Neil A. Wilmot

Jump processes in the market for crude oil

In many commodity markets, the arrival of new information leads to unexpectedly rapid changes--or jumps--in commodity prices. Such arrivals suggest the assumption that log-return relatives are normally distributed may not hold. Combined with time-varying volatility, the possibility of jumps offers a potential explanation for fat tails in oil price returns. This article investigates the potential presence of jumps and time-varying volatility in the spot price of crude oil and in futures prices. The investigation is carried out over three data frequencies (Monthly, Weekly, Daily), which allows for an investigation of temporal properties. Employing likelihood ratio tests to compare among four stochastic data-generating processes, we find that that allowing for both jumps and time-varying volatility improves the models ability to explain spot prices at each level of temporal aggregation; this combination also provides a statistically compelling improvement in model fit for futures prices at the Daily and Weekly level. At the monthly level, allowing for jumps does not provide a statistically significant increase in model fit after incorporating time-varying volatility into the model.

Investigating the temporal relationship between individual-level social capital and health in fragile families.

BackgroundThe potential for social capital to influence health outcomes has received significant attention, yet few studies have assessed the temporal ordering between the two. Even less attention has been paid to more vulnerable populations, such as low-income women with children. Our objective was to explore how different dimensions of social capital impact future health status among this population.MethodsThis study uses data from the Fragile Families and Child Well-Being (FFCWB) Study, which has followed a cohort of children and their families born in large U.S. cities between 1998 and 2000 to mostly minority, unmarried parents who tend to be at greater risk for falling into poverty. Four separate measures of social capital were constructed, which include measures of social support and trust, social participation, perceptions of neighborhood social cohesion, and perceptions of neighborhood social control. The temporal effect of social capital on self-reported health (SRH) is investigated using logistic regression and we hypothesize that higher levels of social capital are associated with higher levels of self-rated health.ResultsAfter controlling for socioeconomic and demographic factors related to social capital and self-rated health, social support and trust, perceptions of neighborhood social cohesion and control at an earlier point in time were positively associated with higher levels of health four-years later. Social participation was not related to increased health. The empirical results appear robust.ConclusionHigher levels of social capital are predictive of improved health over a four-year time frame. These results suggest that policy initiatives supporting increasing the social capital available and accessible by low-income, urban, minority women are viable for improving health. Such policies may have the potential to reduce socioeconomic health disparities.

An empirical study of the influences of recreational park visitation: the case of US National Park Service sites

The authors examine which variables influence recreational visitation at 353 US National Park Service (NPS) sites. The paper provides evidence that variables other than lagged visitation, including various site designators, various regional dummies, lagged RDPI (inferior), alternative sites (complementary effect), the regional 9/11 terrorist attacks variable and the regional terrorism threat level variable (increase in threat level decreases visitation) are statistically and economically significant. Generally, the statistical significance and magnitude of the variables influence increases as site visitation increases. These results should be of interest to planners and policy makers in general, and specifically to NPS planners in charge of a US

The Asymmetric Effects of Oil Price Shocks on the Canadian Economy

2.2 billion budget.

Examination of the influence of social capital on depression in fragile families

A threshold vector autoregression (TVAR) is estimated to study the effects of oil price shocks on Canadian output and price level. While much of the literature has investigated potential asymmetric effects of positive and negative oil price shocks within a linear vector autoregression (VAR), we do so within a nonlinear VAR. Further, we extend the analysis to consider the correlation between asymmetries associated with the business cycle phase and size/sign asymmetries. Positive oil price shocks are found to have a stronger effect on output than negative oil price shocks. This asymmetry is significant in recessions, but lessened during expansions. The results also suggest that the reduction in inflation due to a negative oil price shock is larger than the increase in inflation following a positive oil price shock, especially during periods of low output growth. Yet, neither inflation nor output growth seems to vary disproportionately with the size of the oil price shock. In general, the results are robust to the ordering of the variables in the VAR process and to the time window over which the net oil price change is computed

