Beschreibung:
Given the prevailing global circumstances, characterized by tightening global financial conditions and substantial macro-financial vulnerabilities, the significance of monitoring financial conditions becomes even more pronounced and calls for heightened attention to the assessment and surveillance of financial indicators. This paper introduces a Financial Conditions Index (FCI) tailored for Albania, spanning from 2000 to 2022, using a factor augmented vector autoregressive models with time-varying coefficients (TVP-FAVAR) and incorporating a wide range of indicators, grounded in the empirical literature. By aligning with the main financial dynamics during this timeframe, the constructed index emerges as a robust gauge for monitoring and assessing the financial landscape of the country. Additionally, through a threshold Bayesian VAR model, the paper examines the transmission of monetary policy and financial conditions shocks to the real economy, by capturing non-linear dynamics through differentiating between periods characterized by different stands of financial fragilities. The findings suggest that the credit-to-GDP gap could potentially function as an early warning indicator of financial vulnerabilities, with a positive gap possibly reflecting excessive risk-taking by financial institutions. Furthermore, the transmission of monetary policy and financial conditions shocks to the real economy depends non-linearly on the private nonfinancial sector credit and is not symmetric throughout the considered period, with monetary policy transmission being attenuated during periods of heightened vulnerabilities.