PhD Oral Exam - Anthony Forgetta, Mathematics and Statistics

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

This dissertation is composed of three manuscripts that address problems in energy economics, finance, and epidemiology through statistical modeling.

The first manuscript proposes a covariate-dependent mixture model to describe the behavior of electricity DART spreads, which are differentials between the day-ahead and real-time prices of electricity. The model incorporates multiple regimes and allows covariates to impact both the frequency and severity of DART spread spikes. Using data from the Long Island zone of the New York Independent System Operator, the model demonstrates a strong fit. Results reveal that including covariates in the severity component of the model is crucial, while mild additional performance is obtained with their inclusion in the frequency component. Neural network-based quantile regression benchmarks are unable to improve performance over our mixture model.

The second manuscript examines the diversification benefits of energy commodities during turbulent periods marked by the COVID-19 pandemic and the Russia-Ukraine war, both of which deeply affected energy markets. Revisiting classical allocation strategies, we incorporate electricity futures—a rarely used asset—alongside crude oil and natural gas futures. Using mean-variance optimization, the diversification benefits are evaluated by combining these energy contracts with the S&P 500. Our empirical approach handles the non-stationarity of returns, volatilities, and correlations. Out-of-sample results show improved performance and diversification, especially during crisis periods.

 manuscript extends existing dual-energy X-ray absorptiometry-based body composition classifications by introducing additional centile cut-offs to capture tail behavior. Using NHANES data, we study the association between these phenotypes and health risks, including metabolic syndrome, depression, sleep disorders, and comorbidities. Nine phenotypes were identified using quantile regression (QR), and logistic regression was used to assess their relationship with health risks, compared to standard adiposity measures like body mass index (BMI), waist circumference (WC), and total fat percent. The QR model has a better (higher) LR+ than the median-split model for MetS and comorbidity but consistently underperforms in LR- compared to the median-split model. Both models perform worse than BMI and WC. Whether the classification performances diverge in longitudinal studies should be investigated.