Correcting the errors: Volatility forecast evaluation using high-frequency data and realized volatilities

We develop general model-free adjustment procedures for the calculation of unbiased volatility loss functions based on practically feasible realized volatility benchmarks. The procedures, which exploit recent nonparametric asymptotic distributional results, are both easy-to-implement and highly accurate in empirically realistic situations. We also illustrate that properly accounting for the measurement errors in the volatility forecast evaluations reported in the existing literature can result in markedly higher estimates for the true degree of return volatility predictability.

Potential impacts of leakage from deep CO2 geosequestration on overlying freshwater aquifers.

Carbon Capture and Storage may use deep saline aquifers for CO(2) sequestration, but small CO(2) leakage could pose a risk to overlying fresh groundwater. We performed laboratory incubations of CO(2) infiltration under oxidizing conditions for >300 days on samples from four freshwater aquifers to 1) understand how CO(2) leakage affects freshwater quality; 2) develop selection criteria for deep sequestration sites based on inorganic metal contamination caused by CO(2) leaks to shallow aquifers; and 3) identify geochemical signatures for early detection criteria. After exposure to CO(2), water pH declines of 1-2 units were apparent in all aquifer samples. CO(2) caused concentrations of the alkali and alkaline earths and manganese, cobalt, nickel, and iron to increase by more than 2 orders of magnitude. Potentially dangerous uranium and barium increased throughout the entire experiment in some samples. Solid-phase metal mobility, carbonate buffering capacity, and redox state in the shallow overlying aquifers influence the impact of CO(2) leakage and should be considered when selecting deep geosequestration sites. Manganese, iron, calcium, and pH could be used as geochemical markers of a CO(2) leak, as their concentrations increase within 2 weeks of exposure to CO(2).

Optimizing the Correction of Memory Errors

People are always at risk of making errors when they attempt to retrieve information from memory. An important question is how to create the optimal learning conditions so that, over time, the correct information is learned and the number of mistakes declines. Feedback is a powerful tool, both for reinforcing new learning and correcting memory errors. In 5 experiments, I sought to understand the best procedures for administering feedback during learning. First, I evaluated the popular recommendation that feedback is most effective when given immediately, and I showed that this recommendation does not always hold when correcting errors made with educational materials in the classroom. Second, I asked whether immediate feedback is more effective in a particular case—when correcting false memories, or strongly-held errors that may be difficult to notice even when the learner is confronted with the feedback message. Third, I examined whether varying levels of learner motivation might help to explain cross-experimental variability in feedback timing effects: Are unmotivated learners less likely to benefit from corrective feedback, especially when it is administered at a delay? Overall, the results revealed that there is no best “one-size-fits-all” recommendation for administering feedback; the optimal procedure depends on various characteristics of learners and their errors. As a package, the data are consistent with the spacing hypothesis of feedback timing, although this theoretical account does not successfully explain all of the data in the larger literature.