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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]

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]

This study is the first to conduct a comprehensive analysis of the price discovery and market liquidity aspects of China's crude oil futures market compared to WTI and Brent. With intraday-day data consolidated into 1-second intervals and three measures of price discovery, we find that China's crude oil futures market reports encouraging signs in terms of price discovery and efficiency, also showing great resilience during the COVID-19 pandemic. The market has obtained a dominant role in price discovery relative to WTI and Brent during its day trading hours, and has almost caught up with Brent in terms of market liquidity. [ABSTRACT FROM AUTHOR]

AbstractWith the development of financial markets, the trading of financial derivatives has become more flexible. To meet the demands of investors, over-the-counter (OTC) trading of options has also become increasingly frequent, raising concerns about accurately pricing vulnerable options. This study investigates the pricing problem of European vulnerable options under the sub-mixed fractional jump-diffusion (SMFJ) model. Firstly, the underlying asset price model and the counterparty company value model are established under the SMFJ model with a sub-mixed fractional Vasicek stochastic interest rate. Then, analytical solutions for the European vulnerable options are obtained using the quasi-martingale measure transformation and the risk-neutral pricing method. Additionally, the accuracy of the pricing formula is verified through the Monte Carlo simulation method. Finally, the rationality and validity of the established model are confirmed by numerical simulations, and the influence of key parameters on the pricing models is analyzed. [ABSTRACT FROM AUTHOR]

An important but rarely-addressed option pricing question is how to choose appropriate strikes for implied volatility inputs when pricing more exotic multi-asset derivatives. By means of Malliavin calculus, we construct an asymptotically optimal log-linear strike convention for exchange options under stochastic volatility models. This novel approach allows us to minimize the difference between the corresponding Margrabe computed price and the true option price. We show that this optimal convention does not depend on the specific stochastic volatility model chosen and, furthermore, that parameter estimation can be dramatically simplified by using market observables as inputs. Numerical examples are given that provide strong support for the new methodology. [ABSTRACT FROM AUTHOR]

This paper examines the risk incentives of traditional and non-traditional call options in the context of a levered firm where managers under-invest due to risk aversion. Our results contrast with those presented in the literature inasmuch as lookback calls do not always induce higher risk taking than regular calls, and managers always prefer a combination of regular calls and shares of stock in their compensation package as opposed to only company shares. We also show that Asian options outperform both plain-vanilla and other nonstandard options in inducing higher risk taking and, thereby, are a superior remedy for alleviating the agency costs of deviating from the optimal volatility level. Finally, we shed new insights that better clarify the incorrect arguments found in the literature regarding the delta of regular and lookback calls. [ABSTRACT FROM AUTHOR]

When prices of assets traded in a financial market are determined by nonlinear pricing rules, different parities between call and put options have been considered. We show that, under monotonicity, parities between call and put options and discount certificates characterize ambiguity‐sensitive (Choquet and/or Šipoš) pricing rules, that is, pricing rules that can be represented via discounted expectations with respect to non‐additive probability measures. We analyze how nonadditivity relates to arbitrage opportunities and we give necessary and sufficient conditions for Choquet and Šipoš pricing rules to be arbitrage free. Finally, we identify violations of the Call‐Put Parity with the presence of bid–ask spreads. [ABSTRACT FROM AUTHOR]

We propose a method to identify the informativeness of a future scheduled announcement at the daily level, exploiting the discontinuity it creates in the term structure of option volatility. We implement the strategy in a panel data model to estimate the relation between prior signals and the future announcement. This method allows us to separate substitutes from complements, it can isolate multiple signals within the same quarter, and it can condition on the timing and signal characteristics. We find that analyst forecasts substitute earnings announcement information and that recommendations do not provide extra information on top of forecasts. Moreover, our evidence suggests that insiders sell to avoid uncertainty when the announcement is far away but pull forward earnings information when they trade one month before. This paper was accepted by Eric So, accounting. Funding: This publication is part of the project "Eliciting the properties of private signals" (with project number VI.Veni.201E.029 of the research program VENI SGW, which is (partly) financed by the Dutch Research Council (NWO). Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.4970. [ABSTRACT FROM AUTHOR]

