Distribution Of Returns Research Articles

Food-fuel nexus beyond mean-variance: New evidence from a quantile approach

This paper investigates the dynamic relationship between crude oil, ethanol, and corn markets across various quantiles of return distributions, as well as at higher statistical moments. Using a quantile vector autoregression model and data from 2007 to 2022, we find that the cross-market linkages are quantile dependent, with the strongest connections observed in the tails of the distribution. A shock to the oil market significantly impacts ethanol and corn returns under extreme bearish and bullish conditions. Positive shocks to the corn market reduce ethanol returns when the ethanol market is highly bullish, but this effect becomes positive in the left tail of the distribution. We also identify significant co-movement in higher statistical moments between these markets. Extreme excess kurtosis in the food-fuel nexus is more likely to occur with high financial market uncertainty, a bullish stock market, contracting industrial production, and a strong US dollar. In addition to these variables, credit spreads, futures market liquidity, futures term structure, and hedging pressure also influence kurtosis in individual markets within the nexus.

Choices under uncertainty and the investment horizon

AbstractMean–Variance (M–V) is the most popular investment rule employed by both practitioners and researches. For a short-planned investment horizon, this rule generally conforms with expected utility paradigm. However, for a relatively long horizon, generally more than one year, the distributions of returns become positively skewed, hence the M–V rule loses ground. As the horizon increases, by the M–V rule one needs (mistakenly) to increase the weight of bonds in the stock–bond portfolio, e.g., almost 100% in bonds for a 30-year horizon. Expected utility maximization recommending almost 100% in stocks for long horizons. This, gap is of crucial importance, because life expectancy is increasing, implying longer investment horizons. For long horizons the mean–coefficient-of-variance rule conforms with expected utility, as the distributions are close to log-normal. For intermediate horizons one should employ stochastic dominance or direct expected utility maximization.

Path Tracing-Inspired Modeling of Non-Line-of-Sight SPAD Data.

Non-Line of Sight (NLOS) imaging has gained attention for its ability to detect and reconstruct objects beyond the direct line of sight, using scattered light, with applications in surveillance and autonomous navigation. This paper presents a versatile framework for modeling the temporal distribution of photon detections in direct Time of Flight (dToF) Lidar NLOS systems. Our approach accurately accounts for key factors such as material reflectivity, object distance, and occlusion by utilizing a proof-of-principle simulation realized with the Unreal Engine. By generating likelihood distributions for photon detections over time, we propose a mechanism for the simulation of NLOS imaging data, facilitating the optimization of NLOS systems and the development of novel reconstruction algorithms. The framework allows for the analysis of individual components of photon return distributions, yielding results consistent with prior experimental data and providing insights into the effects of extended surfaces and multi-path scattering. We introduce an optimized secondary scattering approach that captures critical multi-path information with reduced computational cost. This work provides a robust tool for the design and improvement of dToF SPAD Lidar-based NLOS imaging systems.

Deformation of volatility curves of the Russian stock market on the example of margined options on futures contracts on the RTS index

We reassess the Russian stock market behavior in terms of the relationship between risk parameters and return on assets in connection with global structural changes in 2022. The subject of this study is volatility curves calculated from quotes of exchange options on RTS index futures. The purpose of the study is to analyze the relationship between volatility curves and the distribution of returns in 2015–2022, to analyze changes in the structure of this relationship in 2022, to assess the applicability of the GARCH model and to develop new investment and hedge strategies on the Russian stock market. The issue of interpreting the dynamics of volatility curves is disputable however, it was theoretically developed in the theory of valuation of derivative financial instruments. As a result of the study, unrelated structures of the distribution of returns and volatility curves for the RTS composite index were found throughout the entire period concerned, regardless of the presence of external shocks. It is concluded that in the Russian stock market, investors prefer protective options rather than speculative ones, despite the negative asymmetry of the distribution of RTS index returns. And despite the greater weight of positive returns in their distribution in each period under consideration, due to the technical features of the functioning of return indicators, the negative asymmetry of the return distribution does not necessarily correlate with growing volatility curves.

