Risky Mortgages and Macroprudential Policy: A Calibrated DSGE Model for Lithuania

Abstract

Following the financial crisis of 2009 there was an emergence of macroprudential policy tools, as well as a need to model the macroeconomy and the financial sector in a coherent framework. This paper develops and calibrates a small open economy DSGE model for Lithuania to shed some light on the interactions between the macroeconomy and the banking sector, regulated by macroprudential policy. The model features housing market, and endogenous credit risk a la de Walque et al. (2010), whereby the household can default on mortgage repayments, what leads to housing collateral seizure. Foreign-owned banks, that are subject to risk-sensitive macroprudential capital requirements, take into account not only the mortgage default rate but also the cap on loan to value (LTV) ratio when making lending decisions. Using this mechanism, we show that while a more stringent LTV constraint reduced credit demand, it can also lead to an expansion in credit supply via lower credit risk. Therefore, a tightening of LTV requirement should result in only a slight reduction in mortgage lending, coupled with lower interest rate margins. The article compares the impact of the tightening of three macroprudential tools, namely, bank capital requirements, mortgage risk weights and LTV limit. We find that broad-based capital requirements, such as the counter-cyclical capital buffer, are less efficient in leaning against the housing credit cycle, because of a relatively large cost incurred on the firm sector.

Micro-Assessment of Macroprudential Borrower-Based Measures in Lithuania

“…Macroprudential policy literature that focuses on the assessment or calibration of BBMs maintains at least four distinct approaches. The first is based on DSGE modeling as the most structural and least data-driven method, which includes the previously mentioned examples of Lambertini and others (2013), Rubio and Carrasco-Gallego (2014), Ferrero and others (2022), etc., and models that explicitly include mortgage default in Darracq Pariès and others (2011), Forlati and Lambertini (2011), Clerc and others (2015), Nookhwun and Tsomocos (2017), or Karmelavičius (2021) for Lithuania. While DSGE models can serve as a sandbox for experimenting with all kinds of policy rules and options in a general equilibrium setting, they lack the accuracy that is necessary for calibration, and thus are the least practical of all approaches.…”

“…Using a dynamic stochastic general equilibrium (DSGE) model that is calibrated to Lithuanian data, Karmelavičius (2021) finds that a tightening of the LTV requirement may act countercyclically by lowering both credit and house price growth and may increase resilience by reducing the mortgage delinquency rate. Specifically, a 1 p.p.…”

“…For references on Lithuania's experience and accumulation of imbalances preceding the 2009 crisis, seeRamanauskas (2005),Kulikauskas (2016),Karmelavičius and others (2022, 2023), andCevik and Naik (2023). For coverage of the crisis period, refer toKuodis and Ramanauskas (2009) andRamanauskas (2011), and more recently toBaudino and others (2022), who analyze the events in the Baltic countries.2 Note that throughout this paper, we use the terms mortgage, mortgage loan, housing loan, and simply loan interchangeably and thereby refer to credit that is secured by residential real estate unless specified otherwise.3 A mortgage loan is "secondary" if during its inception, the household has at least one other active housing loan.4©International Monetary Fund.…”