We analyze the problem of recovering the pricing kernel and real probability distribution from observed option prices, when the state variable is an unbounded diffusion process. We derive necessary and sufficient conditions for recovery. In the general case, these conditions depend on the properties of the diffusion process, but not on the pricing kernel. We also show that the same conditions determine whether recovery works in practice, when the continuous problem is approximated on a bounded or discrete domain without further specification of boundary conditions. Altogether, our results suggest that recovery is possible for many interesting diffusion processes on unbounded domains.

We introduce a dynamic noisy rational expectations model in which information diffuses through a general network of agents. In equilibrium, agents who are more closely connected have more similar period-by-period trades, and an agent’s profitability is determined by a centrality measure that is related to Katz centrality. Volatility after an information shock is more persistent in less central networks, and volatility and trading volume are also influenced by the network’s asymmetry and irregularity. Using account-level data of all portfolio holdings and trades on the Helsinki Stock Exchange between 1997 and 2003, we find support for the aggregate predictions, altogether suggesting that the market’s network structure is important for these dynamics.

By introducing a labor market into the neoclassical asset pricing model, limited capital market participation can be an equilibrium outcome. Labor contracts are derived endogenously as part of a dynamic equilibrium in a production economy. Firms write labor contracts that insure workers, allowing agents to achieve a Pareto optimal allocation even when the span of asset markets is restricted to just stocks and bonds. Capital markets facilitate this risk sharing because it is there that firms offload the labor market risk they assumed from workers. In effect, by investing in capital markets, investors provide insurance to wage earners who then optimally choose not to participate in capital markets.

We present a tractable general equilibrium model with multiple sectors in which firms offer workers incentive contracts and simultaneously raise capital in stock markets. Workers optimally invest in the stock market and at the same time hedge labour income risk. Firms rationally take agents' portfolio decisions into account. In equilibrium, the cost of capital of each sector is endogenous. The distortion induced by moral hazard generates counterintuitive effects on the real economy. For example, the value of labour market participation may be higher under moral hazard than under first best, further a positive productivity shock may decrease welfare in the moral hazard economy. In addition, our model generates predictions on the effects of moral hazard on asset markets. For example, in the presence of moral hazard, the capital asset pricing model fails because firms, by choosing optimal incentive contracts, transfer risk both through wages and through the stock market. This leads to several cross-sectional asset pricing “anomalies”, such as size and value effects. As we characterize optimal contracts, we can also present empirical predictions relating workers' compensation, firm productivity, firm size, and financial market abnormal returns.

[We develop a model for markets for catastrophic risk. The model explains why insurance providers may choose not to offer insurance for catastrophic risks and not to participate in reinsurance markets, even though there is a large enough market capacity to reach full risk sharing through diversification in a reinsurance market. This is a "nondiversification trap." We show that nondiversification traps may arise when risk distributions have heavy left tails and insurance providers have limited liability. When they are present, there may be a coordination role for a centralized agency to ensure that risk sharing takes place.]

[We show that several well-known asset pricing puzzles are largely mitigated if we endow the representative agent with an arbitrarily small minimum consumption level. This allows us to solve the model for parameter values where the standard "Lucas tree" model is not defined. For these parameters, disasters become more important, and the market risk premium therefore higher, even though consumption is less risky. Our model yields reasonable risk premia, Sharpe ratios, and discount rates; excess price volatility; and a high market price-dividend ratio. We derive closed-form solutions for all variables of interest.]

We use a detailed panel data set of Swedish households to investigate the relation between their labor income risk and financial investment decisions. In particular, we relate changes in wage volatility to changes in the portfolio holdings for households that switched industries between 1999 and 2002. We find that households do adjust their portfolio holdings when switching jobs, which is consistent with the idea that households hedge their human capital risk in the stock market. The results are statistically and economically significant. A household going from an industry with low wage volatility to one with high volatility ceteris paribus decreases its portfolio share of risky assets by up to 35%, or $15,575.

We study a competitive market for risk-sharing, in which risk-tolerant providers of risk protection, who face frictional costs in holding capital, offer coverage over a range of risk classes to risk-averse agents. We distinguish monoline and multiline industry structures and characterize when each structure is optimal. Markets for which the risks are limited in number, asymmetric or correlated will be served by monoline structures, whereas markets characterized by a large number of essentially independent risks will be served by many multiline firms. Our results are consistent with observed structures within insurance, and also have general implications for the financial services industry.

Recent results in value at risk analysis show that, for extremely heavy-tailed risks with unbounded distribution support, diversification may increase value at risk, and that generally it is difficult to construct an appropriate risk measure for such distributions. We further analyze the limitations of diversification for heavy-tailed risks. We provide additional insight in two ways. First, we show that similar non-diversification results are valid for a large class of risks with bounded support, as long as the risks are concentrated on a sufficiently large interval. The required length of the support depends on the number of risks available and on the degree of heavy-tailedness. Second, we relate the value at risk approach to more general risk frameworks. We argue that in markets for risky assets where the number of assets is limited compared with the (bounded) distribution support of the risks, unbounded heavy-tailed risks may provide a reasonable approximation. We suggest that this type of analysis may have a role in explaining various types of market failures in markets for assets with possibly large negative outcomes.

We analyze the performance of irrational investors, who mistake expected returns of assets in a multi-asset economy. Mistakes by probabilistically unsophisticated investors that a priori seem small lead to severe underperformance compared with rational investors, under general conditions. Our results contrast with previous studies of single-asset economies, which find modest underperformance by irrational investors. In a calibration, an irrational investor who mistakes expected returns by 20% loses almost 95% of his consumption and wealth in about 25 years. The welfare cost of this underperformance is significant, about 40% of the total wealth in the economy.