QFRG and DSLab open seminar
10 November 2020
We invite you to the fifth seminar of the monthly series of meetings conducted jointly by QFRG (Quantitative Finance Research Group) and DSLab (Data Science Lab). The meeting will be devoted to the topic: Size does matter. A study on the required window size for optimal quality market risk models, in which Mateusz Buczyński (DSLab) will discuss the impact of the training set size on the quality of market risk models and the method of objectively selecting the best one (Joint research with Marcin Chlebus, PhD).
QFRG http://qfrg.wne.uw.edu.pl/ is a place where research is conducted and experiences are exchanged between people engaged in examining occurrences in the world of investment from the perspective of both theory and practice, on the verge of science and business.
The activity of DSLab http://dslab.wne.uw.edu.pl/ is focused mainly on academic projects devoted to deepening of the knowledge of DSLab team, sharing it with other people interested in Data Science issues and preparing scientific and didactic publications.
The fifth meeting will take place on November 16th, 2020 (Monday) at 17:00 online on Google Meet platform.
Link to the meeting: meet.google.com/poh-xzyw-wvt
The presentation is scheduled for about 45 minutes, and after that we invite you to a discussion.
Please log in the latest at 4:50 PM. The presentation will start at 5:00 PM.
Joining a meeting implies consent to recording. Please turn off cameras and microphones during the presentation and send the questions to the speaker in the chat.
When it comes to market risk models, should we use full data that we possess or rather find a sufficient subsample? We have conducted a study of different fixed moving window’s lengths (from 300 to 2000 observations) for three Value-at-Risk models: historical simulation, GARCH and CAViaR model for three different indexes: WIG20, S&P500 and FTSE100. Testing samples contained 250 observations, each ending with the end of years 2015-2019. We have also addressed the subjectivity of choosing the window’s size by testing change points detection algorithms: binary segmentation and Pelt; to find the best matching cut-off point. Results indicate that the size of the training sample greater than 900-1000 observations doesn’t increase the quality of the model, while the lengths lower than such cut-off provide unsatisfactory results and decrease model’s conservatism. Change point detection methods provide more accurate models. Applying the algorithms with every model’s recalculation provides results better by on average 1 exceedance. Our recommendation is to use GARCH or CAViaR model with recalculated window size.
The next meeting is planned on December 14th 2020.
Posted by mbaba