This article develops precise connections among two general approaches to building interest rate models: a general equilibrium approach using a pricing kernel and the Heath, Jarrow, and Morton framework based on specifying forward rate volatilities and the market price of risk. The connections exploit the observation that a pricing kernel is uniquely determined by its drift. Through these connections we provide, for any arbitrage-free term structure model, a representative-consumer real production economy supporting that term structure model in equilibrium. We put particular emphasis on models in which interest rates remain positive. By modeling the dynamics of the drift of the pricing kernel, we construct a new family of Markovian-positive interest rate models.

Abstract We develop a model of information and portfolio choice in which ex ante identical investors choose to specialize because of fixed attention costs required in learning about securities. Without this friction, investors would invest in all securities and would be indifferent across a wide range of information choices. When securities’ dividends depend on an aggregate (macro) risk factor and idiosyncratic (micro) shocks, fixed attention costs lead investors to specialize in either macro or micro information. Our results favor Samuelson’s dictum that markets are more micro than macro efficient. We derive testable predictions from our model and find empirical support for our predictions in specialization by U.S. equity mutual funds. (JEL G12, G14, G23) 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.

The recent financial crisis has prompted much new research on the interconnectedness of the modern financial system and the extent to which it contributes to systemic fragility. Network connections diversify firms' risk exposures, but they also create channels through which shocks can spread by contagion. We review the extensive literature on this issue, with the focus on how network structure interacts with other key variables such as leverage, size, common exposures, and short-term funding. We discuss various metrics that have been proposed for evaluating the susceptibility of the system to contagion and suggest directions for future research. (JEL D85, E44, G01, G21, G22, G23, G28)

We study the design and incentive effects of contingent convertible debt. With contingent convertibles, the endogenous bankruptcy boundary can be at either of two levels: one with lower default risk or one at which default precedes conversion. An increase in debt moves the firm from the first regime to the second, a phenomenon we call debt-induced collapse. Setting the conversion trigger sufficiently high avoids this hazard. Given this condition, we investigate the effect of contingent capital and debt maturity on optimal capital structure, debt overhang, and asset substitution. We calibrate the model to large banks during the financial crisis.

This article develops precise connections among two general approaches to building interest rate models: a general equilibrium approach using a pricing kernel and the Heath, Jarrow, and Morton framework based on specifying forward rate volatilities and the market price of risk. The connections exploit the observation that a pricing kernel is uniquely determined by its drift. Through these connections we provide, for any arbitrage-free term structure model, a representative-consumer real production economy supporting that term structure model in equilibrium. We put particular emphasis on models in which interest rates remain positive. By modeling the dynamics of the drift of the pricing kernel, we construct a new family of Markovian-positive interest rate models.

We analyze the internal consistency of using the market price of a firm's equity to trigger a contractual change in the firm's capital structure, given that the value of the equity itself depends on the firm's capital structure. Of particular interest is the case of contingent capital for banks, in the form of debt that converts to equity, when conversion is triggered by a decline in the bank's stock price. We analyze the problem of existence and uniqueness of equilibrium values for a firm's liabilities in this context, meaning values consistent with a market‐price trigger. Discrete‐time dynamics allow multiple equilibria. In contrast, we show that the possibility of multiple equilibria can largely be ruled out in continuous time, where the price of the triggering security adjusts in anticipation of breaching the trigger. Our main condition for existence of an equilibrium requires that the consequences of triggering a conversion be consistent with the direction in which the trigger is crossed. For the design of contingent capital with a stock price trigger, this condition may be interpreted to mean that conversion should be disadvantageous to shareholders, and it is satisfied by setting the trigger sufficiently high. Uniqueness follows provided the trigger is sufficiently accessible by all candidate equilibria. We illustrate precise formulations of these conditions with a variety of applications.

Regulatory changes in the over-the-counter (OTC) derivatives market seek to reduce systemic risk. The reforms require that standardized derivatives be cleared through central counterparties (CCPs), and they set higher capital and margin requirements for non-centrally cleared derivatives. We investigate whether these requirements create a cost incentive in favor of central clearing, as intended. We compare the total capital and collateral costs when banks transact fully bilaterally and when they clear all contracts through CCPs. We calibrate our model using data on the OTC market collected by the Federal Reserve. We find that the cost incentive may not favor central clearing. The main factors driving the cost comparison are netting benefits, the margin period of risk, and CCP guarantee fund requirements. Lower guarantee fund requirements lower the cost of clearing but make CCPs less resilient.

Interconnections among financial institutions create potential channels for contagion and amplification of shocks to the financial system. We estimate the extent to which interconnections increase expected losses and defaults under a wide range of shock distributions. In contrast to most work on financial networks, we assume only minimal information about network structure and rely instead on information about the individual institutions that are the nodes of the network. The key node-level quantities are asset size, leverage, and a financial connectivity measure given by the fraction of a financial institution’s liabilities held by other financial institutions. We combine these measures to derive explicit bounds on the potential magnitude of network effects on contagion and loss amplification. Spillover effects are most significant when node sizes are heterogeneous and the originating node is highly leveraged and has high financial connectivity. Our results also highlight the importance of mechanisms that go beyond simple spillover effects to magnify shocks; these include bankruptcy costs, and mark-to-market losses resulting from credit quality deterioration or a loss of confidence. We illustrate the results with data on the European banking system.

An increase in “unusual” news with negative sentiment predicts an increase in stock market volatility. Unusual positive news forecasts lower volatility. Our analysis is based on over 360,000 articles on 50 large financial companies, published during the period of 1996–2014. Unusualness interacted with sentiment forecasts company-specific and aggregate volatility several months ahead. Furthermore, unusual news is reflected more slowly in aggregate volatility than company-specific volatility. News measures from articles explicitly about the “market,” which are more easily accessible to investors, do not forecast volatility. The observed responses of volatility to news may be explained by attention constraints on investors.

This paper develops numerical approximations for pricing collateralized debt obligations (CDOs) and other portfolio credit derivatives in the multifactor Normal Copula model. A key aspect of pricing portfolio credit derivatives is capturing dependence between the defaults of the elements of the portfolio. But, compared with an independent-obligor model, pricing in a model with correlated defaults is more challenging. Our approach strikes a balance by reducing the problem of pricing in a model with correlated defaults to calculations involving only independent defaults. We develop approximations based on power series expansions in a parameter that scales the underlying correlations. These expansions express a CDO tranche price in a multifactor model as a series of prices in independent-obligor models, which are easy to compute. The approach builds on a classical approximation for multivariate Gaussian probabilities; we introduce an alternative representation that greatly reduces the number of terms required to evaluate the coefficients in the expansion. We also apply this method to the underlying problem of computing joint probabilities of multivariate normal random variables for which the correlation matrix has a factor structure.