Representative Agent Research Articles

Probability weighting and equity premium prediction: Investing with optimism

AbstractEmpirically motivated theoretical models of probability weighting which overweight tail events are finding many applications in finance. However, probability weighting has not yet been applied to equity premium prediction or to constructing optimal market timing investment strategies. We show that a measure of market optimism from a representative agent asset pricing model with probability weighting can be used to construct optimal dynamic investment strategies that outperform the buy‐and‐hold strategy and strategies generated by 17 leading equity premium predictors. We further show that this theory‐based measure of market optimism predicts the equity premium and market Sharpe ratio in‐sample and out‐of‐sample. The predictability is not subsumed by disaster probabilities, market sentiment, or market skewness. Our results indicate that our theory‐based measure provides a distinct channel for predicting aggregate stock returns.

Clozapine impaired glucose-stimulated insulin secretion partly by increasing plasma 5-HT levels due to the inhibition of OCT1-mediated hepatic 5-HT uptake in mice.

Patients taking atypical antipsychotics (AAPs), especially clozapine, are often associated with hyperglycaemia. Here, clozapine served as a representative agent for investigating how AAPs induce hyperglycaemia. In normal mice and mice fed a high fat diet (HFD), clozapine impaired glucose tolerance and glucose-stimulated insulin secretion (GSIS) following intraperitoneal glucose administration and increased plasma 5-HT levels. Intraperitoneal 5-HT administration also impaired glucose tolerance and GSIS in mice. In INS-1 cells, high 5-HT levels impaired GSIS, which was attenuated by the 5-HTR3 antagonist tropisetron or by silencing 5-HTR3a. The 5-HTR2a agonist TCB2 attenuated clozapine-induced GSIS impairment. Silencing 5-HTR2a or the 5-HTR2a antagonist ketanserin impaired GSIS. In mice, 5-HT administration impaired GSIS, which was attenuated by tropisetron but aggravated by clozapine. Clozapine increased plasma [2H]5-HT exposure following intravenous administration to mice. In HEK293-OCT1 cells, clozapine inhibited [2H]5-HT and MPP+ uptake. Clozapine or OCT1 silencing impaired 5-HT metabolism in mouse primary hepatocytes, demonstrating that clozapine increased plasma 5-HT levels via the inhibition of OCT1-mediated hepatic 5-HT uptake. Liver-specific silencing of OCT1 increased plasma [2H]5-HT exposure and 5-HT levels and impaired GSIS and glucose tolerance in mice. In conclusion, clozapine impaired GSIS and glucose tolerance by increasing plasma 5-HT levels via the inhibition of OCT1-mediated hepatic 5-HT uptake. Increased 5-HT impaired GSIS by activating islet 5-HTR3a. The antagonistic effect of clozapine on islet 5-HTR2a also contributed to GSIS impairment. The finding that clozapine-induced GSIS impairment was attributed to increased 5-HT levels via the inhibition of OCT1-mediated hepatic 5-HT uptake may partly explain hyperglycaemia caused by other AAPs.

Nominal GDP growth targeting vs. Taylor rules in a model with financial frictions

Nominal GDP growth targeting has gained favor over the last few decades in monetary policy literature (e.g., Ireland 2020; Garín et al. 2016; Beckworth and Hendrickson 2020). Calibrated, small-scale New Keynesian DSGE models have supported the proposition that nominal GDP growth (NGDP growth) rate targeting will improve welfare as compared with alternative strategies such as inflation targeting and Taylor rules with standard parameter settings. This paper examines that proposition in the context of a medium-scale New Keynesian model having a financial sector with frictions. In that model, NGDP growth rate targeting does poorly as compared with the Optimal Taylor rule and a wide range of Taylor rule settings leading up to the Optimal (representative agent utility maximizing) Taylor rule. The presence of the financial sector reveals that not only is NGDP growth rate targeting the least successful among the alternative targets and rules in creating welfare in this model; NGDP growth rate targeting also creates dramatically higher volatility of the policy rate and most financial variables.