The Paris Agreement (PA) opens for parties to use Internationally Transferred Mitigation Outcomes (ITMOs) for implementing their Nationally Determined Contributions (NDCs). This paper analyzes spot, forward and options market trading of ITMOs up to the end of the first PA trading period (2030), given uncertainty about (1) the fulfillment of parties' NDC targets, and (2) the existence and functioning of the ITMO markets, as ITMO banking beyond 2030 is not allowed. Closed-form solutions are derived for options trading and its welfare impacts given uniform distributions of parties' uncertainties about fulfilling their individual commitments. Access to call options for late ITMO purchases leads to larger forward ITMO sales or less current mitigation, help parties stay in NDC compliance in 2030, brings early revenue to low-income parties, and is welfare enhancing for all parties. Access to put options for late ITMO sales is less important, and will not be used when put options are not subsidized and parties are risk neutral. The ITMO markets can be enabled by donor-provided climate finance. Effectively functioning ITMO markets can dramatically reduce parties' costs of achieving their NDCs, and could increase parties' ambitions, then also reducing global greenhouse gas emissions under the agreement. [ABSTRACT FROM AUTHOR]

We show how inter-asset dependence information derived from market prices of options can lead to improved model-free price bounds for multi-asset derivatives. Depending on the type of the traded option, we either extract correlation information or derive restrictions on the set of admissible copulas that capture the inter-asset dependences. To compute the resulting price bounds for some multi-asset options of interest, we apply a modified martingale optimal transport approach. Several examples based on simulated and real market data illustrate the improvement of the obtained price bounds and thus provide evidence for the relevance and tractability of our approach. [ABSTRACT FROM AUTHOR]

This article summarises standard film financing practices for the production and distribution of new content over the past 30 years using a mixed-methods approach. It takes the U.S. movie industry as a case in point to study how excess risk and uncertainty in the financing of new projects are processed and managed by private-sector, entities as well as what market-based solutions have been developed to prevent market failure. The research has wide-ranging implications for other creative industries and their use of intangible assets in creative content finance. In particular, it discusses the prominent role of intellectual property rights in financial transactions in the U.S. audiovisual sector and the ability of the industry to leverage funds through the strategic use of its IP assets. The research findings are based on a series of semi-structured interviews, commissioned expert memoranda, and a dedicated panel of selected industry experts. In addition, using novel data from Uniform Commercial Code filings and official IP registers, we conduct exploratory analysis and provide descriptive evidence on the use of credit and intangible collateral as well as the industry diffusion of such practices. In light of the digital transformation of the industry, this research also documents industry trends and recent changes in U.S. film financing and provides an alternative explanation for the surge in new titles. Finally, we outline generic policy options for improving the financing environment for new films in the United States. The results could help to inform the larger debate on IP-backed finance and the strategic use of intangible assets in content industries. [ABSTRACT FROM AUTHOR]

In this paper, we present a generalized adaptive Monte Carlo algorithm using the Diagonal and Off-Diagonal Splitting (DOS) iteration method to solve a system of linear algebraic equations (SLAE). The DOS method is a generalized iterative method with some known iterative methods such as Jacobi, Gauss-Seidel, and Successive Overrelaxation methods as its special cases. Monte Carlo algorithms usually use the Jacobi method to solve SLAE. In this paper, the DOS method is used instead of the Jacobi method which transforms the Monte Carlo algorithm into the generalized Monte Carlo algorithm. we establish theoretical results to justify the convergence of the algorithm. Finally, numerical experiments are discussed to illustrate the accuracy and efficiency of the theoretical results. Furthermore, the generalized algorithm is implemented to price options using the finite difference method. We compare the generalized algorithm with standard numerical and stochastic algorithms to show its efficiency. [ABSTRACT FROM AUTHOR]

The article introduces the Kansas City Fed's Measure of Policy Rate Uncertainty (KC PRU), which aims to measure uncertainty about future policy rates using financial options prices. The KC PRU is calculated daily and has shown correlations with changes in policymakers' communication strategies and macroeconomic events. The measure indicates a recent increase in policy rate uncertainty, attributed to factors such as inflation outlook, large increases in the funds rate, and regional banking stresses. Overall, the KC PRU provides a timely and consistent measure of policy rate uncertainty, reflecting historical movements and recent developments. [Extracted from the article]

The electricity sector plays a key role in achieving zero emissions targets. The required transition will lead to substantial changes in the supply, demand, and distribution of electricity, as well as in stakeholder roles. Future market designs may change substantially to accommodate these changes, address challenges, and take advantage of new opportunities. This paper reviews the characteristics of future carbon-neutral electricity systems and electricity market design options. To provide a guiding framework for the literature review, we transfer the complexity of electricity systems into a three-layer structure: Firstly, we analyze papers that rely on techno-economic modeling of the physical electricity system. As a case study, we analyze various studies focusing on a decarbonized European electricity system in 2050. Secondly, we review papers that investigate the economic behavior and effects of self-interest-seeking stakeholders such as producers, network operators, and consumers. Finally, we review papers focusing on policy and market design questions that guide policymakers in achieving a target physical asset combination while considering the behavior of stakeholders. We highlight common trends and disagreements in the literature, review the main drivers of future markets, and finally provide a mapping between those drivers, challenges, and opportunities. The review concludes that the most promising next step toward a fully comprehensive assessment approach is to combine existing approaches across topical and disciplinary boundaries. [ABSTRACT FROM AUTHOR]

