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The purpose of this paper is to derive a new option pricing model for options on futures calendar spreads. Calendar spread option volume has been low and a more precise model to price them could lead to lower bid-ask spreads as well as more accurate marking to market of open positions.

The new option pricing model is a two-factor model with the futures price and the convenience yield as the two factors. The key assumption is that convenience follows arithmetic Brownian motion. The new model and alternative models are tested using corn futures prices. The testing considers both the accuracy of distributional assumptions and the accuracy of the models’ predictions of historical payoffs.

Panel unit root tests fail to reject the unit root null hypothesis for historical calendar spreads and thus they support the assumption of convenience yield following arithmetic Brownian motion. Option payoffs are estimated with five different models and the relative performance of the models is determined using bias and root mean squared error. The new model outperforms the four other models; most of the other models overestimate actual payoffs.

The model is parameterized using historical data due to data limitations although future research could consider implied parameters. The model assumes that storage costs are constant and so it cannot separate between negative convenience yield and mismeasured storage costs.

The over 30-year search for a calendar spread pricing model has not produced a satisfactory model. Current models that do not assume cointegration will overprice calendar spread options. The model used by the Chicago Mercantile Exchange for marking to market of open positions is shown to work poorly. The model proposed here could be used as a basis for automated trading on calendar spread options as well as marking to market of open positions.

The model is new. The empirical work supports both the model’s assumptions and its predictions as being more accurate than competing models.

This paper proposes the Student's t Dynamic Linear Regression (St-DLR) model as an alternative to the various extensions/modifications of the ARCH type volatility model. The St-DLR differs from the latter models of volatility because it can incorporate exogenous variables in the conditional variance in a natural way. Moreover, it also addresses the following issues: (i) apparent long memory of the conditional variance, (ii) distributional assumption of the error, (iii) existence of higher moments, and (iv) coefficient positivity restrictions. The model is illustrated using Dow Jones data and the three-month T-bill rate. The empirical results seem promising, as the contemporaneous variable appears to account for a large portion of the volatility.

Empirical work in macroeconomics almost universally relies on the hypothesis of rational expectations (RE).

This chapter departs from the literature by considering a variety of alternative expectations formation models. We study the econometric properties of a popular New Keynesian monetary DSGE model under different expectational assumptions: the benchmark case of RE, RE extended to allow for “news” about future shocks, near-RE and learning, and observed subjective expectations from surveys.

The results show that the econometric evaluation of the model is extremely sensitive to how expectations are modeled. The posterior distributions for the structural parameters significantly shift when the assumption of RE is modified. Estimates of the structural disturbances under different expectation processes are often dissimilar.

The modeling of expectations has important effects on the ability of the model to fit macroeconomic time series. The model achieves its worse fit under RE. The introduction of news improves fit. The best-fitting specifications, however, are those that assume learning. Expectations also have large effects on forecasting. Survey expectations, news, and learning all work to improve the model's one-step-ahead forecasting accuracy. RE, however, dominate over longer horizons, such as one-year ahead or beyond.

The purpose of this paper is to provide a review of inventory management articles published in major logistics outlets, identify themes from the literature and provide future direction for inventory management research to be published in logistics journals.

Articles published in major logistics articles, beginning in 1976, which contribute to the inventory management literature are reviewed and cataloged. The articles are segmented based on major themes extracted from the literature as well as key assumptions made by the particular inventory management model.

Two major themes are found to emerge from logistics research focused on inventory management. First, logistics researchers have focused considerable attention on integrating traditional logistics decisions, such as transportation and warehousing, with inventory management decisions, using traditional inventory control models. Second, logistics researchers have more recently focused on examining inventory management through collaborative models.

This paper catalogs the inventory management articles published in the major logistics journals, facilitates the awareness and appreciation of such work, and stands to guide future inventory management research by highlighting gaps and unexplored topics in the extant literature.

The assumption that a set of observed variables is a function of an underlying common factor plus some error has dominated measurement in marketing and the social sciences in general for decades. This view of measurement comes with assumptions, which, however, are rarely discussed in research. In this article, we question the legitimacy of several of these assumptions, arguing that (1) the common factor model is rarely correct in the population, (2) the common factor does not correspond to the quantity the researcher intends to measure, and (3) the measurement error does not fully capture the uncertainty associated with measurement. Our discussions call for a fundamental rethinking of measurement in the social sciences. Adapting an uncertainty-centric approach to measurement, which has become the norm in in the physical sciences, offers a means to address the limitations of current measurement practice in marketing.

This essay is a review of the recent literature on the methodology of economics, with a focus on three broad trends that have defined the core lines of research within the discipline during the last two decades. These trends are: (a) the philosophical analysis of economic modelling and economic explanation; (b) the epistemology of causal inference, evidence diversity and evidence-based policy and (c) the investigation of the methodological underpinnings and public policy implications of behavioural economics. The final output is inevitably not exhaustive, yet it aims at offering a fair taste of some of the most representative questions in the field on which many philosophers, methodologists and social scientists have recently been placing a great deal of intellectual effort. The topics and references compiled in this review should serve at least as safe introductions to some of the central research questions in the philosophy and methodology of economics.