Measuring socially appropriate social preferences

This paper extends the literature on structural estimation of social preferences to account for the desire to adhere to social norms and hide one's true intentions via moral wiggle room. We conduct an experiment to test whether accounting for normatively appropriate behavior allows us to distinguish between preference types who care about outcomes versus adhering to social norms and whether the introduction of moral wiggle room undermines the stability of social preference estimates. We find that social preference estimates are remarkably robust to the inclusion of moral wiggle room. However, the representative agent is strongly motivated by norms and failing to account for this motive in our model causes us to overestimate how much agents care about helping those who are worse off. Using finite mixture models to endogenously identify latent preference types, we replicate previous work finding that the majority of subjects can be classified as strong or moderate altruists when normative concerns are not considered. Accounting for the normative appropriateness of decisions when categorizing participants, however, reveals different motives across types: strong altruists are only marginally concerned with norms while the moderate altruists are highly sensitive to them and, once norms are taken into account, don't care at all about the outcomes of others. Our results thus recast the prior findings in a new light. Rather than the two most common types being strong altruists and moderate altruists, we find that they are better described as strong altruists and norm followers.

Study on the Pyrolysis and Incineration of Lewisite by Thermodynamic Equilibrium Calculation

AbstractLewisite is a representative chemical warfare agent that contains arsenic. Thermal decomposition is an essential method for Lewisite destruction, and appropriate destruction conditions can enhance the environmental friendliness of this method by minimizing the hazards of destruction products. In this study, the variation of Lewisite during pyrolysis and incineration was calculated, with particular emphasis on the evolution behaviors of arsenic and chloride with temperature, pressure, and molar ratio. The main products identified during pyrolysis were HCl(g), C, As, and AsCl3(g). Under anoxic conditions, AsCl3(g) and As were the dominant species. In oxygen‐enriched conditions, arsenic existed primarily as AsCl3(g), As2O5, As2O4, and AsOCl. Below 600 °C, arsenic predominantly existed in high valence forms such as As2O5. Above 600 °C, the binding capacity of chlorine and arsenic was stronger than that of oxygen, resulting in an AsCl3(g) molar fraction exceeding 75 % at 1200 °C. The primary chlorides in oxygen‐enriched conditions were Cl2(g), HCl(g), AsCl3(g), and Cl(g). Lower incineration temperatures (around 500 °C), sufficient oxygen conditions (molar ratio above 4 : 1), and positive pressures were conducive to reducing the toxicity of destruction products. The results are in good agreement with the experimental data.

Hybrid platforms with free entry: demand-enhancing activities

AbstractWe study the decision of a platform as to the quantity and quality of the products to sell directly on its own marketplace, where also third-party sellers decide how much to invest in the quality of their products. Using a representative agent framework that is based on a quasi-linear quality-augmented indirect utility function, we show that, under free entry, the quality investments of sellers do not change with platform entry, while the number of joining sellers does. Moreover, contrarily to what is found in the received literature, the platform may go hybrid even in the case it does not enjoy a competitive advantage vis-á-vis third-party sellers. We then study the welfare implications of a platform’s entry decision and show that promoting sellers’ investments and/or contrasting platform entry may lead to a larger as well as a lower consumer welfare. This depends on the platform’s response in equilibrium, both in terms of changes of its quality-enhancing investments and the fee charged on the revenues of third-party sellers.

Formal insurance and altruism networks

We study how altruism networks affect the demand for formal insurance. Agents with CARA utilities are connected through a network of altruistic relationships. Incomes are subject to a common shock and to a large individual shock, generating heterogeneous damages. Agents can buy formal insurance to cover the common shock, up to a coverage cap. We find that ex-post altruistic transfers induce interdependence in ex-ante formal insurance decisions. We characterize the Nash equilibria of the insurance game and show that agents act as if they are trying to maximize the expected utility of a representative agent with average damages. Altruism thus tends to increase demand of low-damage agents and to decrease demand of high-damage agents. Its aggregate impact depends on the interplay between demand homogenization, the zero lower bound and the coverage cap. We find that aggregate demand is higher with altruism than without altruism at low prices and lower at high prices. Nash equilibria are constrained Pareto efficient.

Old-Keynesianism in the New Keynesian Model

I investigate an ‘old Keynesian’ fiscal policy in which government spending endogenously responds to inflation and the output gap when the nominal interest rate is pegged at the ZLB. I do so within a standard Representative Agent New Keynesian model (RANK), as well as a two-period Overlapping Generations New Keynesian model (OLG-NK). For both model versions, I find that the equilibrium values for inflation and the output gap under a standard Taylor rule regime can be replicated under the ‘old Keynesian’ regime. However, a unique stable countercyclical equilibrium is only feasible within the OLG-NK model and crucially depends on the pension system.