Asymmetric Laplace scale mixtures for the distribution of cryptocurrency returns

AbstractRecent studies about cryptocurrency returns show that their distribution can be highly-peaked, skewed, and heavy-tailed, with a large excess kurtosis. To accommodate all these peculiarities, we propose the asymmetric Laplace scale mixture (ALSM) family of distributions. Each member of the family is obtained by dividing the scale parameter of the conditional asymmetric Laplace (AL) distribution by a convenient mixing random variable taking values on all or part of the positive real line and whose distribution depends on a parameter vector $$\varvec{\theta }$$ θ providing greater flexibility to the resulting ALSM. Advantageously concerning the AL distribution, our family members allow for a wider range of values for skewness and kurtosis. For illustrative purposes, we consider different mixing distributions; they give rise to ALSMs having a closed-form probability density function where the AL distribution is obtained as a special case under a convenient choice of $$\varvec{\theta }$$ θ . We examine some properties of our ALSMs such as hierarchical and stochastic representations and moments of practical interest. We describe an EM algorithm to obtain maximum likelihood estimates of the parameters for all the considered ALSMs. We fit these models to the returns of two cryptocurrencies, considering several classical distributions for comparison. The analysis shows how our models represent a valid alternative to the considered competitors in terms of AIC, BIC, and likelihood-ratio tests.

Robust dynamic contracts with multiple agents

We develop a continuous-time dynamic multi-agent contracting model in which the principal is unsure about the distributions of the project's terminal payoffs and worries about model misspecification. With model uncertainty, workers' wages depend on the outputs of other unrelated projects and the optimal contracts exhibit overdetermination. We demonstrate an inverse U-shaped relationship between the extent of overdetermination and group size. Moreover, model uncertainty induces wage compression, especially in small firms as the empirical evidence demonstrates. Finally, expanding the group size increases the average project value by mitigating the negative impacts of ambiguity.

Moral wiggle room and group favoritism among political partisans.

How does the availability of excuses for self-interested behavior impact group favoritism? We report the results of a preregistered experiment, conducted on the eve of the 2022 midterm elections, in which American political partisans made payoff distribution choices for themselves and a partner who was known to be a co-partisan or opposing partisan. Under full information, participants exhibit significant group favoritism. However, when the payoff consequences for one's partner are initially hidden, participants exploit this excuse to act selfishly regardless of who their partner is and ignorance rates are identical for in-group and out-group members. As a result, moral wiggle room has a significantly larger impact on selfish behavior for those interacting with co-partisans than opposing partisans, leading to a reduction in group favoritism.

Impact of Sharia‐Based Screening on Equity Risk: International Evidence

ABSTRACTThis paper investigates the impact of mainstream Sharia‐based screening methodologies on equity risk. The study is based on the daily returns of 23 pairs of stock indices belonging to different index providers, namely Dow Jones, FTSE, and S&P. For risk measurement, the study uses five Value at Risk (VaR) methods. Moreover, the conditional VaR (CVaR) is used to analyze the tail behavior of return distributions. The results show that Sharia‐based screening is a source of significant differences in risk. There is strong evidence that Islamic stock indices (ISIs) tend to be less risky than their conventional peers. Also, the screening process allows for keeping extreme negative returns at a lower level relative to unscreened indices. The analysis during the Dotcom and Subprime crises shows that, globally, the results are time‐invariant. In sum, Islamic screening is tantamount to prudential rules that lead to low‐risk portfolios; it is, in its essence, a risk‐reduction strategy. Findings suggest that Sharia‐compliant stocks would be of great attractiveness to risk‐averse investors. They will find their claim in this asset class, as its riskiness is relatively low. For banks, a lower VaR of Islamic investing implies fewer capital requirements for market risk when holding portfolios that include Sharia‐compliant stocks. Fewer capital requirements will enhance the banks' ability to invest and make profits, which will, in turn, result in reducing the cost of capital.

Risk conscious investment¶

The risk conscious investor is defined as the maximizer of a conservative valuation or a dynamic nonlinear expectation. Both the static and dynamic problems are addressed using distortions of tail probabilities or distortions of tail measures. The multivariate static problem is solved in the context of the multivariate bilateral gamma model. For the dynamic model states and their transitions are modeled in two ways. The first defines states by a parametric model for the return distribution with transitions described by a continuous-time finite state Markov chain between the distributional possibilities. In the second model states are represented by the first four power variations with transitions given by (OU) equations for modeling stochastically the first four power variations as Tempered Fractional Lévy Processes (TFLP). Numerical solutions for policy functions are implemented in trading 768 equity assets over seven years ending December 2021.

