Indonesia’s Financial Stress Events and Macroeconomic Dynamics

KAU Journal of Islamic Economics, 2018

This research aims at detecting early indicators that cause conventional banking and Islamic banking crises, identifying the longest crisis period for both types of banks, and comparing the stability between the two kinds of banks. The method used is the Markov Switching Vector Autoregressive (MS-VAR) approach. This study uses secondary data obtained from official sources and in monthly form from January 2004 to March 2017. The results show that Islamic banking is more stable against internal and external shocks than conventional banking. Z-score for Islamic banking is higher (11,933) than the Z-score for conventional banking (11,679). The longest crisis period for conventional banking was

2015

Proceedings of the International Conference on Science and Science Education August 2015, p. MA.109-113 Available on http://fsm.uksw.edu/ojs/index.php/2015/article/view/6The financial crisis has hit the Indonesian economy since 1970 precisely in 1978, 1983, 1986, 1997, and 2008. The most severe crisis in mid-1997, beginning with the fall of the exchange rate Bath Thailand. There for, a model of financial crisis is needed as a reference to solve or anticipate crises that occur in the future. The aim of this study is to form a model of the financial crisis in Indonesia based on indicators of international reserves ratio to M2 using two and three state of SWARCH models. Based on the international reserves ratio to M2 data of the period January 1980 to December 2010 obtained the appropriate model is SWARCH(2,1) and SWARCH(3,1). This model can capture signals of crisis that occurred in May to July 1983 and March to June 199

Journal of Physics: Conference Series, 2018

The currency crisis that occurred in the middle of 1997, 1998 and 4 th quarter of 2008 was caused by disturbances in the currency system. When there is disturbance in the system then the cause indicators will experience fluctuations and changes in conditions. High fluctuation can be modeled using volatility model, while the changes in condition can be modeled using Markov switching model. Therefore, combination of volatility and Markov switching models is very appropriate to explain the crisis. In this paper, we use real output and ICI indicators on the period of January 1990 until December 2016 to explain the crisis as well as to forecast the possibility of an impending crisis. The results show that the appropriate model is ARCH (1) and Markov switching with 3 states, called SWARCH (3,1) model. This model can catch the crisis that happened in

2006

In this paper we examine the nature of currency crisis. In line with Jeanne (1997) and Jeanne (2000), we provide an empirical support of the view that both fundamentals and annimal spirits play an important role in the genesis of currency crisis. We do so by employing an out-of-sample forecasting exercise to analyse the Mexican crisis in 1994. We also extend the empirical framework suggested by Jeanne and Masson (2000) to test for the hypothesis that currency crisis was driven by sunspots. To this end we contribute to the existing literature by comparing Markov regime switching model with time-varying transition probabilities with two alternative models. The rst is a Markov regime switching model with constant transition probabilities. The second is a linear benchmark model. Empirical results show that Markov regime switching model with time varying transition probabilities outperfoms both linear and nolinear alternative models but it fails to predict the Mexican currency crisis in...

Journal of Physics: Conference Series, 2017

The severity of the financial crisis that occurred in Indonesia required an early warning system of financial crisis. The financial crisis in Indonesia can be detected based on imports, exports, and foreign exchange reserves. The purpose of the research is to determine an appropriate model to detect the financial crisis in Indonesia based on imports, exports, and foreign exchange reserves. Markov switching is an alternative framework for the approach often used in financial crisis detection. Combined volatility and Markov switching model with three states assumptions can be established if an AR and volatility models have been obtained. Imports, exports, and foreign exchange reserves data from January 1990 to December 2016 have the heteroscedasticity effect so that an ARCH model is used as a volatility model. Research shows that SWARCH(3.1) model is an appropriate model for detecting financial crisis in Indonesia based on imports, exports, and foreign exchange reserves.

International Journal of Scientific Research in Science, Engineering and Technology, 2020

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.

Jurnal Indonesia Sosial Teknologi

The currency crisis that occurred in Indonesia in 1997/1998 and 2008 had a significant impact on the Indonesian economy. An early detection system for crises is necessary to minimize the impact of such crises. One model that can detect currency crises is a combination of volatility and Markov switching models. There are several indicators that can be used to detect currency crises in a country, and one of them is exports. Research results indicate that the best combination of volatility and Markov switching models for the export indicator is MS-ARCH (2,2) with an assumption of two states. The crises of 1997/1998 and 2008 can be detected using the smoothed probability values with certain limits. Predictions for the period of July 2022-June 2023 based on the export indicator show no signs of a crisis in Indonesia.

2018

This paper aims to identify the Indonesia rupiah per US dollar turning points using a regime switching model. Firstly, to detect if nonlinear model suits the data at hand, the BDS test and CUSUM of square test was used and the results indicates that a nonlinear model suits the data. The paper then proceeds by using a univariate two state Markov Switching autoregressive model (MSAR) developed by Hamilton (1989), Engel and Hamilton (1990) to capture regime shifts behaviour in both the mean and the variance of the Indonesian rupiah per US dollar exchange rate between 2000 to 2015. The empirical evidence indicates strong transition probabilities suggesting that only extreme events can switch the series from an appreciation regime to a depreciation regime vice versa. Moreover, the results of the MSAR model was found to successfully capture the timing of the regime shifts of the Indonesian rupiah per US dollar exchange rate because of government intervention, Indonesian presidential elect...

SSRN Electronic Journal, 2002