Kreditavimą lemiantys veiksniai ir jo sąryšiai su ekonominiais procesais

LTStraipsnyje pateikiama empirinė Lietuvos kredito rinkos procesų analizė. Sudaromi keturi vektoriniai paklaidų koregavimo modeliai, leidžiantys įvertinti ilgojo laikotarpio kredito ir makroekonominių rodiklių sąryšius, taip pat nepusiausvyros modelis, padedantis išskirti kredito pasiūlą ir paklausą lemiančius veiksnius, galiausiai – panelinių duomenų modelis, leidžiantis įvertinti, kaip kreditavimas priklauso nuo banko ypatybių. Atlikta analizė patvirtina, kad kredito rinka ir ekonomikos raida yra glaudžiai susijusios. Per pastarąjį finansinį ciklą kredito procesus gana nedaug lėmė palūkanų normos ir būsto kainos, spartus kredito augimas labiau buvo susijęs su nominaliąja konvergencija ir žemu pradiniu įsiskolinimo lygiu. Tačiau kreditas ir palūkanų normos yra svarbūs būsto kainų kilimo veiksniai. Perteklinis kredito augimas taip pat gali būti svarbus ekonominių disbalansų susidarymo veiksnys. Pagrindiniai žodžiai: kredito rinka, kredito paklausa, kredito pasiūla, pusiausvyra, nepusiausvyros modelis, vektorinis paklaidų koregavimo modelis. [Iš leidinio]Reikšminiai žodžiai: Kredito paklausa; Kredito pasiūla; Kredito rinka; Nepusiausvyros modelis; Pusiausvyra; Vektorinis paklaidų koregavimo modelis; Balance; Credit demand; Credit market; Credit supply; Disequilibrium model; Equilibrium; Equilibrium model; The demand for loans; The supply of credit; Vector error correction model; Vector error-correction model.

ENMonetary policy conducted by the Eurosystem, as well as micro- and macroprudential regulations implemented by the Bank of Lithuania have a significant impact on credit market developments in Lithuania. In order to ensure effectiveness of policy interventions, it is necessary to gain a deeper understanding about the workings of the local credit market, its empirical regularities, the relationship between credit and the broader economy, and key drivers behind credit market activity. The present paper is aimed at adressing some of these issues. To this end, we develop a suite of simple empirical models: four vector error-correction models (VECMs) allow us to analyse dynamic and cointegrating relationships between credit and various macroeconomic variables, the disequilibrium model sheds light on credit supply and demand conditions in Lithuania over the last decade, wheareas the panel regression model explains bank lending activity with the help of both macroeconomic and bank-specific variables. VECM results suggest that the Lithuanian credit market activity is closely linked to housing prices and the overall level of economic activity. In contrast, the role of nominal interest rates in determining credit activity over the financial cycle was found to be quite modest: during the credit boom, gradually rising interest rates had a modest dampening effect on credit market activity, whereas in the post-crisis period an extremely favourable interest environment did not instantly ignite credit market buoyancy. One of the more important drivers behind strong credit growth during the better half of the previous decade was a low initial price level (also linked to low initial wage and indebtedness levels), which created a strong potential for nominal convergence and credit market deepening.VECM analysis also provides interesting insights into the role of credit in endogenous relationships with other economic variables. There are indications that plentiful and cheap credit might lead to increases in house prices, whereas the positive impact of rising house prices on credit is less pronounced. Likewise, credit fluctuations do a much better job in explaining the variation in bank deposits, as opposed than vice versa. Positive credit shocks also lead to postitive changes in investment, corporate earnings and overall economic activity but credit developments can only explain a relatively small part of the general variation in these variables. Strong credit growth is a precursor to a rise in economic imbalances, for instance, in the form of current account deficits. The credit supply and demand disequilibrium model reveals that credit supply in Lithuania positively depends on the rates of return on capital, banks‘ net borrowing from abroad and housing prices, and negatively depends on interest rate margins. Also, prior to the financial crisis, the credit supply was positively related to the level of capital in the banking system; however this was no longer the case after the crisis, when the system as a whole became very well capitalised. Credit demand is positively related to real economic activity and investment, and negatively linked to housing prices and real interest rates. The panel regression model yields quite similar results. Notably, the model does not show significant differences in the lending activity of domestically and foreign-owned banks, except that the latter can effectively boost their lending activity by borrowing from their parent institutions. [From the publication]