A longitudinal examination of social capital as a predictor of depression

Background While it appears that social capital has a positive effect on mental health, most studies have been cross-sectional in nature and/or employ weak measures of social capital or mental health. Even less attention has been paid to vulnerable populations, such as low-income women with children. Thus, our objective was to explore how different dimensions of social capital impact depression in this population. Methods We used data from the Fragile Families and Child Wellbeing Study, which has followed a cohort of children born in large US cities to mostly minority, unmarried parents for over 9u2005years. These families tend to be at greater risk for falling into poverty. Four separate measures of social capital were constructed, using measures that are reliable and that offer evidence of validity including social support and trust, social participation, perceptions of neighbourhood social cohesion and perceptions of neighbourhood social control. The temporal effect of social capital on mental health, as measured by a standardised screening for depression was investigated using logistic regression. Results After controlling for relevant socioeconomic and demographic factors, prior depression, and prior self-rated health, the social capital dimensions of social support and trust and perceived neighbourhood social cohesion are significant predictors of depression. Conclusions These results suggest that social and neighbourhood environments play an important role in mental health status. Intervention and policy initiatives that increase social capital may be viable for improving mental health among low-income urban, minority women.

Discontinuities in the coal market

ABSTRACT Depression is the world’s most common mental health disorder and its prevalence is higher among women and the poor. This article seeks to examine the effect that social capital has on mental health, in a longitudinal study. We used data from the Fragile Families and Child Wellbeing Study, which has followed a cohort of families born in large US cities to mostly minority, unmarried parents for over 15 years. Two measures of social capital are constructed, an index of social support and trust, and a measure of social participation. Data from four waves (totalling 8 years) of the study are analyzed in a panel logit model. After controlling for socioeconomic and demographic factors, the measure of social support and trust is found to be a significant predictor of depression, and remains so even after controlling for the effect of social participation. Our measure of social participation, based on attendance at religious services, does not appear to be a significant predictor of depression. Intervention and policy initiatives that increase social capital, particularly social support and trust, may be viable for improving depression among low-income urban, minority women.

Forecasting Recreational Visitation at US National Parks

ABSTRACT Coal has been an important source of energy in the USA for centuries. Coal prices can be quite uncertain and highly volatile, often experiencing large changes. Understanding the data-generating process of coal prices would seem critical, both from a market perspective and from a policy perspective. This study investigates the appropriate stochastic process underlying coal prices. Commonly assumed processes, such as geometric Brownian motion fail to properly account for the arrival of unanticipated information which inflicts rapid changes – or jumps – in energy markets. Such discontinuities can manifest ‘fat tails’ in the distribution of returns. To investigate the possibility of time-varying volatility, generalized autoregressive conditional heteroscedastic models are also incorporated into the analysis. We find compelling empirical evidence that discontinuities must not be ignored, with US coal prices experiencing jumps every few days. The result has implications for the potential closure of coal-fired plants in response to cheaper alternatives or climate-based regulations.

Jump Processes in Natural Gas Markets

This study evaluates forecasting accuracy among several competing methodologies, including time series and econometric methods, on visitation to 255 US National Park Service (NPS) sites. The performance of these models is contrasted with the model currently in use by the NPS. One-yearahead, 2-year-ahead, and combined (1- and 2-year-ahead) forecasting performance at the individual park level is examined utilizing several measures of forecasting accuracy, including root mean square error (RMSE) and mean absolute percentage error (MAPE). Results indicate incorporating economic variables can significantly improve forecasts, particularly for large and small parks. For medium size parks the naive forecast errors were typically lowest. Furthermore, the naive model performed well, often producing the best forecast, followed by the econometric model. Regionally, the naive and econometric models preform best, with the Pacificwest region being the exception. Utilizing the most accurate model for each park leads to a 24% improvement over current forecasts (1-year horizon) and suggests that a mixed model approach is optimal.