Energy firms in China towards resilience: A dynamic quantile connectedness approach

Α comprehensive understanding of the energy and technology sectors is imperative for the implementation of effective climate and industrial policies. This study investigates the quantile interconnectedness between the top five energy equities and the top five technology equities, listed on the Shanghai Stock Exchange 50 Index (SSE50), based on market capitalization. Employing the Quantile VAR methodology, we uncover significant interconnectedness across various quantiles of the return distribution for both energy and technology equities. This implies that price fluctuations within one sector can have a significant effect on the other. Furthermore, our analysis reveals directional connectedness, where energy equities consistently influence technology equity prices throughout the entire period of investigation. Additionally, a heightened degree of interconnectedness is observed during periods of extreme market movements, i.e. in the upper and lower tails of the return distribution. This suggests a more integrated system during stressed conditions, where all equities under investigation exhibit a magnified influence on each other, potentially leading to amplified volatility spillovers. These findings provide valuable insights for investors, portfolio managers, and policymakers, enabling them to develop more informed investment strategies and construct robust risk management frameworks.

Exploring decision-making: experimental observations on project selection and the impact of justification pressure

AbstractIn this experimental investigation, we explore the impact of justification on project choices. Introducing a novel element, we implement asymmetric payoff schemes commonly employed in business, signifying distinct payoff distributions for the firm (principal) and the manager (agent). The agent has to choose one project from two options that differ in their risk-return profiles. The outcomes of our experiment substantiate our hypothesis, indicating that a mandate for justification decreases the probability of agents selecting the project with higher risk and return. The degree of this reduction appears to hinge on the nature of justification. Increased profit shares for the agent or a project recommendation from the principal can partially counterbalance the distortion in the project choice.

Cooperative V2G-enabled vehicle-to-vehicle sharing in energy and reserve markets: A coalitional approach

The dynamics of electric vehicles (EVs) charging significantly influence the current power system dynamics. However, with advancements in battery technology and charging infrastructure, EVs can also serve as energy storage systems through vehicle-to-grid (V2G) technology. This opens up possibilities for novel approaches, such as a coalition-based V2G-enabled vehicle-to-vehicle (V2V) energy and reserve sharing mechanism. Unlike traditional transactive energy models that often under-utilize EVs due to mismatches with smaller renewable outputs and peak loads, the proposed cooperative V2V sharing mechanism aims to maximize the use of EVs’ charging and discharging capabilities. It forms a grand coalition of EV users to optimize energy and reserve market participation. The model introduces mathematical formulations to describe how EVs collaborate in both energy and reserve markets. It ensures fairness and stability in pay allocations among users within the cooperative framework. The theoretical foundation includes proof of balance in the coalition approach and a two-stage imputation method to achieve fair and optimal payoff distribution. This minimizes incentives for users to defect from the coalition, ensuring stability. To address scalability challenges inherent in coalition formation problems, a decomposition algorithm is proposed. This algorithm enhances efficiency in solving problems that grow exponentially with the number of users. The effectiveness and superiority of this approach are validated through applications to various community energy systems of different sizes. The proposed plan can increase 22.21% of the total payoff in 10-users case and 22.39% in 30-users case. The computation time scales near-linearly with the number of users, although the computation scales exponentially with it. These demonstrations highlight its capability in modeling and solving complex energy sharing scenarios.

Actively managed equity mutual funds in emerging markets

In this study, we analyze the performance of mutual fund managers who operate in emerging markets. Emerging stock markets differ from mature markets in significant ways, such as weak prosecution for insider trading, poor corporate governance practices, and a lack of liquidity, which create market inefficiencies that active investment strategies can exploit. We evaluate conditional alphas and multiple measures of liquidity, with a focus on equity funds that invest in emerging markets to achieve abnormal returns. Our analysis shows that smaller funds are significantly impacted by liquidity and that conditioning for public information has a significant effect on the distribution of returns. We find evidence of positive abnormal returns, which suggests that active management in emerging markets can be profitable.

