Data analysis in MRI usually entails a series of processing procedures. One of these procedures is noise assessment, which in the context of this work, includes both the identification of noise-only pixels and the estimation of noise variance (standard deviation). Although noise assessment is critical to many MRI processing techniques, the identification of noise-only pixels has received less a...

We explore the analyst earnings forecasts data to study the interactive effect between disagreement and underreact to earnings news on asset prices. We find that (1) changes in the mean of forecasted earnings as an underreaction measure positively predict future returns, that (2) changes in the standard deviation of forecasted earnings as a disagreement measure negatively predict future returns...

How are risks evaluated? Here risk is meant in the simplest sense: facing certain gains or losses, with given probabilities. The “subjective” approach considers each individual decision-maker separately, and proceeds according to that decision-maker’s preference and utility. But can risks be evaluated in an “objective” manner—depending only on the risks themselves and not on the specific decisi...

We develop a model of portfolio choice that nests the views of Keynes—who advocates concentration in a few familiar assets—and Markowitz—who advocates diversification across assets. We rely on the concepts of ambiguity and ambiguity aversion to formalize the idea of an investor's "familiarity" toward assets. The model shows that when an investor is equally ambiguous about all assets, then the o...

Let $a_1, \ldots, a_n \in \mathbb{R}$ satisfy $\sum_i a_i^2 = 1$, and let $\varepsilon_1, \varepsilon_n$ be uniformly random $\pm 1$ signs $X \sum_{i=1}^{n} a_i \varepsilon_i$. It is conjectured that \varepsilon_i$ has $\Pr[X \geq 1] 7/64$. The best lower bound so far $1/20$, due to Oleszkiewicz. In this paper we improve 6/64$.

This paper develops a parametric family of models of generalized autoregressive heteroscedasticity (garch). The family nests the most popular symmetric and asymmetric garch models, thereby highlighting the relation between the models and their treatment of asymmetry. Furthermore, the structure permits nested tests of different types of asymmetry and functional forms. U.S. stock return data reje...