scholarly journals Financial Stress and Economic Activity Analysis in Indonesia

2020 ◽  
pp. 637-643
Author(s):  
Wulan Fatmawati ◽  
Iman Sugema ◽  
Syamsul Hidayat Pasaribu
Keyword(s):  
Monetary Policy ◽  
Price Index ◽  
Consumer Price Index ◽  
Financial Stress ◽  
Stress Index ◽  
Monthly Data ◽  
Vector Autoregressive ◽  
Monetary Stability ◽  
Financial Stress Index ◽  
Transmission Of Monetary Policy

Financial Stress marks the beginning of a crisis and may occur in all countries. This period is certainly unanticipated as it may disrupt a country's financial and monetary stability. An unstable financial system tends to be vulnerable to various stresses and may also hinder the transmission of monetary policy to function normally, thus resulting in ineffective monetary policy. This study aims to analyze financial and monetary stability in Indonesia using time series monthly data from January 1996 to January 2018. We used Vector Autoregressive (VAR) model. Our estimates suggest that the response of consumer price index to financial stress index takes longer to stabilize. This also applies to consumer price index response to consumer price index.

Measuring the Impact of Financial Crisis

2016 ◽  
pp. 274-283
Author(s):  
İsmail Yıldırım
Keyword(s):  
Exchange Rate ◽  
Interest Rate ◽  
Financial Crisis ◽  
Global Financial Crisis ◽  
Financial Stress ◽  
Stress Index ◽  
Destructive Effect ◽  
Monthly Data ◽  
Financial Stress Index ◽  
The Impact

Crisis in 2001 and global financial crisis in 2008 effect Turk economy in a lot of ways. Financial crisis creates destructive effect especially on increasing market economies. It is not so easy to watch occurring of this financial crisis and determining of its expanding. First of all determining of crisis terms are needed to predict of financial crisis. In this part, a financial stress index is composed by using TL interest rate and monthly data of global gross reserves belongs to $/TL exchange rate between 1997:01-2014:12 terms for Turkey. Months when financial stress index raised to top level for Turkey and financial crisis are observed on, are found as February(2001) and November (2008).

scholarly journals The Monetary Transmission Mechanism: An SVAR Analysis of the Four Municipalities in China

2017 ◽  
Vol 5 (1) ◽  
pp. 81
Author(s):  
Lili Wu ◽  
Mingxu Li
Keyword(s):  
Monetary Policy ◽  
Housing Demand ◽  
Monetary Policy Shocks ◽  
Monthly Data ◽  
Vector Autoregressive ◽  
Supply Shocks ◽  
Svar Model ◽  
Policy Shocks ◽  
Transmission Of Monetary Policy

This paper explores the role of housing markets in the transmission of monetary policy shocks across four Chinese municipalities, namely Beijing, Shanghai, Tianjin, and Chongqing. The analysis is based on identification of housing demand shocks, monetary policy shocks and credit supply shocks through a Structural Vector Autoregressive (SVAR) model estimated using monthly data for four cities from July 2005 to December 2015. The empirical results show great differences in the four cities as far as the housing market is concerned. They also indicate that housing plays a stronger role in the transmission of monetary policy shocks in Beijing and Shanghai than in Tianjin and Chongqing. These results are reasonably robust across several model specifications.

The Cleveland Financial Stress Index

2012 ◽  
Author(s):  
Timothy Bianco ◽  
Mikhail V. Oet ◽  
Stephen J. Ong
Keyword(s):  
Financial Stress ◽  
Stress Index ◽  
Financial Stress Index

TREND INFLATION AND MONETARY POLICY IN EASTERN EUROPE

2017 ◽  
Vol 23 (4) ◽  
pp. 1649-1663
Author(s):  
Monika Junicke
Keyword(s):  
Monetary Policy ◽  
Transition Economies ◽  
Inflation Targeting ◽  
Price Index ◽  
Consumer Price Index ◽  
Producer Price ◽  
Trend Inflation ◽  
Theoretical Predictions ◽  
Dynamic Stochastic ◽  
Eastern European Countries

I use a two-country dynamic stochastic general equilibrium (DSGE) model with a nonzero steady-state inflation to study monetary policy in transition economies. In particular, my analysis focuses on whether inflation targeting is based on a consumer price index (CPI) or its producer counterpart, producer price index (PPI). This issue is specifically relevant for transition economies as they might be subject to Balassa–Samuelson effects arising from trading in international markets. Under these circumstances, domestic inflation is possibly higher than imported inflation, hence targeting PPI inflation may prove more effective in influencing domestic macroeconomic variables than targeting CPI inflation. Using a Bayesian methodology, I find that the central banks of three Eastern European countries (namely, the Czech Republic, Hungary, and Poland) are likely to target PPI inflation rather than CPI inflation. This result is in line with the theoretical predictions in the literature, and is robust across several Taylor-type rules.