Purpose: The primary aim of this paper is to identify the specificity and level of implementation of circular economy (CE) practices in small and medium-sized enterprises (SMEs) in Poland. Design/methodology/approach: To address the research questions, a comparative analysis was conducted using statistical data from the Flash Eurobarometer surveys titled 'SMEs, Green Markets, and Resource Efficiency'. These surveys capture the opinions of SMEs on practices for enhancing resource efficiency and transitioning to CE. Findings: The research results indicate that the level of implementation of CE practices by SMEs in Poland is low. This conclusion is supported by the number of CE practices reported by SMEs and the level of CE investment funds incurred in previous years. The most frequently implemented CE practices aim at achieving energy savings, material savings, and waste reduction. The major barrier for SMEs in Poland implementing CE practices is the complexity of administrative and legal procedures. It was also revealed that increased access to external funding sources, as well as advice on financing options and financial planning for CE transition investments, could significantly enhance the interest of SMEs in Poland in adopting CE practices. Originality/value: This paper contributes to the existing literature by presenting the results of an analysis on the implementation level and economic effects of CE practices, barriers to adoption, and resources intended to support CE implementation in SMEs in Poland. The findings are directed towards policymakers, authorities, managers, and practitioners involved in the implementation of CE practices. [ABSTRACT FROM AUTHOR]

This paper investigates algorithms for identifying the optimal policy for pricing American Options. The American Option pricing is reformulated as a Sequential Decision-Making problem with two binary actions (Exercise or Continue), transforming it into an optimal stopping time problem. Both the least square Monte Carlo simulation method (LSM) and Reinforcement Learning (RL)-based methods were utilized to find the optimal policy and, hence, the fair value of the American Put Option. Both Classical Geometric Brownian Motion (GBM) and calibrated Stochastic Volatility models served as the underlying uncertain assets. The novelty of this work lies in two aspects: (1) Applying LSM- and RL-based methods to determine option prices, with a specific focus on analyzing the dynamics of "Decisions" made by each method and comparing final decisions chosen by the LSM and RL methods. (2) Assess how the RL method updates "Decisions" at each batch, revealing the evolution of the decisions during the learning process to achieve optimal policy. [ABSTRACT FROM AUTHOR]

Grain quality is largely driven by grain infrastructure (technology) and handling practices (application of knowledge on handling). The use of inappropriate infrastructure and inappropriate handling protocols poses food safety and health-related risks. This review provides evidence for the link between drying and storage operations in the context of preserving grain quality. The purpose of this study was to evaluate the close grain quality relationship between drying and storage, with an appraisal of operations in Africa. This study further benchmarked successful and scalable models in Africa to infer guidance for promotion of optimal and effective drying and storage initiatives. While open-sun drying is undoubtedly the most adopted approach to grain drying for the rural-poor farmers, this study revealed greater success in grain storage, especially with the breakthrough at the introduction and adoption of small-scale hermetic storage technologies. Upon assessment of the cob, WFP Zero Food Loss Initiative, and AflaSight models implemented in Rwanda and Uganda, this study suggests: (i) the adoption of system thinking; (ii) the use of sustainable approaches such as gender inclusion, sustainable financing options, and use of existing infrastructures along-side novel interventions; and (iii) enabling policies and political will as strategic pathways for successful implementation of improved grain-quality interventions during drying and storage. In the short term, grain handlers must develop appropriate grain management protocols during open-sun drying to limit the impact of drying-related grain quality deterioration. Consortia-based implementation of the three models evaluated in this review could improve grain quality, food security and safety, and market linkages with premium grain markets, fostering economic growth and transformation. [ABSTRACT FROM AUTHOR]

In this paper we model the dynamics of the Chinese crude oil futures returns by using a skew‐geometric Brownian motion correlated with the market volatility, which is taken as a square‐root stochastic process. We use the OVX index data as proxy for market volatility. We validate the proposed model in terms of accuracy of its calibrations through an in‐sample simulation. Instead, out‐of‐sample simulations are used to show that a correlated skew‐geometric Brownian motion is more appropriate for modelling the Chinese returns compared to a single skew‐geometric Brownian motion in terms of forecasts. Furthermore, we price an American call option on the Chinese futures by using a recursively scheme based on a closed‐form formula, and an alternative Monte Carlo approach, for the related European call option. We show that our call price estimates are very close to market values and our model generally outperforms many benchmarks in literature, such as the Barone‐Adesi and Whaley formula and its generalizations. [ABSTRACT FROM AUTHOR]

