QUADRATIC TERM STRUCTURE MODELS FOR RISK-FREE AND DEFAULTABLE RATES

In this paper, quadratic term structure models (QTSMs) are analyzed and characterized in a general Markovian setting. The primary motivation for this work is to find a useful extension of the traditional QTSM, which is based on an Ornstein–Uhlenbeck (OU) state process, while maintaining the analytical tractability of the model. To accomplish this, the class of quadratic processes, consisting of those Markov state processes that yield QTSM, is introduced. The main result states that OU processes are the only conservative quadratic processes. In general, however, a quadratic potential can be added to allow QTSMs to model default risk. It is further shown that the exponent functions that are inherent in the definition of the quadratic property can be determined by a system of Riccati equations with a unique admissible parameter set. The implications of these results for modeling the term structure of risk-free and defaultable rates are discussed.     

EXPRESSING INVENTORY CONTROL POLICY IN THE TURNOVER CURVE

Estimation of the inventory level for an entire class of items is a valuable time saver when control of inventories at the aggregate level, rather than the item level, is of interest. Inventory approximation by location in supply chain network configuration and evaluation of inventory control policy shifts, are two examples of application. In this article, various popular inventory policies are related to a general function known as an inventory turnover curve that expresses inventory levels from the combined demand of multiple items. By knowing some basic item characteristics of representative items in a product class, the type of inventory policy being used, and the current aggregate inventory level, an inventory turnover curve can be constructed. This resulting turnover curve can be used to estimate inventory levels within 4.6%, on the average, of theoretically predicted ones.     

On Measuring the Value of a Nonmarket Good Using Market Data

We use line integral theory to lay out in a more general theoretical framework the conditions under which it is possible to measure with market data the welfare effects of a change in a nonmarket good. We present in detail a numerical method of measuring the value of nonmarket goods using market data, under either weak neutrality or weak complementarity. Our numerical method is more flexible than the existing analytical method because it can be used with any well-behaved Marshallian demand function, and can be used even when the willig condition does not hold.     

Manipulating uncertainty

Uncertainty about the distributional incidence of policy reforms may, if it impinges selectively on particular subsets of voters, alter the direction of the majority vote. This possibility should be a matter of special concern when subject to potential manipulation by a purposeful agent such as a Leviathan-like bureaucracy. This paper discusses a constitutional defense against such prospect. This paper was prepared for a conference on “Constitutional Status Quo and Prospects for Change” held at George Mason University in April, 1994. I am grateful to participants in that conference and to my colleagues at Nova, especially to Mário Páscoa, for their comments and criticisms. I also benefited from comments of an anonymous referee. Responsibility for errors remains with me.     

EARNINGS EXPECTATIONS, FIRM SIZE, AND THE INFORMATIVENESS OF STOCK PRICES

The notion that prices impound a wide array of information, including market expectations, has led to earnings forecast models conditioned on prices. Yet, presumably, analysts' forecasts capture both public information and certain private information not previously impounded in prices. Accordingly, price-based models are seemingly an inefficient, and less effective, source of expecta-tions. This article investigates this hypothesis using financial analysts', price-based, and naive forecasts. Results indicate that analysts' forecasts (1) are at least as accurate as price-based and naive models, and (2) yield better expectations for market tests relating returns and earnings. These inferences are robust across different information environments. The evidence suggests that analysts either possess private information or are more effective information processors, or both.