Modeling interactions in a dynamic heuristic business network

This article presents a novel model for understanding the structure and dynamics of business networks, emphasizing the role of propensities to connect and cooperate as key drivers. The model incorporates behavioral elements and imperfect information updates, departing from traditional rational actor approaches. Starting from the theoretical background, several propositions are outlined, such as dynamism, connection choices, costs, strategy selection, information update, and the update based on experiences. Through simulations, the study successfully demonstrates that the proposed model effectively captures essential characteristics of business networks, including reciprocity, complexity, adaptation, and cooperation. The findings highlight the significance of propensities to connect and cooperate in shaping network structure, evolution, and stability. Particularly, higher propensities to cooperate and connect lead to denser and more cohesive networks, fostering reciprocity, stability, and network performance. The increase only in connection propensities does not have the same result. The lower cooperation propensities result in scale-free networks and asymmetrical distribution of cumulative payoffs. This highlights a crucial insight: different levels of cooperation lead to distinct network properties. Practical implications, increasingly relevant with the rise of digital platforms and metaverse, suggest targeted interventions to enhance network effectiveness, such as incentivizing cooperation, reducing relationship costs, and promoting a culture of trust and collaboration. While providing valuable insights, certain limitations exist, such as not considering the influence of preexisting economic or social structures, equalizing costs and payoffs among actors, and overlooking specific reasons for network connections. Future research should address these refinements and explore their effects on network structure and process characteristics.

Ambiguity, limited market participation, and the cross-sectional stock return

Using expected utility under uncertain probability theory (EUUP, Izhakian, 2017, 2020), we study whether the ambiguity related to individual stocks is priced in the Chinese A-share market and the mechanism behind the ambiguity premium phenomenon. Theoretically, when the asset price is within a specific range, ambiguity-averse investors refrain from participating in trading. As asset ambiguity increases, high ambiguity-averse investors exit the market, with those with lower aversion remaining in the market (limited market participation phenomenon). Investors who remain in the market due to lower ambiguity aversion are willing to accept a low ambiguity premium. Empirically, we measure monthly ambiguity using the volatility of daily stock return distributions within a month and find the following: (1) The equal-weighted average returns of the most ambiguous portfolios (top 20%) are significantly lower than those of the least ambiguous portfolios (bottom 20%). (2) Ambiguity still significantly negatively affects the cross-sectional stock return after controlling for common firm characteristics. (3) The higher the ambiguity, the lower the future trading activity, supporting our theoretical predictions. These findings reveal the mechanism of the negative ambiguity premium in the Chinese A-share market. This study provides new ideas for building a factor-pricing model suitable for the A-share market and a fresh perspective for preventing systemic financial risk.

Asymmetry and the Cross-section of Option Returns

We propose a novel measure of upside asymmetry based on probability density function that utilizes information from the distribution of option returns. Our analysis of U.S. option data reveals a positive cross-sectional relationship between the upside asymmetry (RML) and subsequent option returns. This relationship cannot be explained by stock or option characteristics studied in previous literature and do not reverse in the following months. Additionally, the relationship is stronger among firms with higher costs of arbitrage. RML robustly predicts future realized and implied volatilities after controlling for historical volatility and lagged implied volatility.

Investment management approaches: A comprehensive review of equity trading simulators, methodological challenges, and future directions

This paper provides a comprehensive review of equity trading simulators, focusing on their performance in assuring pre-trade compliance and portfolio investment management. A systematic search was conducted that covered the period of January 2000 to May 2023 and used keywords related to equity trade simulators, portfolio management, pre-trade compliance, online trading, and artificial intelligence. Studies demonstrating the use of simulators and online platforms specific to portfolio investment management, written in English, and matching the specified query were included. Abstracts, commentaries, editorials, and studies unrelated to finance and investments were excluded. The data extraction process included data related to challenges in modern portfolio trading, online stock trading strategies, the utilization of deep learning, the features of equity trade simulators, and examples of equity trade simulators. A total of 32 studies were included in the systematic review and were approved for qualitative analysis. The challenges identified for portfolio trading included the subjective nature of the inputs, variations in the return distributions, the complexity of blending different investments, considerations of liquidity, trading illiquid securities, optimal portfolio execution, clustering and classification, the handling of special trading days, the real-time pricing of derivatives, and transaction cost models (TCMs). Portfolio optimization techniques have evolved to maximize portfolio returns and minimize risk through optimal asset allocation. Equity trade simulators have become vital tools for portfolio managers, enabling them to assess investment strategies, ensure pre-trade compliance, and mitigate risks. Through simulations, portfolio managers can test investment scenarios, identify potential hazards, and improve their decision-making process.