This work considers the calibration of exponential Lévy models to market options data and the subsequent pricing of exotic options. We focus on a natural and promising extension of the bilateral Gamma process that includes a diffusive component. This extension is shown to address a major deficiency in terms of fit quality and shape of the calibrated model volatility surface. The new model, which we call a bilateral Gamma motion, performs well in market calibrations, as demonstrated in several equity market examples, and significantly outperforms the pure-jump bilateral Gamma process. The new model's performance is shown to rival some of the more common alternatives, such as CGMY and jump diffusion models, with consistently strong calibration results. Calibrated model parameters and corresponding exotic option prices are presented, including Asian options, occupation time derivatives (step and fader options), and barrier options. The experiments highlight the sensitivity of various contracts to model assumptions and the importance of model selection and calibration in pricing exotic options. [ABSTRACT FROM AUTHOR]

We consider a dynamic duopoly game where firms choose both the timing and size of their investments. The existing real options literature predominantly consists of contributions where firms have a single option to invest. This paper relaxes this assumption by giving Firm A multiple options to undertake further investments with the purpose to expand whereas Firm B only holds the option to enter the market. In this asymmetric setting we get the surprising result that, in equilibrium, Firm B invests first. If Firm A invests first, Firm A and Firm B keep on being involved in preemption games for subsequent investments until Firm B enters the market, which leads to inefficiently early investments of Firm A. When Firm B invests first, then only one preemption game is played, which leads to Firm A being free to choose its unrestricted optimal investment moments. [ABSTRACT FROM AUTHOR]

In practice, capital‐constrained third‐party logistics (3PL) firms usually obtain bank financing (BF) to invest in logistics technology to enhance the consumer shopping experience. The emergence of financial innovation has prompted the adoption of alternative financing modes by 3PLs, including e‐commerce platform financing (EPF) and fourth‐party logistics financing (4PF). To clarify the differences among the three financing modes at the operational management level, we develop a Stackelberg game model to capture the strategic interactions between the 3PL and creditors. We examine a platform‐based supply chain where a manufacturer sells products through an e‐commerce platform to consumers, with the logistics services for the supply chain provided by the 3PL and fourth‐party logistics (4PL) firms. The analysis results reveal that the equilibrium interest rates under EPF and 4PF share a similar pattern regarding the logistics service cost coefficient, the commission rate, and the 4PL service fee. The difference is that under EPF (4PF), the logistics service level increases in the commission rate (4PL service fee) but decreases under the other financing strategy. Furthermore, the 3PL is inclined to invest in logistics technology only when the fixed investment cost is low, and these financing modes are complementary rather than alternative. The 3PL and manufacturer can benefit from 4PF (BF) when the logistics service cost coefficient is large and the market size is small (large); otherwise, EPF can enhance their profits. We also find that when the opportunity costs of capital are homogeneous, both the 4PL and the platform are consistently willing to act as financiers themselves. However, when considering heterogeneity in the opportunity costs of capital, they may be reluctant to provide financing. Each financing mode may achieve Pareto improvements under specific conditions. Finally, we also analyze the impact of technology R&D risks and endogenous 4PL service fee, yielding some valuable insights. [ABSTRACT FROM AUTHOR]

This paper proposes a new index of forward looking absolute deviation extracted from option prices. The new index, named absolute deviation index (ADIX), is model‐free and easy to compute using at‐the‐money call and put option prices. It is shown that the spread between volatility index (VIX) and ADIX captures departures from normality in the risk‐neutral distribution and an empirical analysis using S&P 500 options data for the time period 1996–2021 reveals that the spread carries significant forecasting ability with respect to future equity returns at short to medium horizons. Portfolio strategies that use the spread as a predictor of S&P 500 returns outperform buy‐and‐hold strategies in an out‐of‐sample mean‐variance asset allocation exercise. [ABSTRACT FROM AUTHOR]

In this study, we derived pricing formulas for various forward-start style exotic options based on an uncertain stock models with periodic dividends. Specifically, we present valuations for forward-start, Cliquet/Ratchet, and spread options. In addition, we conducted numerical simulations of these formulas and compared them to pricing formulas for the same options based on a dividendpaying stock model driven by standard Brownian motion. [ABSTRACT FROM AUTHOR]

