We study the differential impact of exchange rate volatility on cost efficiency and market structure when banks have non-trivial exposures to foreign currency operations. First, we document that cost efficiency estimates are both severely downward biased by 30% on average and generally not rank...

We study the differential impact of exchange rate volatility on cost efficiency and market structure when banks have non-trivial exposures to foreign currency operations. We use unique pre-war data on quarterly revaluations of assets and liabilities denominated in foreign currencies that Russian...

Mixed data sampling (MIDAS) regression has received much attention in relation to modeling financial time series due to its flexibility. Previous work has mainly focused on forecasting of realized volatilities and has rarely been used to predict realized correlations. This paper considers a...

Under the Basel II accord, improving probability of default models is a key risk-management priority. There are four main aspects of this research: suggesting the bank default classification; using a wide time horizon (quarterly Russian banking statistics from 1998 to 2011); investigating the...

We propose a new approach based on a generalization of the classic logit model to improve prediction accuracy in US bank failures. We introduce mixed-data sampling (Midas) aggregation to construct financial predictors in a logistic regression. This allows us to relax the limitation of...

Economic time series are available at different frequencies due to their origin and data collection techniques. A mixed data sampling (MIDAS) regression is mainly a forecasting tool designed to harness mixed-frequency data. This dissertation proposes a computationally efficient estimation...