Financial Crisis In Indonesia Research Articles

Will Indonesia enter the 2023 financial crisis? Application of early warning model system

Purpose ― This paper estimates the possibility of a financial crisis in Indonesia using an early warning system (EWMS) model. Method ― A quantitative EWMS model has been developed to detect a potential financial crisis in 2023 based on the econometric logistic probability model (Logit) Findings ― Based on the model estimates, Indonesia is expected to enter a financial crisis without adequate macroeconomic policies in the next 12 to 24 months. In recent years, Indonesia has implemented prudent macroeconomic policies such as increasing the Bank Indonesia policy rate and sustaining the state budget to avoid the impact of a deep financial crisis. Implications ― To avoid the potential for further financial crises, Indonesia must implement a wider range of crisis mitigation policies. Originality/value ― Although many argue that financial crises are predictable, it has been demonstrated in the literature that little is known about how to prevent them. This paper contributes to providing empirical evidence to address these issues.

STRATEGI UMKM UNTUK MENGHADAPI PERUBAHAN EKONOMI PADA MASA PANDEMI DEMI KEBERLANGSUNGAN USAHA

Micro, small and medium corporations are humans monetary sports that make a contribution to accelerating the recuperation from the monetary crisis in Indonesia. Micro, Small, and Medium corporations (MSMEs) are enterprise sectors that have a vital function within the financial system and may live to tell the tale all through the financial crisis in Indonesia. for the duration of the COVID-19 pandemic, many perpetrators experienced difficulties. The issue of acquiring uncooked materials, hampering production and distribution, the lack of facts era expertise and the obstruction of business networks makes MSME actors should innovate. Because of this, it's miles necessary to increase its competitiveness. robust synergy from MSME actors in the agency will boom competitiveness, a good way to accelerate their enterprise growth, This research targets to find out, perceive, review, examine and interpret all studies that have been achieved previously, This research ambitions to discover the business strategies applied with the aid of Micro, Small, and Medium establishments (MSMEs) in dealing with the Covid-19 pandemic., The approach used is the technique qualitative studies with a descriptive technique, The consequences of the identity are anticipated to add to the literature on Micro, Small, and Medium businesses (MSMEs) that have a role in the Indonesian economy in addition to the authorities’ efforts to resolve a problem as a way to have an effect on the financial system including poverty and unemployment.

PEMBERDAYAAN MASYARAKAT TENTANG DIGITAL MARKETING DAN DIGITAL PAYMENT KEPADA USAHA MIKRO KECIL DAN MENENGAH (UMKM)

The Covid-19 pandemic has shaken the entire world economy, including Indonesia. During the 1998 financial crisis in Indonesia, micro, small and medium enterprises (MSMEs) were able to survive and support the economy. Digital marketing and digital payments are one of the renewals of today's technological advances. The existence of a breakthrough like this forces MSME actors to transform into the digital era. At the time of the COVID-19 pandemic that had hit Indonesia, and the government's policies that limited the mobility of the community, digital marketing and digital payments were the right solution for MSME players to stay afloat and help improve the Indonesian economy. Based on the results of activities in the Bekasi Jati village about digitizing marketing and digitizing payments for MSME actors by conducting registration training to shopee application platform in the area, there are still many things that need to be improved, starting from having a bank account and replacing their mobile phones that support them to market their products on the internet. E-commerce in order to make their business advanced in technology-based business competition. The impact of the program that the author has given to MSME actors who have registered in e-commerce is very helpful for them in terms of product marketing and increasing their income during the covid-19 pandemic that is hitting the surrounding environment.

