.6 and 27.3 days in run A and run B, respectively. These results imply that very frequent irrigation did not accelerate establishment. Additional research elucidating zoysiagrass sprig physiology during propagation and development is necessary to better define management practices that hasten establishment. Core Ideas: Zoysiagrass' slow growth hinders its establishment rate and widespread use as a putting green surface.Irrigation frequency did not affect the establishment rate.Elevated vapor pressure deficit had no measurable effect on establishment. [ABSTRACT FROM AUTHOR]

Slightly over fifty years ago, the Black–Scholes option pricing model revolutionized investing by enabling a shift from linear to non-linear payoff structures. Myron Scholes later published two papers documenting the performance of passive option strategies that outperformed the underlying index on a risk–return basis. The options market has evolved considerably over the last fifty years from an open outcry trading structure with options being single-listed to a high-frequency computer-based market. This paper re-evaluates the trilogy of foundational studies to determine whether passive-option-enhanced portfolios still produce superior performance in the current high-frequency options market environment. [ABSTRACT FROM AUTHOR]

This study sought to ascertain a machine learning algorithm capable of predicting crises in the African stock market with the highest accuracy. Seven different machine-learning algorithms were employed on historical stock prices of the eight stock markets, three main sentiment indicators, and the exchange rate of the respective countries' currencies against the US dollar, each spanning from 1 May 2007 to 1 April 2023. It was revealed that extreme gradient boosting (XGBoost) emerged as the most effective way of predicting crises. Historical stock prices and exchange rates were found to be the most important features, exerting strong influences on stock market crises. Regarding the sentiment front, investors' perceptions of possible volatility on the S&P 500 (Chicago Board Options Exchange (CBOE) VIX) and the Daily News Sentiment Index were identified as influential predictors. The study advances an understanding of market sentiment and emphasizes the importance of employing advanced computational techniques for risk management and market stability. [ABSTRACT FROM AUTHOR]

This paper analyses forward‐looking estimates of the expected market return in Australian. By utilising option prices, we compute a lower bound for the capital gain and dividend components of the expected return. Over a 17‐year period, the average 1‐month expected return lower bound is found to be 8.6% per annum, compared with an average realised return of 10.9% per annum. Our option‐based estimates demonstrate significant predictive power beyond historical averages and enable direct measurement of the expected return term structure. This approach complements traditional measures of expected returns and offers valuable insights for practitioners, academics, and policymakers in Australia. [ABSTRACT FROM AUTHOR]

Fifty years ago, the world of trading was changed forever with a simple mathematical equation: the Black-Scholes(-Merton) option pricing model. The Black-Scholes equation allowed both investors and traders to shift from a linear payoff structure to a complex nonlinear payoff structure. Most academic articles celebrating this anniversary likely will focus on the mathematical equations that evolved from Black-Scholes—but ignore its effect on the trading community. Before Black-Scholes, options were exotic contracts sold in limited quantities. The Chicago Board Options Exchange was formed which started the modern trading of options almost simultaneously the Black-Scholes formula was acknowledged by the SEC. Option market making originally started as local traders (locals) trading in the pits. The ever-expanding option volumes evolved into the current technology-dominated market making industry. In this article we will discuss how Black-Scholes and the binomial tree calculations that followed it shifted traders from clever people who stood in the pits to technologists who create complex trading and investment algorithms. [ABSTRACT FROM AUTHOR]

The rigid assumptions underlying conventional parametric option-pricing models prevent them from accurately capturing the underlying nature of the market. Most of these models must be estimated numerically, because they cannot be solved analytically, which inevitably leads to estimation errors. Previous studies of option pricing, which have focused primarily on a single option variable, have not taken into account the effects of other options traded within the same time period. In order to address these issues, the authors use convolutional neural network (CNN) pattern recognition to fit the option panel data and provide a feature-rearrangement mechanism to account for the correlations between non-adjacent options. The suggested feature-rearranged CNN (FR-CNN) model is entirely data-driven without any preliminary assumptions. The authors' experiments with the Chinese SSE 50 ETF option show that the proposed FR-CNN outperformed the jump-diffusion model and CNN by 59.59% and 17.34%, respectively. [ABSTRACT FROM AUTHOR]

