Publications

The adoption of the positive neutral countercyclical capital buffer (PN CCyB) has increased across jurisdictions, yet quantitative calibration methodologies vary considerably and typically address either the PN CCyB or the overall CCyB separately. This paper proposes a unified calibration approach that addresses both using a macroprudential stress testing model. The approach simulates capital depletion under macroeconomic scenarios that feature different degrees of cyclical risk. By comparing capital depletion across these scenarios, it isolates incremental capital erosion attributable to cyclical developments. Applied to the Lithuanian banking sector across 23 sub-sample periods covering 2018Q4–2024Q2, the calibration indicates a PN CCyB in the range of
0.75–1.25 percent and an overall CCyB around 2 percent under elevated cyclical risk. The results are robust across alternative balance sheet dynamics and scenario severity assumptions.

Keywords: Countercyclical Capital Buffer, Macroprudential Policy, Stress Testing, Bank Capital, Systemic Risk.

JEL codes: E58, G21, G28.

This paper introduces the Systemic Risk Modelling System (SRMS), a new macroprudential stress testing model for the Lithuanian banking sector. The SRMS addresses the limitations of traditional static models by incorporating dynamic balance sheet assumptions and capturing second-round effects, providing a more comprehensive assessment of systemic risks. The model’s applications extend beyond stress testing, including macroprudential policy stance assessment, capital-at-risk analysis, and macroprudential policy impact evaluation. The SRMS model enhances the understanding of systemic risks within the Lithuanian banking sector and offers a potential benchmark for other national central banks seeking to strengthen their financial stability frameworks.

Keywords: macroprudential stress testing, macroprudential policy, feedback loop, secondround effects

JEL codes: E37, E58, G21, G28

While Euro area interest rates were responding to accommodative monetary policy and decreasing throughout 2015-19, in stark contrast, Lithuania’s bank lending rates increased. Although the rates have slightly dropped around the onset of the pandemic, they are still elevated and well above the EA figures. This paper calls into question, what were the drivers of such interest rate dynamics in Lithuania? By analysing the historical events and practical aspects of loan pricing in Lithuania’s banking industry, we build an empirical model that exploits lending rate variation across banks, time and lending segments, and maps it to different drivers of pricing. We find that the recent changes in lending rates can be attributed to average bank margins, which moved largely in response to changes in market concentration.

Keywords: interest rates, loan pricing, banking, concentration, capital requirements.

JEL Codes: D22, D40, E43, G21, L11.

The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.

During the Covid-19 pandemic, house prices and mortgage credit are growing at a long-unseen pace. However, it is unclear, whether such growth is warranted by the underlying market and macroeconomic fundamentals. This paper offers a new structural two-market disequilibrium model that can be estimated using full-information methods, and applied to analyse housing and credit dynamics. Dealing with econometric specification uncertainty, we estimate a large ensemble of the two-market disequilibrium model specifications for Lithuanian monthly data. Using the model estimates, we identify the historical drivers of Lithuania’s housing and credit demand and supply, as well as price and market quantity variables. The paper provides a novel approach in the financial stability literature to jointly measure house price overvaluation and mortgage credit flow gaps. We find that by mid-2021 Lithuania was experiencing a heating in housing and mortgage credit markets, with home prices overvalued by around 16% and the volume of mortgage credit flow being 20% above its fundamentals.

JEL Codes: C34, D50, E44, E51, G21.

The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.