Methodological and Empirical Investigations in Quantification of Modern Operational Risk Management
Date
2016
Authors
Guharay, Sabyasachi
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Abstract
Establishing robust quantitative metrics which allow decision makers to determine the amount of risk in a system with extreme loss events is a problem of interest in many scientific fields. One of the fundamental metrics which is universally accepted in all fields of risk management is the quantity known as Value-at-Risk (VaR). Both academic researchers and industry practitioners are currently looking at ways to make this estimate more statistically robust and accurate with minimal assumption requirements. In particular, modern Operational Risk Management (ORM), a subfield of risk management, closely investigates methodologies to robustly estimate VaR. With this brief background in mind, this dissertation investigates two fundamental components of modern ORM: (1) Statistically modeling severity (magnitude) of losses and estimating corresponding Aggregate Loss distribution; (2) Robust Estimation of Value-at-Risk. One of the key problems in the modeling of loss severity is that there is no currently known flexible severity loss distribution which can generalize to fit any type of severity data.
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Operations research, Systems science