A tool for quantitative risk analysis of Android applications based on machine learning techniques
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Updated
Nov 25, 2024 - Python
A tool for quantitative risk analysis of Android applications based on machine learning techniques
This project studies the effects of the shape parameter estimator uncertainty at different threshold levels on the value-at-risk confidence interval for quantitative risk management (QRM) using the Generalized Pareto Distribution (GPD) from the Extreme Value Theory (EVT) approach.
Practical, hands-on risk modeling, risk assessment and verifications of risk models across major risk classes and understanding risk regulation as well. Implementing risk models in Python, R and Excel.
Python implementation of the rearrangement algorithm to find bounds of functions of dependent risks, e.g., best- and worst-case value-at-risk (VaR).
Replicating Hull & White (2017) paper on minimum-variance delta hedging
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