Mid-season nitrogen management for winter wheat under price and weather uncertainty

ContextIn-season nitrogen (N) management tools are essential for optimizing N application rates, maximizing farmers’ economic returns and minimizing adverse environmental impacts. The primary limitation to developing such tools is the risk associated with uncertainties in weather forecasts and crop price projections required to estimate yields and returns for different N rates. Therefore, characterizing the risk associated with these uncertainties is crucial for determining optimum N rates in-season. ObjectiveThis study investigated the N application decision-making process for farmers, accounting for risks associated with weather and crop price uncertainties through crop modeling and economic analysis. MethodsWe used field trial data for winter wheat in Kansas to examine how optimal nitrogen rates and economic returns vary over sites, years, and differing farmers’ risk attitudes. First, the Environmental Policy Integrated Climate (EPIC) agroecosystem model was used to simulate the distribution of final yields under different N applications during early spring. Then, an autoregressive moving average (ARMA) model estimated the wheat price distribution at harvest based on historical prices. Finally, optimal N application rates for farmers with different risk appetites were estimated using two risk decision models: the constant-absolute-risk-averse (CARA) expected utility model, which treats upside (higher-than-expected returns) and downside (lower-than-expected returns) deviations equally, and the invariant-preference, generalized-deviation (IPGD) model, which focuses on downside risk. ResultsWe found that optimal N rates vary greatly between sites and years, as well as across farmers with different risk preferences. Due to the positive skewness of economic return distribution, farmers tend to apply lower N rates when considering downside risk. On average, the optimal N rate for farmers with a CARA coefficient of 0.002 is 77 kg/ha in the CARA model and 67 kg/ha in the IPGD model. Compared to the outcome of risk-neutral N usage, risk-averse N usage for a farmer with a CARA coefficient of 0.008 could reduce the uncertainty (standard deviation) of return by 6.2 %, on average, while the expected return decreased by only 1.2 %. ConclusionsBy lowering the N rate, risk-averse farmers would reduce the uncertainty of returns and incur a minor return loss, suggesting the possibility of improving agricultural resilience while also improving N use efficiency. Our analysis also underscores the importance of yearly site-specific N management, given the substantial variation in optimal rates across years and locations. SignificanceThis study provides the foundation for an N application decision framework that considers both weather and price uncertainty. The analysis also demonstrates the potential co-benefit of enhancing agriculture’s climate and market resilience while potentially lowering N losses.

Volatility or higher moments: Which is more important in return density forecasts of stochastic volatility model?

Extensions of the stochastic volatility (SV) model focus on improving volatility inference or modeling higher moments of the return distribution. This study investigates which extension can better improve return density forecasts. By examining various specifications with S&P 500 daily returns for nearly 20 years, we find that a more accurate capture of volatility dynamics with realized volatility and implied volatility is more important than modeling higher moments for a conventional SV model in terms of both density and tail forecasts. The accuracy of volatility estimation and forecasts should be the precondition for higher moment extensions.

Positivity and convexity in incomplete cooperative games

Incomplete cooperative games generalize the classical model of cooperative games by omitting the values of some of the coalitions. This allows for incorporating uncertainty into the model and studying the underlying games and possible payoff distributions based only on the partial information. In this paper, we conduct a systematic investigation of incomplete games, focusing on two important classes: positive and convex games. Regarding positivity, we generalize previous results from a special class of minimal incomplete games to a general setting. We characterize the non-extendability to a positive game by the existence of a certificate and provide a description of the set of positive extensions using its extreme games. These results also enable the construction of explicit formulas for several classes of incomplete games with special structures. The second part deals with convexity. We begin with the case of non-negative, minimal incomplete games. We establish the connection between incomplete games and the problem of completing partial functions and, consequently, provide a characterization of extendability and a full description of the set of symmetric convex extensions. This set serves as an approximation of the set of convex extensions.