This article discusses the issue of option pricing in a continuous-time framework with transaction costs. Using the replication strategy that depends on the percentage of transaction costs and the revision interval, the authors develop a closed-form option pricing formula for European call options whose underlying asset price follows a sub-mixed fractional Brownian motion, which can better describe the characteristics of leptokurtosis, fat-tail, and long memory of the underlying asset. In- and out-of-sample empirical analyses on Shanghai Stock Exchange 50 ETF options illustrate the efficiency and accuracy of the sub-mixed fractional Brownian motion model and highlight the importance of including transaction costs in option pricing. [ABSTRACT FROM AUTHOR]

A preceding article has demonstrated that, using the Analytic Recovery Method, unconditional density functions can be recovered with highest accuracy from a series of call and put option prices. For each individual maturity, the closest approximation free of butterfly arbitrage is identified. From these unconditional densities, the conditional densities between maturities, as well as their moments, are recovered with equal precision. As functions of the moments, super moments are defined whose additive behavior creates maximum transparency. They are represented as functions of maturity. In the time-homogeneous case, linear dependence on maturity is obtained. The comparison with mean, volatility, skewness, and kurtosis extracted from option prices of major stock indices reveals certain discontinuities, and points to possible inconsistencies between market data for different maturities. [ABSTRACT FROM AUTHOR]

In the literature, one can find many claims about how long a learning video should be, but only a few valid reasons and even less empirical evidence. It is argued that a video should be as short as possible according to the learners' attention span. Short videos shall prevent the learner from becoming too passive. The Segmenting Principle postulates the division of longer passages into smaller, separate sections as an alternative to shortening. In this article, we present two studies. In the first study, we examined the video length and segmentations of the entire 895 German and a sample of 524 English channels from YouTube Education (83,558/154,370 videos). We clustered the videos by length into three groups and identified a series of videos by their titles. Short Videos with up to 14 or 22 min of playing time can be considered common practice. About 8 % of the videos with short lengths were part of a series of segmented videos. Videos of medium length were part of a series in 21 % and 14 % of the cases. We conclude that dividing comprehensive video-based learning resources into multiple segments is a common practice. In the second study, we investigate two design variants for structuring longer videos into segments: (i) video with an additional chapter overview, visible chapter boundaries, and navigation options for the segments, and (ii) sequence of segmented videos of suitable length. An online user study compared these two variants with non-segmented video players (N=22). Segmented videos resulted in higher learning gains than the non-segmented version of the same video. The participants perceived the segmented videos in conditions (i) and (ii) better structured. The question of video length is not crucial for learning outcomes as long as the video can be provided in meaningful segments within the video player. [ABSTRACT FROM AUTHOR]

The proportion of exchange‐listed firms with negative earnings has increased to over 40% in recent years. Previous research shows that the valuation of these loss firms is comparably difficult due to their uncertain future earnings path. Given these valuation issues, we argue that the stocks of loss firms should be particularly prone to mispricing such that simple firm value proxies might allow the prediction of subsequent stock returns. Supporting this hypothesis empirically, we find that book‐to‐market and revenue‐to‐price positively predict the cross‐section of loss firms' stock returns. In particular, these value effects are significantly stronger compared to gain firms. Our further analyses support a behavioural mechanism for the empirical observations as the return predictability is disproportionately strong around earnings announcements and as analysts are too optimistic for loss firms with low values of book‐to‐market and revenue‐to‐price. Our analyses on short selling and option trading indicate that sophisticated investors are aware of the documented return predictability, but limits to arbitrage prevent an immediate correction of mispricing. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel neural network (NN) for solving optimal control problems associated with American options in financial markets. American options provide holders with the flexibility to exercise the option before expiration, thereby affecting potential profitability. This paper focuses on determining the optimal exercise strategy and option price to maximize the payoff by solving a class of American option optimal control problems. We reformulate the optimal control problem into a linear complementarity problem(LCP). Subsequently, we employ the penalty approach and smoothing method to convert the LCP into a bi-nonlinear system with a set of partial differential equations (PDEs). By solving the reformulated PDE equations with the proposed method, we obtain numerical solutions that yield the optimal exercise strategy and option price. Numerical examples of American call and put options demonstrate the efficiency and usefulness of the proposed methods. [ABSTRACT FROM AUTHOR]

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample performance of option and stock characteristics in predicting future option returns. The nonlinear machine learning models generate statistically and economically sizable profits in the long-short portfolios of equity options even after accounting for transaction costs. Although option-based characteristics are the most important standalone predictors, stock-based measures offer substantial incremental predictive power when considered alongside option-based characteristics. Finally, we provide compelling evidence that option return predictability is driven by informational frictions and option mispricing. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

