This project will look at improving the quality of software by using AI to determine if a defect / issue exists and if so where it exists for easier fixes. This research is innovative and this domain is not proven. The student will explore new techniques for a highly relevant issue in industry. The industry partner will gain insight and knowledge into how improvements can be made ultimately resulting in faster time to market and potential cost savings.
As part of reforms to the regulations governing Canadian banks, new rules governing the capital to be set aside for market risk have been proposed, termed the Fundamental Review of the Trading Book (FRTB). With the new rules, some Canadian banks will move to calculating capital requirements using a regulatory Standardized Approach. The goal of the research is to analyze the drivers of the FRTB capital charges and contrast these against the drivers of current regulatory capital, and both against theoretical ideals for economic capital requirements.
Applying advances in ambient intelligence technologies, this research aims to design and develop a smart workplace to optimize not only physical comfort in employees but also participant happiness. Through ubiquitous monitoring of ambient factors and affective states a number of important research questions associated with designing and developing a smart workplace will be tackled.
Trust lies at the very foundations of computer and information security, and is the basis for real-world schemes that require security properties, such as those that underlie consumer banking. Under this research project, we will investigate models for delegation of trust that meet desirable properties, for example, that guarantee that no security compromises occur unless certain trust assumptions are violated.
A fundamental activity of a commercial bank is the lending of capital through various credit instruments such as direct loans to consumers, mortgages, and lines of credit. Efficient lending is at the core of a well functioning economy. The risk in lending is commonly referred to as credit risk and represents the risk of loss due to a borrower's failure to make payments on debt. It is crucial for a bank to be able to estimate risk of loss from loans as large unexpected losses can result in insolvency or increased costs of borrowing for the public.
Over the past two decades, commodities have become mainstream financial instruments. With a flux of wealth invested in commodities markets, commodities price levels have increased beyond expectations. Moreover, the commodities markets have become more volatile than ever. Understanding the nature of price changes and market movements is essential to project and investment valuations. In this project, we aim at developing a structural storage model for commodities prices. This model will be based on supply/demand and inventory levels of commodities.
The proposed project addresses the main challenge in modeling long-dated (maturities of 30 years or more) foreign exchange (FX) interest rate (IR) hybrid derivatives, namely the strong sensitivity of the products to the skew of the FX volatility smiles via the use of a stochastic process, such as the Heston model. Numerical methods based on a partial differential equation (PDE) approach will be developed for the pricing of these derivatives. The expected benefits of the project to the industrial partner are (i) flexible modeling frameworks for long-dated FX-IR, which can be easily modified
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