Entrepreneurial career outcomes of women who switch to self-employment during the crises

This paper aims to reveal the implication of transitions from paid to self-employment during the period of economic crises, and whether such a transition may widen the gender gap in career outcomes. We use insights from the career perspective in entrepreneurship to study career outcomes in three steps. First, we study the survival rate and the career patterns of individuals who switch to self-employment in the post-crisis period. Second, we examine income, job satisfaction, and life satisfaction, as the subjective and objective measure of career success of individuals who survive in self-employment. Lastly, we look at the possible differences in entrepreneurial career outcomes between male and female self-employed in our samples. We use descriptive statistics and entropy balancing to examine the changes in career outcomes of individuals who switch to self-employment during the 1998 Asian financial crisis in Indonesia and the 2008 global financial crisis in the United Kingdom. Our findings demonstrate that in both countries, the survival rate is less than 50% for over 5 years following the transitions, with the risk of experiencing downward job mobility for those who do not survive in self-employment. In addition, our study reveals that transitions to self-employment give no gains to the individuals; as the career outcomes of those who switch to self-employment are relatively similar or even not any better than similar individuals who remain in paid employment. Lastly, we found a smaller proportion of females survive in self-employment in the long term, as those who switch to and survive in self-employment in both countries are dominated by males.

Early detection of the financial crisis in Indonesia, Thailand, and South Korea based on real exchange rates

The 1997 Asian financial crisis and the 2008 global financial crisis had a significant impact on the economies of various countries, including Indonesia, Thailand, and South Korea. Therefore, this study aims to detect early financial crises in these three countries based on real exchange rate indicators. The early detection of financial crises, relevant institutions in applying appropriate policies. Thus, financial crises can be avoided. The real exchange rate indicator contains fluctuations, and changing conditions. As a solution, the volatility models, i.e. autoregressive conditional heteroscedasticity (ARCH) and generalized ARCH (GARCH) explains volatility or fluctuations, and Markov switching (MS) explains changing conditions. The combination of these models yields a smoothed probability value used for detecting financial crises. The real exchange rate of Indonesia, Thailand, and South Korea data against the United States were used from January 1990 to March 2020. The results showed that MSARCH( 2,1), MS-ARCH(2,1), and MS-GARCH(2,1,1) are for Indonesia, Thailand, and South Korea, respectively. The real exchange rate indicator can detect crises in Indonesia and South Korea in the 1997 and 2008. Whereas in Thailand, it was only able to detect crises occured in the 1997. The prediction results of the smoothed probability showed no signal of the financial crisis in Indonesia, Thailand, and South Korea one year later.

Spatial impacts of fiscal stimulus policies during the 2009 global financial crisis in Indonesia

In response to the 2008/2009 global financial crisis (GFC), many developing countries, including Indonesia, launched fiscal stimulus packages (FSP). These FSPs typically consist of several sophisticated fiscal policies that may not necessarily complement each other. While the impact of these policies at the aggregate country level in developed countries has been widely discussed, the spatial impact of these policies within developing countries is less understood. Utilizing an inter-regional computable general equilibrium (CGE) model, this paper aims to assess and understand the short- and long-term economic impacts of these stimulus policies using Indonesia as a case study. This paper, hence, provides a quantitative ex-post assessment of FSPs typically implemented during the 2009 GFC by developing economies. Overall, results, indicate that fiscal stimulus had a positive impact on aggregate demand and on poverty prevention, principally via stimulating private consumption. Corporate income tax cuts have the largest economic impact in the long-run, and cash transfers are the most useful policy tool for alleviating poverty. An FSP, however, could have an uneven spatial distributional effect on output across regions, particularly in the short-term.

The application of combined Markov regime switching and volatility model in detecting early financial crisis in Indonesia

The financial crisis had occurred in Indonesia in the middle of 1997 to 1998 which caused the economy of Indonesia was down suddenly. Because the crisis occurred suddenly and even unexpectedly, the Indonesian government was not ready to deal with it. Therefore, the Indonesian government needs a system that can detect crisis signals; so that, such a financial crisis can be avoided. Export and import indicators have high fluctuation and regime-changing during a crisis so that they also can be used to detect crisis signals. The volatility model is able to explain the volatility which included in the indicators, while the Markov regime switching model can explain the regime-changing. Furthermore, a method that can be used to detect a crisis is the combination of Markov regime switching and volatility model. The value of smoothed probability obtained from the combination of those models is able to detect the financial crisis in Indonesia. The results show that export and import indicators can be modelled successively using MRS-GARCH (2,1,1) and MRS-ARCH (2,1). The results obtained from the prediction of those two models show that there is no financial crisis signal in Indonesia for a year later.