The uncertainty surrounding the possibility of generating sufficient funds from international donors and other public financial resources to support climate action calls for the identification of alternative sources of climate finance options. Ensuring a reliable stream of funds to drive climate action requires the identification of diverse funding channels that can supplement existing ones. Also, in light of the proactive approach required to combat climate change, overreliance on a singular funding source could hinder the achievement of climate‐related goals. This is because the existing sources of funding from the central government, local governments, and international agencies remain insufficient to meet the perceived cost of climate change in Ghana. Through a qualitative in‐depth interview, empirical data was drawn from religious institutions to identify their contribution toward climate finance. The study revealed that religious institutions play a significant role in climate finance due to their advocacy capacity, financial resources, and extensive networks. Funding from religious institutions has also been channeled into stock adaptation projects and public education, and this is evident in the areas of agriculture, waste, energy, water, and the support rendered to SMEs in general. Besides, their engagement in climate finance is also motivated by ethical, theological, and moral considerations. The study, therefore, argues that funding from religious institutions toward climate change adaptation and mitigation initiatives should be seen as a complementary source of funding to climate finance. [ABSTRACT FROM AUTHOR]

In this article we propose a semi-analytic approach to pricing American options in time-dependent jump-diffusion models with exponential jumps. The idea of the method consists of further generalization of our method developed for pricing barrier (Itkin, Lipton, and Muravey 2021) and American (Carr and Itkin 2021; Itkin and Muravey 2024) options in various time-dependent one factor and even stochastic volatility models. The proposed approach: i) allows arbitrary dependencies of the model parameters on time; ii) reduces solution of the pricing problem for American options to a simpler problem of solving a system of an algebraic nonlinear equation for the exercise boundary and a linear Fredholm-Volterra integral equation for the option Gamma; once done, the American option price is presented in closed form; iii) the options' Greeks solve a similar Fredholm-Volterra linear equation obtained by just differentiating the pricing equation by the required parameter. Also, solving integral equations instead of PIDE usually brings better accuracy under the same speed, or better speed under the same accuracy. [ABSTRACT FROM AUTHOR]

The distribution of financial data usually shows strong skewness and kurtosis. In this article, we propose a probability distribution with parameters explicitly representing skewness and kurtosis. This distribution extends logistic distribution to a wide range of distributions bounded by Gaussian and Laplace. The properties of the new distribution are studied, and parameter estimation methods are proposed. As an application, the new distribution can be used for pricing volatility skew and smile in option quotes and constructing local volatility models for the evaluation of path-dependent financial derivatives. [ABSTRACT FROM AUTHOR]

This article presents a novel tree approach to pricing derivatives linked to new Risk-Free Rate benchmarks. The methodology is versatile and can be applied to both backward–and forward-looking caplets. It draws upon the analogy with the pricing of Asian options, allowing for effective pricing in the context of these new benchmark rates. This article demonstrates the efficacy of this approach by applying it to model the overnight rate using numerical examples. The focus is on established interest rate tree models, which are widely utilized in the financial industry for valuing bonds with options linked to legacy benchmarks like LIBOR. The numerical examples presented in this article validate the reliability and accuracy of the proposed tree-based approach, showcasing its superiority over traditional Monte Carlo simulation methods. [ABSTRACT FROM AUTHOR]

We propose a novel analytical method to evaluate VIX options under the general class of affine and non-affine stochastic volatility models, which extends the current literature in particular to the realm of non-affine stochastic volatility models. The approach is based on a closed-form approximation of the VIX index through the Ito-Taylor expansion and the subsequent continuous-time Markov chain (CTMC) approximation to evaluate VIX options. The formula is in explicit closed-form and does not involve numerical (Fourier) inversions, in contrast to the existing literature. Numerical experiments with several stochastic volatility models demonstrate that it is accurate and efficient by comparing with benchmarks in the literature and Monte Carlo simulations. Empirical experiments demonstrate that in general non-affine stochastic volatility models provide better fit to the VIX options data. [ABSTRACT FROM AUTHOR]

Using data from January 2004 through November 2019, we examine the relative performance of four options-based investment strategies versus a buy-and-hold strategy in the underlying stock. Specifically, using 10 stocks widely held in 401(k) plans, we examine monthly returns from strategies that include a long stock position as one component. Ignoring early exercises for simplicity, we find that the covered combination and covered call strategies generally outperform the long stock strategy, which in turn generally outperform the collar and protective put strategies, regardless of the performance measure considered. The outperformance of the covered combination was smallest in the 2015–2019 subperiod. These results also hold for equally-weighted and value-weighted portfolios constructed from the 10 individual stocks. Our findings suggest that options-based strategies can improve the risk-return characteristics of a long equity portfolio. [ABSTRACT FROM AUTHOR]

