Model-free Risk Measure Estimates

Student: Sim Veronika

Supervisor: Nickolay Anatoljevich Andreev

Educational Programme: Economics (Bachelor)

Year of Graduation: 2021

This work is devoted to the problem of refining the model-free risk measure estimates. It is considered the popular risk measure Conditional Value-at-Risk, in another words it is also called the Expected Shortfall. This measure is estimated by using confidence intervals. All work is written in the Python programming language in Jupyter Notebook using machine learning elements. By following the work, it was revealed that the bounded random variable influenced on the upper bound of concentration inequalities. This allows us to obtain a smaller value of the confidence interval of estimated CVaR, which makes the assessment of the risk measure is more accurate. The obtained results can be used in studies on the assessment of regulatory capital for risk planning or for optimal management of an asset portfolio.

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