Prediction of Indonesian financial crisis using Markov regime switching autoregressive conditional heteroscedasticity models based on bank deposits and lending/deposit interest rate indicators

The financial crisis that occurred in middle of 1997 made Indonesia to become one of the countries which had the worst and the longest affected in term of its rate of recovery. This event made us aware of the importance of creating an early warning system for the financial crisis. The crisis occurred due to several macroeconomic indicators experiencing very high fluctuation and changes in the structure of the condition (regime). The combination of volatility and Markov regime switching models is a type of model that can explain the fluctuation and changes in such condition. The indicators of bank deposit and lending/deposit interest rate in January 1990 to June 2019 were used to construct the combined model. The results of this research showed that the MRS-ARCH(2,1) model for the bank deposit and lending/deposit interest rate indicators can explain the conditions of the financial crisis that occurred. The predicted value of the MRS-ARCH(2,1) model shows that from July 2019 to June 2020 there were no signs of a financial crisis in Indonesia.

Early detection of Indonesian financial crisis using combination of volatility and Markov switching models based on indicators of real exchange rate and M2/foreign exchange reserves

The financial crisis that occurred in the middle of 1997 and 2008 had a significant impact on the economy of Indonesia. To minimize the impact of the crisis, an early detection system that can detect some signs of a financial crisis is needed so that the relevant departments can apply some appropriate policies. The crisis occurred due to several macroeconomic indicators which reached very high fluctuation and changing condition. The combination of volatility and Markov switching models could explain the crisis. The indicators of the real exchange rate and M2/foreign exchange reserves from January 1990 to June 2018 were used to build such combined model. The results showed that the best model of real exchange rate and M2/foreign exchange reserves are MS GARCH (2,1,1). Real exchange rate indicator could explain the crisis in 1997 and 2008 periods, while the M2/foreign exchange reserves indicator could only explain the crisis in 1997. The predicted value of smoothed probability of real exchange rate and M2/foreign exchange reserves indicators showed that by the end of 2019 and early 2020 there will be no signs of a financial crisis in Indonesia.

The prediction of financial crisis in Indonesia based on the smoothed probability value from the combination of volatility and Markov switching models

Indonesia is a country that applies an open economic system. The application of an open economic system can lead to the transmission of crises from other countries, for example the major financial crisis that experienced by Indonesia in 1997 and 2008. The financial crisis caused disruption to the economy stability of the country itself, so it is necessary to predict the financial crisis with the aim to protect economy stability. The indicators used in this research are the Indonesia Composite Index (ICI) and foreign exchange reserves. The method used is a combination of volatility and Markov switching models, namely the MS-ARCH(2,1) for ICI indicator and MS-GARCH(2,1,1) for foreign exchange reserves indicator. The result showed that based on the smoothed probability value of the combination of those two models, the ICI indicator could explain the crisis that occurred in 1997 and 2008, while the foreign exchange reserves indicator could only explain the crisis that occurred in 1997. In addition, the results of financial crisis prediction in Indonesia show that Indonesia will not experience a financial crisis for the next year. However, if the ICI and foreign exchange reserves indicators decrease by at least 32%, a crisis can occur.

Financial crisis prediction in Indonesia using combined of volatility and Markov switching models based on real interest rate on deposit and nominal exchange rate indicators

The financial crisis has happened Indonesia 1997 and 2008. The crisis had brought the disadvantages impact on the economy of Indonesia. To anticipate the impact of the crisis, an early detection system that can detect some signs of a financial crisis is needed so that the government as a decision maker in maintain the stability of the economy. The crisis occurred due to hight volatility and structural changes in macroeconomic indicators. One of model that can be used to captured signals crisis is a combination of volatility and Markov switching. Fluctuations can be explained using volatility model, while some changes in conditions are explained through Markov switching. This study uses indicators of the real interest rate on deposit and nominal exchange rate to detect signals crisis in Indonesia. The results showed that the MS-ARCH(2,1) models of the real interest rate on deposit could only explain the crisis that occurred in 1997 and the MS-GARCH(3,1,1) models of nominal exchange rate could explain the crisis that occurred in 1997 and 2008. Based on the predicted value of smoothed probability, it shows that from July 2019 to June 2020, there are no signals of financial crisis in Indonesia.

