MODELLING BANK DISTRESS EVENTS: THE EFFECT OF NETWORK CONNECTEDNESS
Predicting bank distress is important since it is strongly related to financial stability. This study introduces the early-warning model that incorporates banking network connection to predict bank distress events for Indonesian banks. We employ variance decomposition to estimate the weighted con...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/36847 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Predicting bank distress is important since it is strongly related to financial stability.
This study introduces the early-warning model that incorporates banking network
connection to predict bank distress events for Indonesian banks. We employ
variance decomposition to estimate the weighted connection among banks and use
it as the network variable, both directional networks and net connections, in the
model. Logit pooled regression is used to see the relation between distress events
probability with network, bank-level, and macroeconomic variables. The usefulness
of the model is calculated by taking into account the policymaker’s preferences of
signaling a false alarm or missing a stress event. The aim of this study is to show
that by incorporating networks the performance of the model is increased. The
results show that asset, liquidity, and management quality are significant to the
distress probabilities and by incorporating network variables, the performance of
the model does increase. There is an important note that increasing the connection,
directionally, is good as it is related to a lower distress probability. But the
connection to others must be larger than from others so that the relation of the
network on distress probability is still negative. |
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