We analyze the predictability of daily data on the CBOE  V I X  and  S K E W  indices, used to capture the average level of risk-neutral risk and downside risk, respectively, as implied by S&P 500 index options. In particular, we use forecast models from the Heterogeneous Autoregressive ( H A R ) class to test whether and how lagged values of the  V I X  and of the  S K E W  may increase the forecasting power of  H A R  for the  S K E W  and the  V I X . We find that a simple  H A R  is very hard to beat in out-of-sample experiments aimed at forecasting the  V I X . In the case of the  S K E W , the benchmarks (the random walk and an  A R (1) ) are clearly outperformed by  H A R  models at all the forecast horizons considered and there is evidence that special definitions of the  S K E W  index based on put options data only yield superior forecasts at all horizons. [ABSTRACT FROM AUTHOR]

Based on the typical positions of S&P 500 option market makers, we derive a funding illiquidity measure from quoted prices of S&P 500 derivatives. Our measure significantly affects the returns of leveraged managed portfolios; hedge funds with negative exposure to changes in funding illiquidity earn high returns in normal times and low returns in crisis periods when funding liquidity deteriorates. The results are not driven by existing measures of funding illiquidity, market illiquidity, and proxies for tail risk. Our funding illiquidity measure also affects leveraged closed-end mutual funds and, to an extent, asset classes where leveraged investors are marginal investors. [ABSTRACT FROM AUTHOR]

This study examines how options trading plays a unique role in curbing firms' earnings management. We find that options trading volume deters managers' earnings manipulations, and the effect can be explained by unique characteristics of the options markets. Our results remain unchanged when using both an instrumental variable approach and difference-in-differences analyses to mitigate endogeneity concerns, and after controlling for investors' short-selling activities. This study adds to the literature by documenting a real impact of options trading on financial reporting. Our results suggest that the options markets promote price efficiency not only by incorporating private information from informed traders, but also by incentivizing managers to disseminate less manipulated information. [ABSTRACT FROM AUTHOR]

AbstractMost of the existing methods for pricing Asian options are less efficient in the limit of small maturities and small volatilities. In this article, we use the large deviations theory for the analysis of short-maturity Asian options. We present a constant volatility model for the underlying market that incorporates a jump term in addition to the drift and diffusion terms. We estimate the asymptotics for the out-of-the-money, in-the-money, and at-the-money short-maturity Asian call and put options. Under appropriate assumptions, we show that the asymptotics for out-of-the-money Asian call and put options are governed by rare events. For the at-the-money Asian options, the result is more involved and in that case, we find the upper and lower bounds of the asymptotics of the Asian option price. [ABSTRACT FROM AUTHOR]

Modeling implied volatility (IV) is important for option pricing, hedging, and risk management. Previous studies of deterministic implied volatility functions (DIVFs) propose two parameters, moneyness and time to maturity, to estimate implied volatility. Recent DIVF models have included factors such as a moving average ratio and relative bid-ask spread but fail to enhance modeling accuracy. The current study offers a generalized DIVF model by including a momentum indicator for the underlying asset using a relative strength index (RSI) covering multiple time resolutions as a factor, as momentum is often used by investors and speculators in their trading decisions, and in contrast to volatility, RSI can distinguish between bull and bear markets. To the best of our knowledge, prior studies have not included RSI as a predictive factor in modeling IV. Instead of using a simple linear regression as in previous studies, we use a machine learning regression algorithm, namely random forest, to model a nonlinear IV. Previous studies apply DVIF modeling to options on traditional financial assets, such as stock and foreign exchange markets. Here, we study options on the largest cryptocurrency, Bitcoin, which poses greater modeling challenges due to its extreme volatility and the fact that it is not as well studied as traditional financial assets. Recent Bitcoin option chain data were collected from a leading cryptocurrency option exchange over a four-month period for model development and validation. Our dataset includes short-maturity options with expiry in less than six days, as well as a full range of moneyness, both of which are often excluded in existing studies as prices for options with these characteristics are often highly volatile and pose challenges to model building. Our in-sample and out-sample results indicate that including our proposed momentum indicator significantly enhances the model's accuracy in pricing options. The nonlinear machine learning random forest algorithm also performed better than a simple linear regression. Compared to prevailing option pricing models that employ stochastic variables, our DIVF model does not include stochastic factors but exhibits reasonably good performance. It is also easy to compute due to the availability of real-time RSIs. Our findings indicate our enhanced DIVF model offers significant improvements and may be an excellent alternative to existing option pricing models that are primarily stochastic in nature. [ABSTRACT FROM AUTHOR]

