ALIENOR is an econometric model built to provide macroeconomic scenarios and conduct macroprudential analysis, in particular for larger stress-test exercises. In the model design, we pay particular attention to the link between financial variables and the real economy, to estimate the potential impact of the materialization of financial systemic risk, and to perform policy exercises. In addition, we quantify the impact of the macroeconomy on financial variables, with a focus on households’ credit, Non-Financial Corporates’ credit, and real estate prices, given the key role played by those variables during the crisis. Finally, we analyse the consequences on the economy of an exogenous increase in the long-term interest rates and a decrease in real estate prices.
The Great Recession highlighted the strong linkages between the financial sector and the real economy. The increased attention paid to macrofinancial risks and the development of this new regulation, going under the name of macroprudential policy, create the need to develop a set of tools to run the different stages of macroprudential analysis: risk assessment, ex-ante calibration of macroprudential instruments and ex-post evaluation of their effectiveness. In particular, pre-crisis macroeconometric models often paid little attention to macrofinancial variables, such as credit or real estate prices, which have become key variables for macroprudential regulators.
In this work, we develop a macro-financial econometric model named ALIENOR aiming to support macroprudential analysis regarding: (i) risk assessment, for example by producing macroeconomic adverse scenarios to be used as input in stress tests models evaluating banks’ solvency and (ii) calibration of instruments, by assessing the impact of macroprudential measures on the real economy. This model is composed of a set of econometric and accounting equations, describing the dynamics of the macroeconomic and financial aggregates for the French economy, focusing on interactions among financial and macroeconomic variables. This relatively parsimonious model is able to produce adverse scenarios in which the financial developments play a crucial role in determining macroeconomic dynamics.
It is important to note that, while usable in a purely standalone way, we intend to articulate ALIENOR with stress tests exercises of bank capital. In those exercises, banks’ resilience is tested considering different macroeconomic scenarios derived from ALIENOR. To avoid redundancy, bank capital is thus absent of the ALIENOR.
The results quantitatively support the key role of the transmission channels between financial and macroeconomic variables. Regarding the impact of financial variables on real ones, the model is specified in order to obtain a financial accelerator: a deceleration in credit growth has a negative impact on spending and on asset prices. The slowdown in the real economy further decreases asset prices and banks propensity to lend.
Regarding the impact of real variables on financial ones, we put the emphasis on three macrofinancial variables that have proved critical in financial cycles and systemic crises: households’ credit, Non-Financial Corporates’ (NFC) credit, and real estate prices. For households’ credit, we develop a credit disequilibrium model (Maddala and Nelson (1974), Laroque, and Salanié (1994)). This modelling choice allows disentangling demand and supply-driven regimes, delivering interesting insights on the effectiveness of macroprudential policies. In our estimated model, in normal times the regime is demand driven, meaning that credit supply is in excess with respect to demand. Conversely, during crises, the regime is supply-driven: banks supply less credit than demanded by households, triggering aggregate credit rationing. For firms’ credit, we exploit the information provided by the evolution of firms’ aggregate balance sheet. This design results in a clear narrative of the underlying factors of the momentum of corporate debt. For the housing sector, the equation for real estate prices includes households’ Debt-Service Ratio (DSR) as the main driving factors. The DSR is the fraction of income that agents use to repays their debt (principal and interests), thus capturing households’ purchasing power.
The model is used to produce adverse scenarios: we analyze the effects on the economy of two different types of shocks: (i) a 100 basis points exogenous increase in the long term interest rates; (ii) a negative housing shock equal to an initial reduction of -10% of the real estate prices. Under the long-term interest rate hike, the financial sector goes through a generalized increase of the interest rates. Overall, the total credit in the economy decreases, with negative effects on the real economy. In addition, the increase in the interest rates and the fall in revenues cause a substantial decrease of housing prices. Under the house price shock, the model shows a decline in households’ spending, triggering a deceleration on the aggregate demand side. Moreover, this decrease further lowers house prices amplifying the initial negative shock and activating an important financial accelerator mechanism.
Updated on: 07/18/2019 14:54