Volatility-Sensitive Forecasting: Deep Learning Model Robustness across Calm and Crisis Periods – study by László Vancsura, Tibor Tatay, and Tibor Bareith in Virtual Economics journal
Volatility-Sensitive Forecasting: Deep Learning Model Robustness across Calm and Crisis Periods Laszlo Vancsura Tibor Tatay Tibor Bareith Virtual Economics – Vol. 9 No. 1 (2026) – Published: 2026-03-31 Abstract This study investigates how the forecasting performance of deep learning models is affected by changing market conditions, with particular emphasis on periods of differing […]
Two decades of earnings inequality in Hungary – by Rita Pető, István Boza and Martin Neubrandt
Two decades of earnings inequality in Hungary A new study by István Boza, Martin Neubrandt, and Rita Pető uses Hungarian administrative microdata harmonized through the Global Repository of Income Dynamics (GRID) framework to examine how much has earnings inequality changed in Hungary since the mid-2000s. Covering 2004 to 2021, the paper finds that aggregate […]
