Monday, June 18, 2012

Monitoring Systemic Risk Based on Dynamic Thresholds

Monitoring Systemic Risk Based on Dynamic Thresholds. By Kasper Lund-Jensen
IMF Working Paper No. 12/159
June 2012

Summary: Successful implementation of macroprudential policy is contingent on the ability to identify and estimate systemic risk in real time. In this paper, systemic risk is defined as the conditional probability of a systemic banking crisis and this conditional probability is modeled in a fixed effect binary response model framework. The model structure is dynamic and is designed for monitoring as the systemic risk forecasts only depend on data that are available in real time. Several risk factors are identified and it is hereby shown that the level of systemic risk contains a predictable component which varies through time. Furthermore, it is shown how the systemic risk forecasts map into crisis signals and how policy thresholds are derived in this framework. Finally, in an out-of-sample exercise, it is shown that the systemic risk estimates provided reliable early warning signals ahead of the recent financial crisis for several economies.



The financial crisis in 2007–09, and the following global economic recession, has highlighted the importance of a macroprudential policy framework which seeks to limit systemic financial risk.  While there is still no consensus on how to implement macroprudential policy it is clear that successful implementation is contingent on establishing robust methods for monitoring systemic risk.3 This current paper makes a step towards achieving this goal. Systemic risk assessment in real time is a challenging task due to the intrinsically unpredictable nature of systemic financial risk. However, this study shows, in a fixed effect binary response model framework, that systemic risk does contain a component which varies in a predictable way through time and that modeling this component can potentially improve policy decisions.

In this paper, systemic risk is defined as the conditional probability of a systemic banking crisis and I am interested in modeling and forecasting this (potentially) time varying probability. If different systemic banking crises differ completely in terms of underlying causes, triggers, and economic impact the conditional crisis probability will be unpredictable. However, as illustrated in section IV, systemic banking crises appear to share many commonalities. For example, banking crises are often preceded by prolonged periods of high credit growth and tend to occur when the banking sector is highly leveraged.

Systemic risk can be characterized by both cross-sectional and time-related dimensions (e.g.  Hartmann, de Bandt, and Alcalde, 2009). The cross-sectional dimension concerns how risks are correlated across financial institutions at a given point in time due to direct and indirect linkages across institutions and prevailing default conditions. The time series dimension concerns the evolution of systemic risk over time due to changes in the macroeconomic environment. This includes changes in the default cycle, changes in financial market conditions, and the potential build-up of financial imbalances such as asset and credit market bubbles. The focus in this paper is on the time dimension of systemic risk although the empirical analysis includes a variable that proxies for the strength of interconnectedness between financial institutions.

This paper makes the following contributions to the literature on systemic risk assessment: Firstly, it employs a dynamic binary response model, based on a large panel of 68 advanced and emerging economies, to identify leading indicators of systemic risk. While Demirgüç-Kunt and Detragiache (1998a) study the determinants of banking crises the purpose of this paper is to evaluate whether systemic risk can be monitored in real time. Consequently, it employs a purely dynamic model structure such that the systemic risk forecasts are based solely on information available in real time. Furthermore, the estimation strategy employed in this paper is consistent under more general conditions than a random effect estimator used in other studies (e.g.  Demirgüç-Kunt and Detragiache (1998a) and Wong, Wong and Leung (2010)). Secondly, this paper shows how to derive risk factor thresholds in the binary response model framework. The threshold of a single risk factor is dynamic in the sense that it depends on the value of the other risk factors and it is argued that this approach has some advantages relative to static thresholds based on the signal extraction approach.4 Finally, I perform a pseudo out-of-sample analysis for the period 2001–2010 in order to assess whether the risk factors provided early-warning signals ahead of the recent financial crisis.

Based on the empirical analysis, I reach the following main conclusions:

1. Systemic risk, as defined here, does appear to be predictable in real time. In particular, the following risk factors are identified: banking sector leverage, equity price growth, the credit-to-GDP gap, real effective exchange rate appreciation, changes in the banks’ lending premium and the degree of banks interconnectedness as measured by the ratio of non-core to core bank liabilities. There is also some evidence which suggests that house price growth increases systemic risk but the effect is not statistically significant at conventional significance levels.

2. There exists a significant contagion effect between economies. When an economy with a large financial sector is experiencing a systemic banking crisis, the systemic risk forecasts in other economies increases significantly.

3. Rapid credit growth in a country is often associated with a higher level of systemic risk.  However, as highlighted in a recent IMF report (2011), a boom in credit can also reflect a healthy market response to expected future productivity gains as a result of new technology, new resources or institutional improvements. Indeed, many episodes of credit booms were not followed by a systemic banking crisis or any other material instability. It is critical that a policymaker is able to distinguish between these two scenarios when implementing economic policy. I find empirical evidence which suggests that credit growth increases systemic risk considerably more when accompanied by high equity price growth. Therefore, I argue that the evolution in equity prices can be useful for identifying a healthy credit expansion.

4. In a crisis signaling exercise, I find that the binary response model approach outperforms the popular signal extracting approach in terms of type I and type II errors.

5. Based on a model specification with credit-to-GDP growth, banking sector leverage and equity price growth I carefully evaluate the optimal credit-to-GDP growth threshold.  Contrary to the signal extraction approach the optimal threshold is not static but depends on the value of the other risk factors. For example, the threshold is around 10 percent if equity prices have decreased by 10 percent and banking sector leverage is around 130 percent but only around 0 percent if equity prices have grown by 20 percent and banking sector leverage is 160 percent. In comparison, the signal extraction method leads to a (static) credit-to-GDP growth threshold of 4.9 percent based on the same data sample.

6. In the out-of-sample analysis, I find that the systemic risk factors generally provided informative signals in many countries. Based on an in-sample calibration, around 50– 80 percent of the crises were correctly identified in real time without constructing too many false signals. In particular, a monitoring model based on credit-to-GDP growth and banking sector leverage signaled early warning signals ahead of the U.S. subprime crisis in 2007.