Quantifying Model Risk using Neural Networks while Calculating Market Risk

Student: Kagan Elizaveta

Educational Programme: Financial Technology and Data Analysis (Master)

Year of Graduation: 2024

This paper examines and highlights existing measures for assessing market risk and methods for their calculation. The dependence of the results on the size of the training sample and the frequency of recalibration of the distribution parameters is investigated. Particular attention is paid to approaches to the quantitative measurement of model risk. The work provides a list of possible model risk indicators. Further, using these indicators, classical risk forecasting methods are compared with a neural network using empirical data as an example. The result of the work is a set of programs for analyzing financial instruments, calculating the Value-at-Risk market risk measure in various ways and conducting testing taking into account parameters, as well as evaluating model risk.

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