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Session Title: "Cryptocurrencies Quantile and Tail Expectation Forecasting"
Speaker: Kokulo Kpai Lawuobahsumo, PhD Student, University of Calabria
Watch the Session: Kokulo Kpai Lawuobahsumo.mp4 - TechSmith Knowmia

Session description: This presentation focuses on forecasting the conditional quantile and tail expectation of a group of cryptocurrencies using the Monotone Composite Quantile Regression Neural Network (MCQRNN) model. We use macroeconomic and financial variables as predictors. Using the Model Comparison Set approach, we further compare our results to GARCH models as a benchmark in order to assess which model presents the best predictive performance.

Bio:  PhD Student in Economics and Business Science at the University of Calabria (Italy). My PhD research focuses on market and credit risk modelling. Master of Science Degree in Finance and Insurance from the University of Calabria (Italy). Bachelor of Public Administration from the United Methodist University (Liberia). Research Interest: Financial risk Modelling, Behavior influence on economic choices and Quantitative risk management.