Rationality, inequality, and the output gap: evidence from a disaggregated Keynesian cross diagram

AbstractThis paper examines the conditions under which a representative agent (RA) model can accurately approximate the output of a multi-agent model that assumes many interacting agents. The study compares the widely used Keynesian cross diagram, which employs a representative agent, to an extended model that explicitly considers multiple interacting households and firms. The extended model reduces to the original RA model when there is one agent of each type. The findings suggest that the RA Keynesian cross diagram model does not accurately approximate the extended multi-agent model when: (1) the network structure of the economy is asymmetric (e.g., firms have different sizes), or (2) the rationality of agents is too low. Additionally, when income inequality is considered by introducing capitalists, the RA model is no longer a good approximation, even if agents are rational. However, fiscal policies that redistribute income can improve the accuracy of the RA model’s predictions. In general, features that increase the overall rationality of the economy and decrease heterogeneity tend to improve the performance of the RA approximation.

The molecular mechanisms of peptidyl-prolyl cis/trans isomerase Pin1 and its relevance to kidney disease.

Pin1 is a member of the peptidyl-prolyl cis/trans isomerase subfamily and is widely expressed in various cell types and tissues. Alterations in Pin1 expression levels play pivotal roles in both physiological processes and multiple pathological conditions, especially in the onset and progression of kidney diseases. Herein, we present an overview of the role of Pin1 in the regulation of fibrosis, oxidative stress, and autophagy. It plays a significant role in various kidney diseases including Renal I/R injury, chronic kidney disease with secondary hyperparathyroidism, diabetic nephropathy, renal fibrosis, and renal cell carcinoma. The representative therapeutic agent Juglone has emerged as a potential treatment for inhibiting Pin1 activity and mitigating kidney disease. Understanding the role of Pin1 in kidney diseases is expected to provide new insights into innovative therapeutic interventions and strategies. Consequently, this review delves into the molecular mechanisms of Pin1 and its relevance in kidney disease, paving the way for novel therapeutic approaches.

A consumption-based term structure model of bonds and equity

In this study, I propose a consumption-based asset pricing model to capture the dynamic properties of term structures of bonds and equity. I extend the long-run risks model by introducing a mean-reversion of dividend growth and the external habit formation of a representative agent. The mean-reverting dividend growth generates a negative equity term premium, while the habit formation augments the equity premium and renders an upward-sloping bond yield curve, on average. In addition, fluctuations in the surplus consumption ratio and the conditional variance of consumption growth allow the model to reproduce pro-cyclical variations in bond and equity yield spreads and counter-cyclical variations in bond and equity term premiums, as observed in the U.S. data.

Information warfare: Analyzing COVID-19 news and its economic fallout in the US

By separating the ‘good news’ from the ‘bad news,’ we analyze the asymmetric effects of the infodemic and market emotions on the US weekly economic activity index (WEI) during COVID-19. The ‘hot stove effect’ and complicated, asymmetric, and time-varying coherence structures are present when we use the nonlinear autoregressive distributed lag and wavelet coherence methods. Our findings contrast existing literature based on the pandemic’s symmetric impacts, which claim that the economy was negatively affected by non-pharmaceutical actions and that the severity of COVID-19 was the cause of the negative economic and financial shocks. We also discovered that while ‘good news’ has strong and favorable effects on WEI, ‘bad news’ has little to no impact. Furthermore, policymakers react to ‘bad news’ more slowly than they do to ‘good news.’ Therefore, to affect the representative agent’s behavior and ensure an effective response to pandemic occurrences, policymakers should distinguish between deterioration (bad news) and improvements (good news) in shocks.