This study examines the effect of the 2012 introduction of the "Green Credit Guidelines" on the Environmental, Social, and Governance (ESG) performance of polluting enterprises listed on the Chinese A-share market from 2009 to 2019. Using a quasi-natural experiment framework, we employ a difference-in-differences model to evaluate the effectiveness of this green credit policy. Our analysis suggests that the green credit policy significantly hinders the ESG performance of polluting enterprises due to the "crowding out effect." Further investigation reveals that a notable decrease in green innovation, especially in its quality, impedes ESG improvements for these enterprises. Notably, the negative effects are more pronounced for non-state-owned enterprises. Our study provides valuable insights into the dual effects of the green credit policy, highlighting its potential to restrict financing options for polluting enterprises but crowd out resources allocated for environmental projects. To address these challenges, we propose practical strategies aimed at transforming the dual effects into dual benefits, optimizing both economic and environmental effects, and enhancing the overall effectiveness of the policy. [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 pricing problem of European option is investigated under the generalized fractional jump-diffusion model. First of all, the generalized fractional jump-diffusion model is proposed, with the assumption that the underlying asset price follows this model, and the explicit solution is derived using the Itô formula. Then, the partial differential equation (PDE) of the European option price is obtained by using the delta-hedging strategy, and the analytical solutions of the European call and put option prices are obtained through the risk-neutral pricing principle. Moreover, the accuracy of closed-form formula for European option pricing is verified by the Monte Carlo simulation. Furthermore, the properties of the pricing formulas are discussed and the impact of main parameters on the option pricing model are analyzed via calculations of Greeks. Finally, the rationality and validity of the established option pricing model are verified by numerical analysis. [ABSTRACT FROM AUTHOR]

Since the early 21st century, within fuzzy mathematics, there has been a stream of research in the field of option pricing that introduces vagueness in the parameters governing the movement of the underlying asset price through fuzzy numbers (FNs). This approach is commonly known as fuzzy random option pricing (FROP). In discrete time, most contributions use the binomial groundwork with up-and-down moves proposed by Cox, Ross, and Rubinstein (CRR), which introduces epistemic uncertainty associated with volatility through FNs. Thus, the present work falls within this stream of literature and contributes to the literature in three ways. First, analytical developments allow for the introduction of uncertainty with intuitionistic fuzzy numbers (IFNs), which are a generalization of FNs. Therefore, we can introduce bipolar uncertainty in parameter modelling. Second, a methodology is proposed that allows for adjusting the volatility with which the option is valued through an IFN. This approach is based on the existing developments in the literature on adjusting statistical parameters with possibility distributions via historical data. Third, we introduce into the debate on fuzzy random binomial option pricing the analytical framework that should be used in modelling upwards and downwards moves. In this sense, binomial modelling is usually employed to value path-dependent options that cannot be directly evaluated with the Black–Scholes–Merton (BSM) model. Thus, one way to assess the suitability of binomial moves for valuing a particular option is to approximate the results of the BSM in a European option with the same characteristics as the option of interest. In this study, we compared the moves proposed by Renddleman and Bartter (RB) with CRR. We have observed that, depending on the moneyness degree of the option and, without a doubt, on options traded at the money, RB modelling offers greater convergence to BSM prices than does CRR modelling. [ABSTRACT FROM AUTHOR]

Since the early 1970s, the study of Black–Scholes (BS) partial differential equations (PDEs) under the Efficient Market Hypothesis (EMH) has been a subject of active research in financial engineering. It has now become obvious, even to casual observers, that the classical BS models derived under the EMH framework fail to account for a number of realistic price evolutions in real-time market data. An alternative approach to the EMH framework is the Fractal Market Hypothesis (FMH), which proposes better and clearer explanations of market behaviours during unfavourable market conditions. The FMH involves non-local derivatives and integral operators, as well as fractional stochastic processes, which provide better tools for explaining the dynamics of evolving market anomalies, something that classical BS models may fail to explain. In this work, using the FMH, we derive a time-fractional Black–Scholes partial differential equation (tfBS-PDE) and then transform it into a heat equation, which allows for ease of implementing a high-order numerical scheme for solving it. Furthermore, the stability and convergence properties of the numerical scheme are discussed, and overall techniques are applied to pricing European put option problems. [ABSTRACT FROM AUTHOR]

