<![CDATA[Stress testing in the banking sector is an important tool for assessing a bank's risk level and provides a basis for assisting financial institutions and regulators in making decisions by taking into account the impact of macroeconomic changes on banking risk, one of which is credit risk, which can be seen from the level of non-performing loans (NPL). bank. NPL ratio data and macroeconomic data are periodic data presented in monthly, quarterly or semi-annual periods and are categorized as time series data. At the stationarity test stage it is known that the data is not stationary, so to overcome this a differencing process is carried out so that it uses the Vector Autoregressive Integrated (VARI) model in the stress testing analysis. Based on the Granger test, there is a causal relationship between macroeconomic variables and NPL variables, so the model can be used to predict NPL values as a stress-testing analysis. The model that meets all assumptions and has the maximum lag is the VARI (5.1) model. The results of the model analysis show that the largest and most significant contribution to changes in the NPL variable is from real GDP shocks followed by shocks from Bank Indonesia's policy variables, namely the BI Rate and Inflation.]]>
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