Mateusz Buczyński graduated from the Faculty of Economic Sciences at University of Warsaw in 2019. He specializes in data science and machine learning and is eager to prove the relevance of these methods in the area of risk management. His research is mostly focused on the validation of market risk models, but also the use of machine learning to price prediction and NLP usage in financial modeling. He is also one of the finalists of Econometric Game of 2019.
The importance of window size: a study on the required window size for optimal-quality market risk models
In this paper the authors study different moving-window lengths for value-at-risk evaluation, and also address subjectivity in choosing the window size by testing change point detection algorithms.
Old-fashioned parametric models are still the best: a comparison of value-at-risk approaches in several volatility states
The authors present backtesting results for 1% and 2.5% VaR of six indexes from emerging and developed countries using several of the best-known VaR models, including generalized autoregressive conditional heteroscedasticity (GARCH), extreme value theory…