We investigate the implications of recovering real-world conditional expectation of return functions using options on the S&P 500 index and Treasury bond futures. First, we construct estimates of the probability of disasters, defined as higher than 6%, 5%, or 4% equity market declines over option expiration cycles. This measure of disaster probability forecasts realized disasters. Second, we employ options on the futures of the 10- and 30-year Treasury bonds to construct estimates for the expected return of bond futures. These measures display forecasting ability for subsequent futures returns beyond the level, slope, and curvature variables extracted from the yield curve. [ABSTRACT FROM AUTHOR]

Background: Neonatal hyperbilirubinemia is a prevalent condition that affects newborns, characterized by elevated serum bilirubin levels. The accumulation of bilirubin can cause neurotoxicity, resulting in various complications, and in severe cases, even mortality. The underlying pathophysiology of this condition primarily involves an inconsistency between the synthesis and excretion of bilirubin. Despite the short- and long-term side effects of phototherapy, it is considered a core treatment of neonatal hyperbilirubinemia. Exchange transfusion is considered a last resort in cases where phototherapy fails. Several studies have investigated the use of various agents either to replace phototherapy or to be added as adjuvants to reduce the time required for phototherapy and hence minimize the adverse effects. Some of these studies have demonstrated positive outcomes, indicating their effectiveness, while others have failed to yield any significant differences in treatment duration. Main text: The present review summarized the etiology of neonatal hyperbilirubinemia. It was demonstrated that neonatal hyperbilirubinemia has mainly two types: Conjugated and unconjugated hyperbilirubinemia. Treatment options for neonatal hyperbilirubinemia were discussed with phototherapy as the most prevalent and successful option followed by exchange transfusion. Side effects of phototherapy were also discussed together with the different approaches introduced to clinical practice to minimize these side effects. Conclusion: Phototherapy is the most successful treatment of neonatal hyperbilirubinemia, although its side effects are of great concern. Studies introducing adjuvants such as zinc sulfate and ursodeoxycholic acid have demonstrated promising results in reducing the duration of phototherapy and minimizing the side effects. However, these results are inconclusive, and further research is required to validate the safety and efficacy of these adjuvants. [ABSTRACT FROM AUTHOR]

Abstract\nHIGHLIGHTSA flexible financial instrument that is frequently used to assess the fair value of options is the Binomial Option Pricing Model (BOPM). This study examines the innovative use of the BOPM in booking tickets, highlighting how it might improve decision-making in the quickly changing ticketing sector. The option pricing model is used in the study to solve the problem of cancelation fees in ticket purchases. The aim is to provide a solution that enhances the company’s reputation while also being in line with client pleasure. This study probably considers the dynamic nature of ticket prices, variations in demand, and industry uncertainty by using the BOPM. The model’s adaptability is used to customize a response that considers consumer preferences and expectations regarding cancelation fees. The utilization of the BOPM in the ticketing industry highlights the tool’s capacity to enhance decision-making procedures by offering a more sophisticated and customer-focused method for setting prices and developing policies.Introducing a call option in the booking system to reduce the cancelation charges.Using BOPM, to fix a premium for the customer who books a ticket early.Introducing a call option will be eco-friendly for the customers. [ABSTRACT FROM AUTHOR]

The Stochastic Volatility Jump Diffusion (SVJD) model is a common tool used for option pricing. Existing literature has employed many techniques in order to estimate the parameters of this model, including both Bayesian and frequentist approaches. In 2010, Duan and Yeh [Jump and volatility risk premiums implied by VIX. J Econ Dyn Control. 2010;34(11):2232–2244] used a frequentist approach with maximum likelihood estimation that included the VIX, the Chicago Board Options Exchange (CBOE) Volatility Index, which carries real time S&P 500 option price information in addition to the S&P 500 data. In this paper, we combine the previous Bayesian approach in Cape et al. [Estimating Heston's and Bates' models parameters using Markov Chain Monte Carlo simulation. J Stat Comput Simul. 2015;85(11):2295–2314] with the inclusion of VIX by Duan and Yeh. We provide the derivations of the conditional posterior distributions of the SVJD model's parameters and a guide to a Markov Chain Monte Carlo (MCMC) algorithm used to estimate the parameters. We apply the algorithm to the historical S&P 500 and VIX data and provide the subsequent parameter estimates. We offer these tools for others performing similar research with our R code available upon request. [ABSTRACT FROM AUTHOR]

