Volatility Estimation via Machine Learning for Market Risk Assessment

Student: Pastukhov Semen

Supervisor: Victor A Lapshin

Educational Programme: Applied Economics (Master)

Final Grade: 7

Year of Graduation: 2021

The article tests how well a model based on machine learning methods can estimate the conditional volatility used to further calculate the VAR risk measure. Models based on the GARCH approach and machine learning methods are considered. The essence of the test will be to compare the estimates of the VAR risk measure obtained using the GARCH model and the estimates obtained using the neural network model, as well as to determine the feasibility of its use. As a result of the conducted research, the expediency of using a rather complex model based on machine learning methods was confirmed. The conditional volatility estimated with it does allow for the best estimates of the VAR risk measure, among other things.

Full text (added May 10, 2021)

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