This study analyzes the dynamics of the labor market in Ukraine during the full-scale russian invasion, emphasizing the need to account for profound structural changes in this market. We derive estimates of labor force participation and unemployment rates using survey data collected by Info Sapiens for the National Bank of Ukraine and controlling for sample composition. Participation fell by about nine percentage points between 2022 and 2025, while the unemployment rate peaked at 25 percent in May 2022 and gradually declined to 9 percent by December 2024. We corrected possible biases by including potentially omitted variables in our regressions. Regional and demographic disparities were pronounced during the war: Kyiv maintained lower unemployment, the east of the country was hardest hit, and the urban - rural gap narrowed after 2023. The gender gap in the labor market decreased but remained significant during the war. Education proved decisive for job retention, with university and vocational training significantly improving labor participation and employment outcomes. Our findings provide robust evidence of the adjustment of the labor market under extreme conditions and offer essential policy lessons for sustaining employment during the ongoing war.

This paper explores how European sovereign bond yields have responded to policy news about russian state assets (their immobilization and subsequent decisions about their use). The results obtained are used to analyze the potential reactions by EU markets if full confiscation of these assets is pursued. We assemble a dated news sample validated with investor attention and estimate announcement effects on 2-, 10-, and 30-year bond yields (country-specific systemic risk proxy) using multi-window event studies, two-way bootstrap confidence bands, and a placebo (Monte-Carlo) design that draws pseudo-events from non-event periods. To assess the persistence of the shock, we extend inference to 60 trading days (with principal component analysis used to extract a common EU factor and a Bayesian-inspired bootstrap). There are three key results. First, markets did react to the news about asset usage, but effects were modest and faded within days to weeks; immobilization triggered a relatively modest response. Second, yield shocks from news about the use of russian assets were broadly comparable to routine policy or news on credit risk downgrades – well below past EU crisis episodes. Third, on the disaggregated country-level, 10-year yields increased consistently in response to the usage news shock, although the magnitudes of these increases varied.

Concluding remarks by Serhiy Nikolaichuk, First Deputy Governor of the National Bank of Ukraine, at the online workshop Monetary Policy in Emerging Markets: Evolution of Inflation Targeting, organized by the National Bank of Ukraine, Kyiv, 27 November 2025.

Welcome speech by Andriy Pyshnyy, Governor of the National Bank of Ukraine, at the online workshop Monetary Policy in Emerging Markets: Evolution of Inflation Targeting, organized by the National Bank of Ukraine, Kyiv, 27 November 2025.

The article summarizes the key ideas and takeaways from the 9th Annual Research Conference, jointly organized by the National Bank of Ukraine and Narodowy Bank Polski, titled Economic and Financial Integration in a Stormy and Fragmenting World. The event took place on 19–20 June in Kyiv. Strategic guidelines for central banks in the new geopolitical and economic environment were outlined at the event, based on presentations by keynote speakers, panel discussions, and research presentations. Special attention was paid to the role of economic research in enhancing the institutional capacity of central banks and strengthening their resilience and ability to respond in a timely and effective manner to the unprecedented challenges of the global environment.

In this study, we apply ARDL models to estimate the strength of long-run interest rate pass-through in Ukraine. We focus on the transmission of the overnight interbank interest rate to the rates on term deposits of households and loans to non-financial corporations – both in national currency. Controlling for macroeconomic indicators and bank financial variables we obtain bank-level time-varying estimates of transmission and run a set of panel regressions to analyze the determinants of pass-through strength. Besides linear estimates, we report asymmetric transmissions, which differ depending on the decrease or increase in the interbank rate, and timevarying estimates for transmission.

This study explores the impact of central bank communications on FX market dynamics. Our main results suggest that the NBU’s statements and press releases on monetary policy issues do indeed matter. We find that exchange rate movements and volatility are negatively correlated with the volumes of publications by the NBU on its official website. However, this effect is noticeably larger for volatility than for exchange rate changes. The impact of communications on FX developments is strongest a week after a news release, and it persists further. Furthermore, these indicators turn out to be more sensitive to monetary policy announcements than NBU updates overall.

The study examines the problem of modeling and forecasting the price dynamics of crypto currencies. We use machine learning techniques to forecast the price of crypto currencies. The FB Prophet time series model and the LSTM recurrent neural network were selected to implement the study. Using the example of data from Binance (the most popular exchange in Ukraine) for the period from 06.07.2020 to 01.04.2023, prices for Bitcoin, Ethereum, Ripple, and Dogecoin were modeled and forecasted. The recurrent neural network of long-term memory showed significantly better results in forecasting according to the RMSE, MAE, and MAPE criteria, compared to the Naïve model, the traditional ARIMA model, and the FB Prophet results.

This study investigates the determinants of corporate lending in Ukraine, with a focus on distinguishing between supply and demand factors. It uses a two-step process to build a credit standards index (CSI) based on disaggregated data from a Ukrainian bank lending survey (BLS). This paper describes the factors that are significant for corporate lending development in Ukraine. It contributes to the existing literature by developing a measure of corporate loan supply and analyzing its ability to explain corporate credit growth in Ukraine by using bank-level BLS data. First, a panel ordered logit model is used to transform categorical data into a continuous index that measures the likelihood of credit standard tightening. Second, the study examines how this index affects new corporate lending in both national and foreign currencies. It is found that the credit standard index is influenced by exchange rate movements (with depreciations leading to tighter standards), bank liquidity, and bank competition. It is also demonstrated that the CSI has a negative impact on corporate loans in national currency, with a more pronounced effect on smaller banks.