Statistics

Volatility modelling is an important task in the financial markets. This paper first evaluates the range-based DCC-CARR model of Chou et al. (2009) in modelling larger systems of assets, vis-à-vis the traditional return-based DCC-GARCH. Extending Colacito, Engle and Ghysels (2011), range-based volatility specifications are then employed in the first-stage of DCC-MIDAS conditional covariance estimation, including the CARR model of Chou et al. (2005). A range-based analog to the GARCH-MIDAS model of Engle, Ghysels and Sohn (2013) is also proposed and tested - which decomposes volatility into short- and long-run components and corrects for microstructure biases inherent to high-frequency price-range data. Estimator forecasts are evaluated and compared in a minimum-variance portfolio allocation experiment following the methodology of Engle and Colacito (2006). Some consistent inferences are drawn from the results, supporting the models proposed here as empirically relevant alternatives. Range-based DCC-MIDAS estimates produce efficiency gains over DCC-CARR which increase with portfolio size.

Author Keywords: asset allocation, DCC MIDAS, dynamic correlations, forecasting, portfolio risk management, volatility

The Air Health Trend Indicator (AHTI) is a joint Health Canada / Environment and Climate Change Canada initiative that seeks to model the Canadian national population health risk due to acute exposure to ambient air pollution. The common model in the field uses averages of local ambient air pollution monitors to produce a population-level exposure proxy variable. This method is applied to ozone, nitrogen dioxide, particulate matter, and other similar air pollutants.

We examine the representative nature of these proxy averages on a large-scale Canadian data set, representing hundreds of monitors and dozens of city-level populations. The careful determination of temporal and spatial correlations between the disparate monitors allows for more precise estimation of population-level exposure, taking inspiration from the land-use regression models commonly used in geography. We conclude this work with an examination of the risk estimation differences between the original, simplistic population exposure metric and our new, revised metric.

Author Keywords: Air Pollution, Population Health Risk, Spatial Process, Spatio-Temporal, Temporal Process, Time Series

Dissolved organic matter (DOM) is a mixture of molecules with dynamic structure and composition that are ubiquitous in aquatic systems. DOM has several important functions in both natural and engineered systems, such as supporting microorganisms, governing the toxicity of metals and other pollutants, and controlling the fate of dissolved carbon. The structure and composition of DOM determine its reactivity, and hence its effectiveness in these ecosystem functions.

While the structure, composition, and reactivity of riverine and marine DOM have been previously investigated, those of allochthonous DOM collected prior to exposure to microbes and sunlight have received scant attention. The following dissertation constitutes the first in-depth study of the structure, composition, and reactivity of allochthonous DOM at its point of origin (i.e. leaf leachates, LLDOM), as detected by measuring its size and optical properties. Concomitantly, novel chemometric methods were developed to interpret size-resolved data obtained using asymmetrical flow field-flow fractionation, including spectral deconvolution and the application of machine learning algorithms such as self-organizing maps to fluorescence data using a dataset of more than 1000 fluorescence excitation-emission matrices.

The size and fluorescence properties of LLDOM are highly distinct. Indeed, LLDOM was correctly classified as one of 13 species/sources with 92.5% accuracy based on its fluorescence composition, and LLDOM was distinguished from riverine DOM sampled from eight different rivers with 98.3% accuracy. Additionally, both fluorescence and size properties were effective conservative tracers of DOC contribution in pH-controlled mixtures of leaf leachates and riverine DOM over two weeks. However, the structure of LLDOM responded differently to pH changes for leaves/needles from different tree species, and for older needles. Structural changes were non-reversible.

Copper-binding strength (log K) differed for the different fluorescent components of DOM in a single allochthonous source by more than an order of magnitude (4.73 compared to 6.11). Biotransformation preferentially removed protein/polyphenol-like fluorescence and altered copper-binding parameters: log K increased from 4.7 to 5.5 for one fluorescent component measured by fluorescence quenching, but decreased from 7.2 to 5.8 for the overall DOM, as measured using voltammetry. The complexing capacity of DOM increased in response to biotransformation for both fluorescent and total DOM. The relationship between fluorescence and size properties was consistent for fresh allochthonous DOM, but differed in aged material.

Since the size and fluorescence properties of LLDOM are strikingly different from those of riverine DOM, deeper investigation into transformative pathways and mixing processes is required to elucidate the contribution of riparian plant species to DOM signatures in rivers.

Author Keywords: Analytical chemistry, Chemometrics, Dissolved organic matter (DOM), Field-flow fractionation, Fluorescence spectroscopy, Parallel factor analysis (PARAFAC)

Depression during the transition into adulthood is a growing mental health concern, with overwhelming evidence linking the developmental risk for depressive symptoms with maternal depression. In addition, there is a lack of research on the protective role of socioemotional competencies in this context. This study examines independent and joint effects of maternal depression and trait emotional intelligence (TEI) on the longitudinal trajectory of depressive symptoms during emerging adulthood. A series of latent growth models was applied to three biennial cycles of data from a nationally representative sample (N=933) from the Canadian National Longitudinal Survey of Children and Youth. We assessed the trajectory of self-reported depressive symptoms from age 20 to 24 years, as well as whether it was moderated by maternal depression at age 10 to 11 and TEI at age 20, separately by gender. The results indicated that mean levels of depression declined during the emerging adulthood in females, but remained relatively stable in males. Maternal depressive symptoms significantly positively predicted depressive symptoms across the entire emerging adulthood in females, but only at age 20-21 for males. In addition, likelihood of developing depressive symptoms was attenuated by higher global TEI in both females and males, and additionally by higher interpersonal skills in males. Our findings suggest that interventions for depressive symptoms in emerging adulthood should consider development of socioemotional competencies.

Author Keywords: Depression, Depressive Symptoms, Emerging Adulthood, Intergenerational Risk, Longitudinal, Trait Emotional Intelligence