This paper studies equity basket options – i.e. multi-dimensional derivatives whose payoffs depend on the value of a weighted sum of the underlying stocks – and develops a new and innovative approach to ensure consistency between options on individual stocks and the index comprising them. Specifically, we show how to resolve a well-known problem that when individual constituent distributions of an equity index are inferred from the single-stock option markets and combined in a multi-dimensional local/stochastic volatility model, the resulting basket option prices will not generate a skew matching that of the options on the equity index corresponding to the basket. [ABSTRACT FROM AUTHOR]

We focus on analyzing the risk preferences of suppliers regarding two types of financing: partial credit guarantee (PCG) and trade credit financing (TCF). Using the conditional value-at-risk (CVaR) criterion, we study each supply chain member's equilibrium solution and optimal financing strategy under the assumption of demand distributions with an increasing failure rate. Initially, we present an analytical solution of the risk-averse supplier's wholesale price, and prove that the optimal wholesale price decreases in the risk-averse level and initial capital, and increases in the credit guarantee ratio. Furthermore, we derive optimal financing strategies for both the supplier and the retailer under various circumstances. However, it is important to note that the results reveal a potential trade-off associated with PCG. While risk-averse suppliers may be more inclined to provide PCG compared to risk-neutral suppliers, this financing option can negatively impact the supplier's utility and the expected profits of the retailers. Finally, we illustrate how the optimal financing strategies shift in response to changes in the risk-averse level and credit guarantee ratio, and present the win-win situations for the supplier and the retailer. [ABSTRACT FROM AUTHOR]

In this paper, option pricing through introducing a novel hybrid method for solving time-fractional Black–Scholes equation is considered. The presented method is based on time and space discretization. Time discretization is according to a second order finite difference formula. Space discretization is done by a spectral method based on fractional order shifted Hahn functions (FOSHFs) and an operational process by defining fractional order Hahn operational matrices. Convergence and error analysis for FOSHFs approximation and also for the proposed method are discussed. For validating obtained theoretical results and demonstrating the accuracy, convergency and efficiency of the method, two numerical examples with the known exact solutions are considerd and compared to other methods. Furthermore, the presented method is used to price three different European options governed by a time-fractional Black–Scholes model: European call option, European put option and European double barrier knock-out call option. [ABSTRACT FROM AUTHOR]

In accordance with the theme of the Yuri Ijiri lectures, I focus on something foundational to accounting: the communication of uncertainty through accounting numbers. I do so in the context of providing information to investors about "the amount, timing, and uncertainty of future cash flows", an objective of accounting standard setters (with emphasis added). I outline accounting principles that convey uncertainty and discuss the implications for a financial statement analysis that extracts information about uncertainty. I also show how accounting-based valuation is modified to incorporate that information. Asset pricing in finance deals with risk and uses accounting numbers to do so. I explain how that endeavor might be improved by recognizing the accounting for uncertainty. That puts accounting and finance on the same platform. Finally, I address normative issues of accounting policy for conveying information about the uncertainty of future cash flows. [ABSTRACT FROM AUTHOR]

When trading in the call and put contracts of option chains, the portfolios of strikes must be selected. The trader must also decide whether to take long or short positions at the selected strikes. Dynamic strategies for making these decisions are discussed in this paper. On any day, the strategies estimate the drift and volatility parameters of the future probability distribution of the price of the underlying asset. From this distribution, the trader can further estimate the future expected profit and expected loss that may be experienced for any portfolio of strikes of the call and put contracts. Expected profit and expected loss are the reward and risk metrics of such portfolios. An optimal portfolio can then be selected by making the reward as high as possible under the risk tolerance set by the trader. Extensive back-testing applications to historical data of SPY option chains illustrate the effectiveness of these strategies, particularly when dealing with short-term expiry options and when acting as a seller of put and call options. [ABSTRACT FROM AUTHOR]

This study contributes to the discussion about a fair and balanced pension system with a collectively funded pension scheme or social security and a defined contribution pillar. With an invigorated risk approach using financial option positions, it considers the variance of socioeconomic interests of different society-earning cohorts. By that, it enables the assumption of un-uniformity in interests about the fair and sustainable pension design. Specifically, we claim that the alternative cost of hedging the ideal position to the counterparty position studies the implied risks and returns that participants are willing to absorb and hence may lead to a fair compromise when there are different interests. The novelty of the introduced method is mainly based on the variety of participants' risks and not on the utility function. Accordingly, we spare the discussion about the right shape of the utility function and the proper calibrations. [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]