The Future of Indonesia Islamic Banking Industry: Bankruptcy Analyzing the Second Wave of Global Financial Crisis

This study aims to analyze the resilience of the Indonesia Islamic banking industry from bankruptcy risk in times of financial crisis. This study use binary regression as dependent variable. The Islamic banking industry bankruptcy risk generated from capital buffering changes in accordance with POJK No. 11 / POJK.03 / 2016. Economic Growth, BI rate, Inflation rate and Islamic Money Market O/N rate as independent variables. To anticipate the unobserved heterogeneity, Bank Profitability, USD Exchange Rate, Federal Reserves Interest Rate and Money Supply (M2) are used as control variables. Data analysis to predict bankruptcy uses logistic regression model of the global financial crisis in Indonesia. Data obtained from monthly statistics report of Bank Indonesia and the Financial Services Authority from Januari 2008 until December 2019 by the structural break which impacted the Islamic banking capital on December 2012. Our analysis divide indonesia financial condition into two difference time, before and after the structural break. By Probit Logit Regression Model, it was concluded that USD exchange rate is independent variable which is consistently influence the probability of islamic banking bankruptcy risks in all period. BI rate not significantly affected the probability of Islamic banking bankruptcy because profit and loss sharing method applied in Islamic banking. Before the structural break the probability of bankruptcy significantly affected by Money Supply, USD exchange rate and economic growth. But after the structural break, the probability of bankruptcy significantly affected by Bank Profitability, USD exchange rate, BI rate and Islamic Money Market rate. This model precisely predicted by Count R Squared of 75.81% - 86.67%.

Indonesian Financial Crisis Prediction Using Combined Volatility and Markov Regime Switching Model Based on Indicators of Real Interest Rate on Deposits and Lending Interest Rate/Deposit Interest Rate

The financial crisis that occurred in mid-1997 made it aware of the importance of building an early detection system because the crisis had a severe impact on the Indonesian economy. The crisis occurred due to several macroeconomic indicators experiencing very high fluctuations and the existence of changes in the structure (regime). The combined volatility and Markov regime switching models are very suitable to explain the crisis. Indicators of real interest rates on deposits and lending interest rate/deposit interest rates from 1990 to 2018 were used to build the combined model. The result showed that the MRS-ARCH (2,1) model for real interest rate on deposits indicator and the MRS-GARCH (3,1,1) model for lending interest rate/deposit interest rate indicator could explain the crisis. Markov regime switching predictions indicate that in 2019 there are no signs of a financial crisis in Indonesia.

Pemodelan Deteksi Dini Krisis Mata Uang Berdasarkan Indiktor Nilai Tukar Nominal

The financial crisis by definition is a situation where several financial assets lose most of their nominal value. The financial crisis experienced by Indonesia in 1997 had a severe impact on the Indonesian economy, so a model was needed to detect this crisis. The financial crisis can be detected using the nominal exchange rate indicator. This study aims to determine the appropriate combination of volatility models and the Markov switching model as a model for detecting financial crises in Indonesia based on nominal exchange rate indicators. The nominal exchange rate indicator taken from 1990 to 2018 is used to build a model for early detection of the financial crisis in Indonesia. The results showed that the combined exponential generalized autoregressive conditional heteroscedasticity and Markov regime switching, MRS-EGARCH (3,1,1) volatility models were both used to detect financial crises in Indonesia based on nominal exchange rate indicators

Deteksi Krisis Keuangan di Indonesia Berdasarkan Indikator Nilai Tukar Riil Menggunakan Model SWARCH (2,3)

The financial crisis that hit Asia in mid-1997 began with the financial crisis in Thailand which then spread to Indonesia. The impact of the financial crisis in Indonesia is so severe that a crisis detection system is needed. The financial crisis detection system can be done by simple monitoring of macroeconomic indicators such as real exchange rate. Excessive real exchange rate is predicted to have a great chance of crisis.<br />The result shows that the real exchange rate from January 1990 to June 2013 has heteroscedasticity effect and there are structural changes so it can be modeled using SWARCH model (2,3) with ARMA (1.0) as conditional average model and ARCH (3) as model conditional variance. The inferred probabilities value of the SWARCH (2,3) model in February 1998 of 1 and July 1998 of 0.9968 over 0.5 indicates that the period is in a high volatile condition indicating a crisis. The SWARCH model (2.3) based on the real exchange rate indicator was able to capture the high volatile conditions in February 1998 and July 1998 as the impact of the 1997 Asian financial crisis.<br />Keywords : Deteksi, krisis keuangan, nilai tukar riil, SWARCH

