Time-Consistent No-Arbitrage Models of the Term Structure

We present an econometric procedure for calibrating no-arbitrage term structure models in a way that is time-consistent and robust to measurement errors. Typical no-arbitrage models are time-inconsistent because their parameters are assumed constant for pricing purposes despite the fact that the parameters change whenever the model is recalibrated. No-arbitrage models are also sensitive to measurement errors because they fit exactly each potentially contaminated bond price in the cross-section. We overcome both problems by evaluating bond prices using the joint dynamics of the factors and calibrated parameters and by locally averaging out the measurement errors. Our empirical application illustrates the trade-off between fitting as well as possible and overfitting the cross-section of bond prices due to measurement errors. After optimizing this trade-off, our approach fits almost exactly the cross-section of bond prices at each date and produces out-of-sample forecast errors that beat a random walk benchmark and are comparable to the results in the affine term structure literature. We find that non-linearities in the pricing kernel are important, lending support to quadratic term structure models.

Asset Pricing Implications of Firms' Financing Constraints

We incorporate costly external finance in an investment-based asset pricing model and investigate whether financing frictions are quantitatively important for pricing a cross-section of expected returns. We show that common assumptions about the nature of the financing frictions are captured by a simple financing cost' function, equal to the product of the financing premium and the amount of external finance. This approach provides a tractable framework for empirical analysis. Using GMM, we estimate a pricing kernel that incorporates the effects of financing constraints on investment behavior. The key ingredients in this pricing kernel depend not only on fundamentals', such as profits and investment, but also on the financing variables, such as default premium and the amount of external financing. Our findings, however, suggest that the role played by financing frictions is fairly negligible, unless the premium on external funds is procyclical, a property not evident in the data and not satisfied by most models of costly external finance.

Sources of Lifetime Inequality

Is lifetime inequality mainly due to differences across people established early in life or to differences in luck experienced over the working lifetime? We answer this question within a model that features idiosyncratic shocks to human capital, estimated directly from data, as well as heterogeneity in ability to learn, initial human capital, and initial wealth { features which are chosen to match observed properties of earnings dynamics by cohorts. We find that as of age 20, differences in initial conditions account for more of the variation in lifetime utility, lifetime earnings and lifetime wealth than do differences in shocks received over the lifetime. Among initial conditions, variation in initial human capital is substantially more important than variation in learning ability or initial wealth for determining how an agent fares in life. An increase in an agent's human capital affects expected lifetime utility by raising an agent's expected earnings pro¯le, whereas an increase in learning ability affects expected utility by producing a steeper expected earnings profile.Lifetime Inequality, Human Capital, Idiosyncratic Risk

Human Capital and Earnings Distribution Dynamics

Mean earnings and measures of earnings dispersion and skewness all increase in US data over most of the working life-cycle for a typical cohort as the cohort ages. We show that a benchmark human capital model can replicate these properties from the right distribution of initial human capital and learning ability. These distributions have the property that learning ability must differ across agents and that learning ability and initial human capital are positively correlated.

Futures Prices in a Production Economy with Investment Constraints

We document a new stylized fact regarding the term-structure of futures volatility. We show that the relation between the volatility of futures prices and the slope of the term structure of prices is non-monotone and has a %u201CV-shape%u201D'. This aspect of the data cannot be generated by basic models that emphasize storage while this fact is consistent with models that emphasize investment constraints or, more generally, time-varying supply-elasticity. We develop an equilibrium model in which futures prices are determined endogenously in a production economy in which investment is both irreversible and is capacity constrained. Investment constraints affect firms' investment decisions, which in turn determine the dynamic properties of their output and consequently imply that the supply-elasticity of the commodity changes over time. Since demand shocks must be absorbed either by changes in prices, or by changes in supply, time-varying supply-elasticity results in time-varying volatility of futures prices. Calibrating this model, we show it is quantitatively consistent with the aforementioned %u201CV-shape%u201D relation between the volatility of futures prices and the slope of the term-structure.

