Short-selling constraints are common in financial markets, while physical assets such as housing often lack markets for short-selling altogether. As a result, investment decisions are often restricted by such constraints. This paper studies asset prices in behavioral heterogeneous-belief models with short-selling constraints and arbitrarily many belief types. We provide conditions on beliefs such that short-selling constraints bind for different types, along with analytic expressions for price and demands that allow us to construct fast solution algorithms relevant for a wide range of models. An application studies how an alternative uptick rule, as in the United States, affects price dynamics and wealth distribution in a market with many belief types in evolutionary competition. In a numerical example, we highlight a scenario in which a modified version of the alternative uptick rule, triggered by smaller percentage falls in price, reduces both asset mispricing and wealth inequality relative to the current regulation. As extensions, we show how our method applies to multiple asset markets with short-selling constraints, additional heterogeneities, and price setting by a market maker.

We study a weighted comonotonic risk-sharing problem among multiple agents with distortion risk measures under heterogeneous beliefs. The explicit forms of optimal allocations are obtained, which are Pareto-optimal. A necessary and sufficient condition is given to ensure the uniqueness of the optimal allocation, and sufficient conditions are given to obtain an optimal allocation of the form of excess of loss or full insurance. The optimal allocation may satisfy individual rationality depending on the choice of the weight. When the distortion risk measure is value at risk or tail value at risk, an optimal allocation is generally of the excess-of-loss form. The numerical examples suggest that a risk is more likely to be shared among agents with heterogeneous beliefs, and the introduction of the weight enables us to prioritize some agents as part of a group sharing a risk.

This paper studies bilateral risk sharing under no aggregate uncertainty, where one agent has Expected-Utility preferences and the other agent has Rank-dependent utility preferences with a general probability distortion function. We impose exogenous constraints on the risk exposure for both agents, and we allow for any type or level of belief heterogeneity. We show that Pareto-optimal risk-sharing contracts can be obtained via a constrained utility maximization under a participation constraint of the other agent. This allows us to give an explicit characterization of optimal risk-sharing contracts. In particular, we show that an optimal risk-sharing contract contains allocations that are monotone functions of the likelihood ratio, where the latter is obtained from Lebesgue’s Decomposition Theorem.

This article attempts to extend Arrow’s theorem of the deductible to the case of belief heterogeneity, which allows the insured and the insurer to have different beliefs about the distribution of the underlying loss. Like Huberman et al. [(1983) Bell Journal of Economics14(2), 415–426], we preclude ex post moral hazard by asking both parties in the insurance contract to pay more for a larger realization of the loss. It is shown that, ceteris paribus, full insurance above a constant deductible is always optimal for any chosen utility function of a risk-averse insured if and only if the insurer appears more optimistic about the conditional loss given non-zero loss than the insured in the sense of monotone hazard rate order. We derive the optimal deductible level explicitly and then examine how it is affected by the changes of the insured’s risk aversion, the insurance price and the degree of belief heterogeneity.

We assume that firms are more risk averse than households and that they manage their risk through a financial sector, which consists of learning and hedging. Firms that learn (by observing demand shocks) face less uncertainty and produce more than firms that hedge (by selling future production at a fixed price). If a policy or parameter change stabilizes the economy, then there is less learning and usually less production. Welfare, however, is usually maximized when the financial sector, which requires inputs but does not directly provide utility or affect production, is smallest. Monetary policy can improve welfare by either taxing learning or subsidizing hedging. If firms are risk averse over nominal profits instead of real profits, then interest rate policy can also improve welfare by stabilizing prices and thus minimizing the size of the financial sector.

We investigate the effects of memory on the stability of evolutionary selection dynamics based on a multinomial logit model in a simple asset pricing model with heterogeneous beliefs. Whether memory is stabilizing or destabilizing depends in general on three key factors: (1) whether or not the weights on past observations are normalized; (2) the ecology or composition of forecasting rules, in particular the average trend extrapolation factor and the spread or diversity in biased forecasts; and (3) whether or not costs for information gathering of economic fundamentals have to be incurred.

The authors are indebted to the anonymous referee for a very detailed and insightful report on an earlier version of this paper that has led to many improvements in the current version. Address correspondence to: Tony He, School of Finance and Economics, University of Technology, Sydney, P.O. Box 123 Broadway, NSW 2007, Australia; e-mail: tony.he1@uts.edu.au. This paper studies the dynamics of a simple discounted present-value asset-pricing model where agents have different risk attitudes and follow different expectation formation schemes for the price distribution. A market-maker scenario is used as the market-clearing mechanism, in contrast to the more usual Walrasian scenario. In particular, the paper concentrates on models of fundamentalists and trend followers who follow recursive geometric-decay (learning) processes (GDP) with both finite and infinite memory. The analysis depicts how the dynamics are affected by various key elements (or parameters) of the model, such as the adjustment speed of the market maker, the extrapolation rate of the trend followers, the decay rate of the GDP, the lag length used in the learning GDP, and external random factors.