Bill Huajian Yang
Royal Bank of Canada
Bill Huajian Yang, Ph. D (USA) and Britton postdoc fellow (Canada) in mathematics, currently a quantitative leader with Royal Bank of Canada, with focus on machine learning algorithms, automations, risk-supervised clustering, probability of default term structure, and loss given default term structure, CCAR stress testing, IFRS9 expected credit loss, and AIRB regulatory capital estimation. His early researches focused on algebraic topology and stable homotopic calculations, his thesis “The stable homotopy types of stunted lens spaces mod 4” was published in Transaction American Mathematical Society in 1998. He started working for the financial industry in 2001. His latest publication is “resolutions to flip-over credit risk and beyond” by Big Data and Information Analytics (March 2019).
International Financial Reporting Standard 9 expected credit loss estimation: advanced models for estimating portfolio loss and weighting scenario losses
In this paper, the authors propose a model to estimate the expected portfolio losses brought about by recession risk and a quantitative approach to determine the scenario weights.
Smoothing algorithms by constrained maximum likelihood: methodologies and implementations for Comprehensive Capital Analysis and Review stress testing and International Financial Reporting Standard 9 expected credit loss estimation
In this paper, the author proposes smoothing algorithms that are based on constrained maximum likelihood for rating-level PD and for rating migration probability.
Forward ordinal probability models for point-in-time probability of default term structure: methodologies and implementations for IFRS 9 expected credit loss estimation and CCAR stress testing
This paper proposes an ordinal model based on forward ordinal probabilities for rank outcomes.