In this paper we explore the optimal issuance and liquidation of redeemable shares. Redeemable shares are those that the issuer can repurchase, or redeem, at a predetermined price, known as the call price, as soon as a given barrier event is triggered. We first determine the optimal call price for the issuer by stating and solving a stylized earning per share maximization problem from the point of view of a company. Once the call price is determined, we focus on the valuation of both perpetual and finite-maturity redeemable shares and we examine the problem of their optimal liquidation from the point of view of a shareholder holding them. Along with the few closed-form results that can be obtained in a lognormal continuous-time framework, we propose an intuitive and flexible method to retrieve the optimal liquidation policy in the form of a liquidation boundary, thanks to a parsimonious Markovianization of the evaluation problem in a binomial framework. Numerical tests using alternative market models and different dividend formulations confirm the robustness of our results. [ABSTRACT FROM AUTHOR]

AbstractAgainst the backdrop of increasingly severe global climate change issues, more and more countries and regions are adopting carbon emission trading, which is an effective market mechanism. As an important component of the carbon market, American carbon options provide more flexible trading tools to help companies avoid price risks. Therefore, the pricing research of American carbon options is of great significance for promoting the healthy development of the carbon market. Nonetheless, existing pricing methods are all based on probability theory, which need to satisfy the premise that the distribution function is close to the true frequency sufficiently. However, usually such premise is ill-suited in carbon market. To alleviate the predicament of the need for satisfying probability theory’s premise, this work investigates American carbon option pricing under the framework of uncertainty theory, which is another axiomatic mathematical system. For this purpose, uncertain differential equation is adopted to model the price of carbon future, and pricing formulae for American call and put carbon options are derived based on this model. Real data analyses illustrate the proposed method in details. Furthermore, it provides a detailed schema of applying the American carbon option for carbon asset management, which can optimize risk control for carbon consuming enterprises. [ABSTRACT FROM AUTHOR]

AI integration in digital assets and data protection is revolutionizing asset management. Proper AI applications can enhance security, but regulation must balance innovation and misuse, requiring a thorough understanding of technology. Data protection safeguards sensitive data from loss, alteration, or corruption, ensuring compliance with legal and regulatory obligations. It involves business information procedures and data protection mechanisms, particularly in the financial industry. Restricting access to digital asset data is crucial. This paper systematically reviews the recent role of AI in data protection for digital asset systems. Recent studies have reported enhanced algorithms for data protection in digital assets, focusing on transactions, markets, surveillance, and infrastructure assets. Robust algorithms in the digital asset market improve data protection and address issues with traditional crowdfunding. Token financing is a new strategy aimed at solving these issues. However, startups often lack knowledge about risk when choosing new token financing options. In the future, AI may be able to predict potential threats or vulnerabilities to digital assets through data protection trends and patterns. This would allow proactive data protection measures rather than just reactive ones. It will enhance digital asset systems' functionality and accuracy. [ABSTRACT FROM AUTHOR]

In this article, we test the theory of "The Coalition of the Opting" by counterfactually including a paragraph in the Yalta Charter, regulating the security cooperation between Georgia, Ukraine, Azerbaijan, and Moldova, welcoming them to develop both call and put options between the member states. The counterfactual Charter is applied to three examples of Russian armed aggression: the war on Georgia in 2008, the war on Ukraine in 2014, and the full-scale invasion on Ukraine in 2022. Although the application of the counterfactually designed options most likely would not have altered the outcome of these aggressions, our findings suggest that the very existence of a coalition of the opting nevertheless could have compelled Russia to think twice before attacking. Arguably, the additional time could have enabled both the coalition and the Western community to undertake counteractions that factually now were absent. [ABSTRACT FROM AUTHOR]

Agri-food cooperatives are fundamental for social cohesion in rural areas, where they are mostly concentrated. These organizations not only enhance the competitiveness of their members' farms but also generate wealth and employment, helping to retain population and combat rural depopulation. This study analyzes the factors influencing Murcian agricultural producers' decisions to join cooperatives. To do this, a Probit regression model is employed, based on survey data from 225 agricultural producers in the Region of Murcia, both cooperative members and non-members. The results show that household economic dependence on agriculture, preference for long-term commercial relationships, and specialization in olive oil or dairy products increase the likelihood of cooperative membership. Conversely, the availability of alternative marketing options and higher risk aversion decreases this likelihood. This analysis provides valuable information for cooperative managers, sector organizations, and public administrations by offering a profile of agricultural producers, which aids in designing strategies to attract new members and consolidate existing ones. [ABSTRACT FROM AUTHOR]