Exploring the constraints on artificial general intelligence: A game-theoretic model of human vs machine interaction

The potential emergence of artificial general intelligence (AGI) systems has sparked intense debate among researchers, policymakers, and the public due to their potential to surpass human intelligence in all domains. This note argues that for an AI to be considered “general”, it should achieve superhuman performance not only in zero-sum games but also in general-sum games, where winning or losing is not clearly defined. In this note, I propose a game-theoretic framework that captures the strategic interactions between a representative human agent and a potential superhuman machine agent. Four assumptions underpin this framework: Superhuman Machine, Machine Strategy, Rationality, and Strategic Unpredictability. The main result is an impossibility theorem, establishing that these assumptions are inconsistent when taken together, but relaxing any one of them results in a consistent set of assumptions. This note contributes to a better understanding of the theoretical context that can shape the development of superhuman AI.

Total Syntheses of Phorbol and 11 Tigliane Diterpenoids and Their Evaluation as HIV Latency-Reversing Agents.

Tigliane diterpenoids possess exceptionally complex structures comprising common 5/7/6/3-membered ABCD-rings and disparate oxygen functionalities. While tiglianes display a wide range of biological activities, compounds with HIV latency-reversing activity can eliminate viral reservoirs, thereby serving as promising leads for new anti-HIV agents. Herein, we report collective total syntheses of phorbol (13) and 11 tiglianes 14-24 with various acylation patterns and oxidation states, and their evaluation as HIV latency-reversing agents. The syntheses were strategically divided into five stages to increase the structural complexity. First, our previously established sequence enabled the expeditious preparation of ABC-tricycle 9 in 15 steps. Second, hydroxylation of 9 and ring-contractive D-ring formation furnished phorbol (13). Third, site-selective attachment of two acyl groups to 13 produced four phorbol diesters 14-17. Fourth, the oxygen functionalities were regio- and stereoselectively installed to yield five tiglianes 18-22. Fifth, further oxidation to the most densely oxygenated acerifolin A (23) and tigilanol tiglate (24) was realized through organizing a 3D shape of the B-ring. Assessment of the HIV latency-reversing activities of the 12 tiglianes revealed seven tiglianes (14-17 and 22-24) with 20- to 300-fold improved efficacy compared with prostratin (12), a representative latency-reversing agent. Therefore, the robust synthetic routes to a variety of tiglianes with promising activities devised in this study provide opportunities for advancing HIV eradication strategies.

Scalable Learning for Spatiotemporal Mean Field Games Using Physics-Informed Neural Operator

This paper proposes a scalable learning framework to solve a system of coupled forward–backward partial differential equations (PDEs) arising from mean field games (MFGs). The MFG system incorporates a forward PDE to model the propagation of population dynamics and a backward PDE for a representative agent’s optimal control. Existing work mainly focus on solving the mean field game equilibrium (MFE) of the MFG system when given fixed boundary conditions, including the initial population state and terminal cost. To obtain MFE efficiently, particularly when the initial population density and terminal cost vary, we utilize a physics-informed neural operator (PINO) to tackle the forward–backward PDEs. A learning algorithm is devised and its performance is evaluated on one application domain, which is the autonomous driving velocity control. Numerical experiments show that our method can obtain the MFE accurately when given different initial distributions of vehicles. The PINO exhibits both memory efficiency and generalization capabilities compared to physics-informed neural networks (PINNs).

Top–down and bottom–up information acquisition: Application to financial markets

Recent studies confirm the main prediction of the rational inattention framework that the perceived value of information (top–down) as a key driver of attention. However, these investigations also report stimulus-driven salience effects (bottom–up) that counteract the framework’s predictions. In this manuscript, I propose an extension to the standard rational inattention model by incorporating bottom–up attention processes such as salience-effects through varying information processing costs. Applied to asset pricing with a representative agent a higher information salience generally reduces the cost of information processing (attention) needed for the same level of uncertainty reduction. Due to a substitution effect in the attention allocation across information, an attention-maximizing salience emerges. In general, a higher information salience consistently enhances asset price informativeness.

Effect of Corruption on Human Capital in Franc Zone Countries: An Opportunity Cost Analysis

The objective of this paper is to analyze the impact of corruption on human capital in African Franc zone countries. Assuming that the representative agent is rational, we develop a theoretical model in which effort, the real component of the human capital accumulation process, encompasses learning time, the learner’s personal resources, and the educational public good. The learner makes a trade-off between effort and corruption according to the opportunity cost of each option. Thus, in an environment where corruption (large and small) is endemic and where there are very few sanction mechanisms, the learner considers it rational to bribe at the expense of effort. Empirical analysis shows that a drop in the corruption perception index (reflecting an increase in corruption) of one-point leads to a drop in the human capital index of 0.175 percentage points. On the other hand, the impact is positive for public spending on education, official development assistance, real GDP per capita, and the management ratio. These variables reduce the opportunity cost of the effort. A number of recommendations are put forward with a view to reducing the cost of effort and increasing the cost of corruption.

