Diagnosis, Therapy and Prophylaxis of Banking Crises - Challenges in Financial Supervision and Monetary Policy

2003 ◽  
Author(s):  
Stefan Stein ◽  
Christian Brütting ◽  
Stephan Paul ◽  
Andreas Horsch
Keyword(s):  
Monetary Policy ◽  
Banking Crises ◽  
Financial Supervision ◽  
Diagnosis Therapy

New Advantages of Tying One’s Hands: Financial Supervision, Monetary Policy and Central Bank Independence

2010 ◽  
Author(s):  
Lucia Dalla Pellegrina ◽  
Donato Masciandaro ◽  
Rosaria Vega Pansini
Keyword(s):  
Monetary Policy ◽  
Central Bank ◽  
Central Bank Independence ◽  
Financial Supervision

Model of the influence of Internet finance on monetary policy based on gibbs sampling and vector autoregression

2020 ◽  
pp. 1-11
Author(s):  
Hui Wang ◽  
Huang Shiwang
Keyword(s):  
Monetary Policy ◽  
Gibbs Sampling ◽  
Posterior Distribution ◽  
Estimation Method ◽  
Financial Supervision ◽  
Var Model ◽  
Model Parameters ◽  
State Variables ◽  
Frequency Data ◽  
Internet Finance

The various parts of the traditional financial supervision and management system can no longer meet the current needs, and further improvement is urgently needed. In this paper, the low-frequency data is regarded as the missing of the high-frequency data, and the mixed frequency VAR model is adopted. In order to overcome the problems caused by too many parameters of the VAR model, this paper adopts the Bayesian estimation method based on the Minnesota prior to obtain the posterior distribution of each parameter of the VAR model. Moreover, this paper uses methods based on Kalman filtering and Kalman smoothing to obtain the posterior distribution of latent state variables. Then, according to the posterior distribution of the VAR model parameters and the posterior distribution of the latent state variables, this paper uses the Gibbs sampling method to obtain the mixed Bayes vector autoregressive model and the estimation of the state variables. Finally, this article studies the influence of Internet finance on monetary policy with examples. The research results show that the method proposed in this article has a certain effect.

Monetary Policy, Banking Crises, and the Friedman Rule

2002 ◽  
Vol 92 (2) ◽  
pp. 128-134 ◽  
Author(s):  
Bruce D Smith
Keyword(s):  
Monetary Policy ◽  
Banking Crises ◽  
Friedman Rule

scholarly journals The Role of US Monetary Policy in Banking Crises Across the World

2020 ◽  
Vol 68 (1) ◽  
pp. 66-107
Author(s):  
C. Bora Durdu ◽  
Alex Martin ◽  
Ilknur Zer
Keyword(s):  
Monetary Policy ◽  
Banking Crises ◽  
The World ◽  
Us Monetary Policy

scholarly journals The Role of U.S. Monetary Policy in Global Banking Crises

2019 ◽  
Vol 2019 (039) ◽  
Author(s):  
Bora Durdu ◽  
◽  
Alex Martin ◽  
Ilknur Zer ◽  
Keyword(s):  
Monetary Policy ◽  
Banking Crises ◽  
Global Banking

scholarly journals Sentiment in Central Banks’ Financial Stability Reports*

2020 ◽  
Author(s):  
Ricardo Correa ◽  
Keshav Garud ◽  
Juan M Londono ◽  
Nathan Mislang
Keyword(s):  
Monetary Policy ◽  
Systemic Risk ◽  
Financial Stability ◽  
Asset Prices ◽  
Banking Sector ◽  
Banking Crises ◽  
Central Banks ◽  
Percentage Point Increase ◽  
Financial Cycle ◽  
Point Increase

Abstract We use the text of financial stability reports (FSRs) published by central banks to analyze the relation between the sentiment they convey and the financial cycle. We construct a dictionary tailored specifically to a financial stability context, which classifies words as positive or negative based on the sentiment they convey in FSRs. With this dictionary, we construct financial stability sentiment (FSS) indexes for thirty countries between 2005 and 2017. We find that central banks’ financial stability communications are mostly driven by developments in the banking sector. Moreover, the sentiment captured by the FSS index explains movements in financial cycle indicators related to credit, asset prices, systemic risk, and monetary policy rates. Finally, our results show that the sentiment in central banks’ communications is a useful predictor of banking crises—a one percentage point increase in FSS is followed by a twenty-nine percentage point increase in the probability of a crisis.

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