Thanks are due to Hendrik Houthakker and Christopher Sims, for both encouragement and advice in developing this paper. As usual, all remaining errors are my own. This research was supported by a Harvard Dissertation Fellowship, NSF grant 33-708, and the

In the setting of ‘‘affine’ ’ jump-diffusion state processes, this paper provides an analytical treatment of a class of transforms, including various Laplace and Fourier transforms as special cases, that allow an analytical treatment of a range of valuation and econometric problems. Example

stock return dynamics can be described by a stochastic process with a continuous sample path. In this paper, an option pricing formula is derived for the more-general cast when the underlying stock returns are gcncrated by a mixture of both continuous and jump processes. The derived formula has most

. With the Stroop effect as an example, automatic processes are shown to be continuous and to emerge gradually with practice. Specifically, a computational model of the Stroop task simulates the time course of processing as well as the effects of learning. This was accomplished by combining the cascade mechanism

This paper examines the structure of option valuation problems and develops a new technique for their solution. It also introduces several jump and diffusion processes which have nol been used in previous models. The technique is applied lo these processes to find explicit option valuation formulas

as people do we must understand qualitative reasoning about processes, when they will occur, their effects, and when they will stop. Qualitative process theory defines a simple notion of physical process that appears useful as a language in which to write dynamical theories. Reasoning about processes also

of aggregate returns indexes and size-sorted portofolios. Although the rejections are due largely to the behavior of small stocks, they cannot be attributed completely to the effects of infrequent trading or timevarying volatilities. Moreover, the rejection of the random walk for weekly returns does

. With this algorithm, fairing very large surfaces, such as those obtained from volumetric medical data, becomes affordable. By combining this algorithm with surface subdivision methods we obtain a very effective fair surface design technique. We then extend the analysis, and modify the algorithm accordingly

on 22 of 37 benchmark problems, scales nearly linearly with the number of training examples, and can efficiently process noisy datasets containing hundreds of thousands of examples.

This paper examines a model of dynamic price adjustment based on the assumption that information disseminates slowly throughout the population. Compared with the commonly used sticky-price model, this sticky-information model displays three related properties that are more consistent with accepted