Оценка гетерогенных параметров динамики дохода и межвременных потребительских предпочтений

In modern economic literature, much attention is paid to estimates of the structural parameters of consumer choice and the process of income generation. Such estimations play an important role in determining the optimal insurance system, optimal monetary and fiscal policies, as well as in assessing the welfare of the population. In this paper, we estimate a set of parameters that determine the dynamics of household income and consumption in Russia. These parameters are heterogeneous at the household level; the paper uses the estimation method proposed in [Alan et al., 2018]. To estimate the parameters we use RLMS panel data for the period 2005–2019. The results indicate the presence of significant co-dependent heterogeneity of the parameters determining the dynamics of income and consumption. These results reflect the specifics of the consumer behavior of households in Russia. In particular, significant heterogeneity in the sensitivity of consumption to income shocks indicates a large variability of consumption insurance mechanisms available to households. In addition, there is significant heterogeneity in the dispersion of income shocks, which is critical in the context of the discussion of heterogeneous insurance instruments against income shocks (e.g., access to financial assets), as households with higher variation in unanticipated income change value these instruments more. This study is the first to estimate the full distribution of heterogeneous parameters of consumption and income dynamics using Russian data. Estimates of heterogeneous parameters are important for constructing theoretical models of consumer choice and analyzing optimal social policy. In particular, high values of the level of parameter heterogeneity indicate that standard models with a representative agent are not suitable for optimal policy choices.

A Decomposition of Conditional Risk Premia and Implications for Representative Agent Models

We develop a methodology to decompose the conditional market risk premium and risk premia on higher-order moments of excess market returns into risk premia related to contingent claims on down, up, and moderate market returns. The decomposition exploits information about the risk-neutral market return distribution embedded in option prices, but does not depend on assumptions about the functional form of investor preferences or about the market return distribution. The total market risk premium is highly time-varying, as are the contributions from downside, upside, and central risk. Time-series variation in risk premia associated with each region is primarily driven by variation in risk prices associated with the probability of entering each region at short horizons, but it is primarily driven by variation in risk quantities at longer horizons. Analogous decompositions implied by prominent representative agent models generally fail to match the dynamic risk premium behavior implied by the data. Our results provide a set of new empirical facts regarding the drivers of conditional risk premia and identify new challenges for representative agent models. This paper was accepted by Lukas Schmid, finance. Supplemental Material: The internet appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01663 .

Uncertainty in boundedly rational household adaptation to environmental shocks.

Despite the growing calls to integrate realistic human behavior in sustainability science models, the representative rational agent prevails. This is especially problematic for climate change adaptation that relies on actions at various scales: from governments to individuals. Empirical evidence on individual adaptation to climate-induced hazards reveals diverse behavioral and social factors affecting economic considerations. Yet, implications of replacing the rational optimizer by realistic human behavior in nature-society systems models are poorly understood. Using an innovative evolutionary economic agent-based model we explore different framings regarding household adaptation behavior to floods, leveraging on behavioral data from a household survey in Miami, USA. We find that a representative rational agent significantly overestimates household adaptation diffusion and underestimates damages compared to boundedly rational behavior revealed from our survey. This "adaptation deficit" exhibited by a population of empirically informed agents is explained primarily by diverse "soft" adaptation constraints-awareness, social influences-rather than heterogeneity in financial constraints. Besides initial inequality disproportionally impacting low/medium adaptive capacity households post-flood, our findings suggest that even under a nearly complete adaptation diffusion, adaptation benefits are uneven, with late or less-efficient actions locking households to a path of higher damages, further exacerbating inequalities. Our exploratory modeling reveals that behavioral assumptions shape the uncertainty of physical factors, like exposure and objective effectiveness of flood-proofing measures, traditionally considered crucial in risk assessments. This unique combination of methods facilitates the assessment of cumulative and distributional effects of boundedly rational behavior essential for designing tailored climate adaptation policies, and for equitable sustainability transitions in general.