Energy companies are in the business of turning energy from one form into another. For example, a gas-fired power station turns chemical potential energy stored in the natural gas into electrical energy. A natural gas storage facility allows energy (held in the form of natural gas) to be stored at one point in time and recovered at a later time. A gas pipeline moves energy from one location to another. The result is that the financial risks faced by an energy company involve a large portfolio of spreads â differences between energy prices.
This project concentrates on the scenario optimization method which does not need to make any assumption for the underlying asset distribution and directly incorporate such uncertainty into the objective or constraint functions through stochastic programming. The scenario optimization is performed under different parameters and constraints while Markowitz and Black-Litterman model are taken as the benchmarks to evaluate if the scenario optimization can outperform the traditional methods with the same input exchange-traded funds (ETF) data.
The QWeMA division of CANNEX develops solutions for the financial and insurance industry of North America. Our analytics play an important role in determining the value proposition of investment products. Our solutions help the financial community and public through their financial advisors to be able to make informed decisions.
We work at the intersection of finance, mathematics, actuarial science, and computer science. Our solution strategies require us to solve complex mathematical and optimization problems in a finite amount of time.
Liquefaction is a destructive phenomenon which usually takes place after an earthquake in areas with water-saturated soil or sand. During the liquefaction process, soil loses its strength and can no longer support structures and buildings which often leads to their destruction. To prevent damages associated with liquefaction, it is critical to study this phenomenon and understand its underlying mechanisms. One approach to study liquefaction is through computer simulation using the discrete element method.
This project aims to further develop cost-effective methods for characterizing fluid flow fields in high-energy tidal channels, with a focus on use of low-profile drifters to calibrate and validate numerical models of ocean flows. The project will focus on the Finite-Volume Community Ocean Model (FVCOM) used by Acadia and Luna Ocean, primarily for tidal energy site assessment in the Bay of Fundy. The use of measurements gathered by various types of drifters provides a cost effective method for mapping flow fields, resolving spatial and short-term temporal variation in tidal flows.
The University of Alberta proposes to hire an industrial postdoctoral fellow funded through the Mitacs Accelerate program to develop enhanced constraint equation solution methods and 3D graphical authoring tools in partnership with a local company in Edmonton, Alberta. The field of application is educational web software for creating randomized scaled mathematical drawings, delivered in an interactive browser environment.
Insurance companies heavily fund marketing campaigns such as, for instance, customer retention or cross-sell initiatives. Uplift modeling aims at predicting the causal effect of an action such as medical treatment or a marketing campaign on a particular individual by taking into consideration the response to an action. Typically, the result of an uplift model is used to call customers for marketing some products based on important attributes of a customer.
The internship will consist of studying, building, implementing and testing so called factors that are used to characterize the equities, commodities and currencies that the company invests in. These factors can be thought of as characteristics relating a group of securities that is important in explaining their returns and risk. My task will be first to understand the risk factors that are of particular importance to the company’s investment strategy.
Developing smart technology determines the future economy of societies nowadays. Electronic nose is a device that audits the chemicals and transforms it to human odor perception. One of the most challenging steps to transform electronic nose to smart nose is its patter recognition machinery, because electronic nose data are imprecise and noisy. This pattern recognition machinery builds an empirical statistical model using machine learning algorithms over electronic nose data, to transform the these data to human odour perception.
Radars are being used more and more in critical sites such as airports, military bases and borders for surveillance of huge areas to detect unwanted intrusions. Determination of the type of each target is essential for such systems to identify the nature of the intrusion and avoid false and nuisance alarms. This thesis is focused on the design of automatic target classification systems based on analysis of real radar data from different sites and environments.