Asset Prices and Business Cycles with Costly External Finance

This paper asks whether the asset pricing fluctuations induced by the presence of costly external finance are empirically plausible. To accomplish this, we incorporate costly external finance into a dynamic stochastic general equilibrium model and explore its implications for the properties of the returns on key financial assets, such as stocks, bonds and risky loans. We find that the mean and volatility of the equity premium, although small, are significantly higher than those in comparable adjustment cost models. However, we also show that these results require a procyclical financing premium, a property that seems at odds with the data.

Sources of Lifetime Inequality

Is lifetime inequality mainly due to differences across people established early in life or to differences in luck experienced over the working lifetime? We answer this question within a model that features idiosyncratic shocks to human capital, estimated directly from data, as well as heterogeneity in ability to learn, initial human capital, and initial wealth. We find that, as of age 23, differences in initial conditions account for more of the variation in lifetime earnings, lifetime wealth and lifetime utility than do differences in shocks received over the working lifetime.Lifetime Inequality, Human Capital, Idiosyncratic Risk

Sources of Lifetime Inequality

Is lifetime inequality mainly due to differences across people established early in life or to differences in luck experienced over the working lifetime? We answer this question within a model that features idiosyncratic shocks to human capital, estimated directly from data, as well as heterogeneity in ability to learn, initial human capital, and initial wealth -- features which are chosen to match observed properties of earnings dynamics by cohorts. We find that as of age 20, differences in initial conditions account for more of the variation in lifetime utility, lifetime earnings and lifetime wealth than do differences in shocks received over the lifetime. Among initial conditions, variation in initial human capital is substantially more important than variation in learning ability or initial wealth for determining how an agent fares in life. An increase in an agent's human capital affects expected lifetime utility by raising an agent's expected earnings profile, whereas an increase in learning ability affects expected utility by producing a steeper expected earnings profile.

Asset pricing with idiosyncratic risk and overlapping generations

A number of existing studies have concluded that risk sharing allocations supported by competitive, incomplete markets equilibria are quantitatively close to first-best. Equilibrium asset prices in these models have been difficult to distinguish from those associated with a complete markets model, the counterfactual features of which have been widely documented. This paper asks if life cycle considerations, in conjunction with persistent idiosyncratic shocks which become more volatile during aggregate downturns, can reconcile the quantitative properties of the competitive asset pricing framework with those of observed asset returns. We begin by arguing that data from the Panel Study on Income Dynamics support the plausibility of such a shock process. Our estimates suggest a high degree of persistence as well as a substantial increase in idiosyncratic conditional volatility coincident with periods of low growth in U.S. GNP. When these factors are incorporated in a stationary overlapping generations framework, the implications for the returns on risky assets are substantial. Plausible parameterizations of our economy are able to generate Sharpe ratios which match those observed in U.S. data. Our economy cannot, however, account for the level of variability of stock returns, owing in large part to the specification of its production technology.Finance, OLG

How Well Do Banks Manage Their Reserves?

In this paper we investigate how well banks manage their reserves. The optimal policy takes into account expected foregone interest on excess reserves and penalty costs for going below required reserves. Using a unique panel data-set on daily clearing house settlements of a cross-section of Mexican banks we estimate the deposit uncertainty banks face, and in turn their optimal reserve behavior. The most important variables for forecasting the deposit uncertainty are the interbank fund-transfers of the day, certain calendar dates, and the interest differential between the money market rate and the discount rate - a measure reflecting the bank's opportunity cost of money holdings. For most banks the model's prediction accord relatively well with the observed reserve behavior of banks. The model produces reserves costs that are significantly smaller relative to the case when reserves are set via simple rule of thumb. Furthermore, alternative motives for holding reserves (such as liquidity and reputation effects) do not seem to be the explanation for why certain banks hold relatively large reserves.