View by Day

Master Thesis Chair:
Ctirad Slavík

Abstract:

I develop a novel method for computing globally accurate solutions to recursive macro-epidemic models featuring aggregate uncertainty and a potentially large number of state variables. Compared to the previous literature which either restricts attention to perfect-foresight economies amendable to sequence-space representation or focuses on highly simplifed, low dimensional models that could can be analyzed using standard dynamic programming and linear projection techniques, I develop a deep learning-based algorithm that can handle rich environments featuring both aggregate uncertainty and large numbers of state variables. In addition to solving for particular model equilibria, I show how the deep learning method could be extended to solve for a whole set of models, indexed by the parameters of government policy. By pre-computing the whole equilibrium set, my deep learning method greatly simplifes computation of optimal policies, since it bypasses the need to re-solve the model for many different values of policy parameters.

Full Text: “Macro-Epidemic Modelling: A Deep Learning Approach”

Master Thesis Chair:
Stanislav Anatolyev

Abstract:

 The connectedness of financial assets and markets represents an essential concept that has long-lasting consequences for the assessment of risk. Thus, it is important to correctly measure dependencies and describe their dynamics to predict future responses of markets to shocks. In this thesis, I focus on the connectedness of Eastern European stock markets and assess the relationships between returns and volatilities in these markets, accounting for the presence of cryptocurrency markets and other major developed markets. I describe conditional correlations of returns from the DCC model of Engle (2002, JBES). Using the spillover framework proposed by Diebold and Yılmaz (2009, EJ) I measure the connectedness from a static and dynamic perspective. The results indicate that Eastern European markets are tightly connected. The measures of connectedness were fluctuating over time and have risen significantly as a consequence of the recent pandemic. The magnitude of the increase for different groups of markets ranges from 35% to 100%. 

Let us invite you to the theses defenses of our MA in Economic Research students which are going to take place in room #6.

Defense Committee:

Michal Kejak, Krešimir Žigić, Byeongju Jeong

Student: Jan Žemlička
Title of Thesis: Macro-Epidemic Modelling: A Deep Learning Approach

Student: Arseniy Shcherbov
Title of Thesis: Financial Connectedness of Easters European Stock Market