Does Financial Connectedness Predict Crises? / Camelia Minoiu, Chungwon Kang, V.S. Subrahmanian, Anamaria Berea.

Format:

Book

Government document

Author/Creator:

Minoiu, Camelia.

Contributor:

Berea, Anamaria.

Kang, Chungwon.

Subrahmanian, V.S.

Series:

IMF Working Papers; Working Paper ; No. 2013/267

IMF working paper ; WP/13/267

IMF Working Papers

Language:

English

Subjects (All):

Finance.

Physical Description:

1 online resource (45 p.)

Place of Publication:

Washington, D.C. : International Monetary Fund, 2013.

Language Note:

English

Summary:

The global financial crisis has reignited interest in models of crisis prediction. It has also raised the question whether financial connectedness - a possible source of systemic risk - can serve as an early warning indicator of crises. In this paper we examine the ability of connectedness in the global network of financial linkages to predict systemic banking crises. Our results indicate that increases in a country's financial interconnectedness and decreases in its neighbors' connectedness are associated with a higher probability of banking crises after controlling for macroeconomic fundamentals.

Contents:

Cover; Contents; 1. Introduction; 2. Data and methods; 2.1 Data description; 2.2 Global connectivity: Stylized facts; 2.3 Classification algorithm; 3. Results: Connectivity and banking crises; 3.1 Exploring the data: Conditional correlations; 3.2 Results from the classification algorithm; 3.3 Results from regression analysis; 3.4 In- and out-of-sample performance; 4. Conclusions; List of figures; 1. Global banking network in 1980 vs 2007; 2. Network density and total exposures; List of tables; 1. Average network indicators over time; 3. Financial connectedness around systemic banking crises

2. Classification algorithm in-sample performance3. Benchmark probit/logit model; 4. Augmented probit results (full version); 4. ROCs for benchmark vs augmented model (full version); 5. Augmented probit results (parsimonious version); 6. Classification algorithm and probit model in-sample performance; 7. Classification algorithm out-of-sample performance; 8. Classification algorithm and probit model out-of-sample performance; Appendices; Network indicator definitions; Principal components analysis (PCA)

Notes:

Description based upon print version of record.

Description based on online resource; title from PDF title page (ebrary, viewed February 17, 2014).

ISBN:

9781484331767

1484331761

9781484331316

1484331311

9781484331958

1484331958