scholarly journals Analisis Indeks Harga Konsumen di Indonesia Melalui Pendekatan Kointegrasi

2019 ◽  
Vol 4 (2) ◽  
pp. 110-118
Author(s):  
Muhamad Muin ◽  
Keyword(s):  
Exchange Rate ◽  
Empirical Study ◽  
Money Supply ◽  
Price Index ◽  
Consumer Price Index ◽  
Data Series ◽  
Short Term ◽  
Monthly Data ◽  
The Relationship

This study aims to analyze the relationship between the rupiah exchange rate (RER) and the money supply (M1) on the outgrowth of the consumer price index (CPI) in Indonesia. The data used in this study are monthly data series from January 2005 to January 2019. The results of this empirical study shows that there is a relationship between RER and M1 on CPI in the long term and there is a correction in the short term balance (ECM) which is influenced by M1. All of these variables are significant at α = 5% and partly significant at α = 1%.

scholarly journals ASYMMETRIC TRANSMISSION OF MONETARY POLICY TO INTEREST RATES: EMPIRICAL EVIDENCE FROM INDONESIA

2021 ◽  
Vol 24 (1) ◽  
pp. 119-150
Author(s):  
Fitri Ami Handayani ◽  
Febrio Nathan Kacaribu
Keyword(s):  
Monetary Policy ◽  
Interest Rates ◽  
Money Market ◽  
Asymmetric Transmission ◽  
Correction Model ◽  
Monthly Data ◽  
Policy Transmission ◽  
Pricing Behavior ◽  
Lending Rates ◽  
Transmission Of Monetary Policy

This study investigates monetary policy transmission to the interest rates in Indonesia, focusing on changes in pricing behavior that may have occurred after the shift of benchmark policy rates in August 19, 2016. We analyzed monthly data on money market, deposit, and lending rates from November 2011 to December 2019. Two specifications of the error correction model capture asymmetric adjustments. We find that the new policy rate regime has improved the response of money market rates. However, the rigidity of bank retail rates has increased. Specifically, lending rates have become more rigid upwards, as lenders have become more responsive to monetary easing than to monetary tightening.

TOURISM INDUSTRY STRESS INDEX AND ITS RELATIONSHIP TO THE FINANCIAL STRESS INDEX

2021 ◽  
Vol 27 (2) ◽  
pp. 363-383
Author(s):  
Marina Malkina ◽  
Anton Ovcharov
Keyword(s):  
Interaction Design ◽  
Moving Average ◽  
Financial Stress ◽  
Principal Component ◽  
Tourism Industry ◽  
Real Effective Exchange Rate ◽  
Stress Index ◽  
Marginal Effect ◽  
Causality Test ◽  
Financial Stress Index

Purpose – development of the Tourism Industry Stress Index (TSI) and the Financial Stress Index (FSI) followed by an examination of their interaction. Design – The TSI, which aggregates tourist arrivals, overnight stays and net occupancy, was tested on data for Finland, Italy, Germany and Spain between 1993 and 2020. The FSI was composed of the S&P500 index, Brent oil futures, and the real effective exchange rate of the euro. Methodology / Approach – Both stress indices were calculated as the difference between the moving standard deviation and the moving average of the monthly growth rate of the selected indicators. We aggregated them by applying two alternative techniques: arithmetic mean and nonnormalized principal component analysis. The Granger causality test was utilised to assess the dependence between the indices. Findings – We identified periods of increased volatility in the European tourism market and described its connection to financial crises. The causality test of the FSI-TSI model showed that financial turmoil led to increased tourism market stress with an average lag of three months and a marginal effect of 0.2. Originality of the research – We recommend the Financial Stress Index as a predictor of the Tourism Industry Stress Index in the business cycle.

scholarly journals The Financial Stress Index: Identification of Systemic Risk Conditions

Risks ◽  
2015 ◽  
Vol 3 (3) ◽  
pp. 420-444 ◽  
Author(s):  
Mikhail Oet ◽  
John Dooley ◽  
Stephen Ong
Keyword(s):  
Systemic Risk ◽  
Financial Stress ◽  
Stress Index ◽  
Financial Stress Index
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