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Uncertainty in Risk Modelling

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thesis
posted on 22.05.2021, 13:38 by Harjas Singh
In this thesis, we explore the uncertainty issues in risk modelling arising from the different approaches proposed in the literature and currently being used in the industry. The first type of methods that we discuss assume that the returns of the stocks follows a generalized hyperbolic distribution. Data is calibrated by the Expectation-Maximization (EM) algorithm in order to estimate the parameters in the underlying distribution. Once we have the parameters, we estimate the Value at Risk (VaR) and Expected Shortfall (ES) by using Monte Carlo simulations. Furthermore, we calibrate data to different copulas, including the Gauss Copula, the

History

Language

eng

Degree

Master of Science

Program

Applied Mathematics

Granting Institution

Ryerson University

LAC Thesis Type

Thesis