Calibrating Macroprudential Policy to Forecasts of Financial Stability

Abstract

The introduction of macroprudential responsibilities at central banks and financial regulatory agencies has created a need for new measures of financial stability. While many have been proposed, they usually require further transformation for use by policymakers. We propose a transformation based on transition probabilities between states of high and low financial stability.Forecasts of these state probabilities can then be used within a decisiontheoretic framework to address the implementation of a countercyclical capital buffer, a common macroprudential policy. Our policy simulations suggest that given the low probability of a period of financial instability at year-end 2015, U.S. policymakers need not have engaged this capital buffer.

A Profit-to-Provisioning Approach to Setting the Countercyclical Capital Buffer: The Czech Example

“…15 Basically, the model allows the transition probabilities of the first-order Markov process to depend on covariates t Z . The states of the economy are generally interpreted as low and high financial stability states (see Gadea-Rivas andPerez-Quiros, 2015, andBrave andLopez, 2017). However, this might be misleading in our case.…”

“…We estimate a univariate first-order autoregressive Markov-switching model as per Hamilton (1989). Following Brave and Lopez (2017), we use the model to capture the joint dynamics between real GDP and private credit growth while incorporating the different BPI versions into the time-varying transition probability model proposed by Diebold et al (1994). The motivation behind this model setting lies in the effort to capture both credit and real economic activity together with the proposed BPIs (again, we also present the results of this exercise for the Financial Cycle Index and the credit-to-GDP gap in Appendix B).…”

Calibrating the Magnitude of the Countercyclical Capital Buffer Using Market‐Based Stress Tests

“…(2013), Anundsen et al. (2016), Brave and Lopez (2019), and Tölö, Laakkonen, and Kalatie (2018). The rationale is that indicators that perform well as an early warning signal are better suited to signal the need for the buffer as well as its release before a banking crisis occurs.…”

“…The first approach is based on early warning models of banking crises or the buildup of macroeconomic vulnerabilities; see, for example, Behn et al (2013), Anundsen et al (2016), Brave andLopez (2019), andTölö, Laakkonen, andKalatie (2018). The rationale is that indicators that perform well as an early warning signal are better suited to signal the need for the buffer as well as its release before a banking crisis occurs.…”

The link between financial stress index and economic activity: prominent Granger causalities across frequencies in Luxembourg

“…Tng and Kwek (2015) detected that an increase in financial stress leads to tighter credit conditions and lower economic activity in five Asian countries (Indonesia, Malaysia, Philippines, Singapore and Thailand). Brave and Butters (2012) showed that increasing financial stress typically leads to sharp downturns in economic activity. Afonso et al (2018) examined the impact of financial stress, using the FSI developed by the International Monetary Fund (IMF), and fiscal developments in a threshold vector autoregressive model (VAR) model for several countries.…”