Does economic crisis have different impact on husbands and wives? Evidence from the Asian Financial Crisis in Indonesia

AbstractThis paper analyzes the intra‐household allocation of risk‐coping mechanisms by testing whether the Asian financial crisis affected married men and women differently in Indonesia. It estimates the effect of the district consumption shock during the crisis on the change in married men's and women's working status and assets. It finds that the regional shock is associated with a large increase in wives’ employment and a large decrease in wives’ business assets in urban areas, and not associated with change in husbands’ working status or asset holdings in urban areas. In rural areas the regional shock is associated with a drop in women's business assets and not related to other outcomes of husbands or wives. Receiving government social safety net program aid during the crisis seems to substitute for the decrease in business assets of women as a coping strategy in both urban and rural areas.

Financial Crisis Forecasting in Indonesia Based on Bank deposits, Real Exchange Rates and Trade Terms Indicators

Indonesia has experienced a financial crisis several times, but the crisis that occurred in 1997 had a bad impact on the economy and national stability. Therefore, it needs a model that can be used to predict the condition ahead. This paper proposes forecasting the financial crisis in Indonesia. Bank deposits, real exchange rates and exchange rates of trade indicators are used in this paper. Data from January 1990 to December 2015 are used to form the models, while data from January to December 2016 are used to accurate the models. Combination of volatility and Markov switching models are used to model the data. The result suggests that the appropriate model for bank deposit and real exchange trade is SWARCH (3.1), and for real exchange rates is SWARCH (3.2). SWARCH (3.1) model has the accuracy 100%, while SWARCH (3.2) has the accuracy 75%. Based on these models, it can be forecasted that there is no financial crisis in Indonesia on 2017.

The detection of financial crisis using combination of volatility and markov switching models based on real output, domestic credit per GDP, and ICI indicators

Open economic system has not only provided ease for every country to interact with each other, but also make it easier to transmitted the crisis. Financial crisis that hit Indonesia in 1997-1998 and 2008 severely impacted the economy, thus a method to detect crisis is required. According to Kamisky et al. [6], crisis can be detected based on several financial indicators such as real output, domestic credit per Gross Domestic Product (GDP), and Indonesia Composite Index (ICI). This research aims to determine the appropriate combination of volatility and Markov switching model to detect financial crisis in Indonesia based on the indicators. Volatility model used for modeling the unconstant-variance of ARMA. Markov switching is an alternative model of time series data with changed conditions in the data, or called state. In this research, we are using three assumption of states namely low volatility state, medium volatility state and high volatility state. The data of each indicator were taken from 1990 until 2016. The result of the study show that MS-ARCH(3,1) can be used to detect the financial crisis that hit Indonesia in 1997-1998 and 2008 based on real output, domestic credit per GDP, and ICI indicators.

Detection method of financial crisis in Indonesia using MSGARCH models based on banking condition indicators

Financial crisis has hit Indonesia for several times resulting the needs for an early detection system to minimize the impact. One of many methods that can be used to detect the crisis is to model the crisis indicators using combination of volatility and Markov switching models [5]. There are some indicators that can be used to detect financial crisis. Three of them are the difference between interest rate on deposit and lending, the real interest rate on deposit, and the difference between real BI rate and real Fed rate which can be referred as banking condition indicators. Volatility model used to overcome the conditional variance that change over time. Combination of volatility and Markov switching models used to detect condition change on the data. The smoothed probability from the combined models can be used to detect the crisis. This research resulted that the best combined volatility and Markov switching models for the three indicators are MS-GARCH(3,1,1) models with three states assumption. Crises in mid of 1997 until 1998 has successfully detected with a certain range of smoothed probability value for the three indicators.

Herd Behavior and Indonesian Financial Crisis

Indonesia is one of an emerging country in Asia. As an emerging country, Indonesian capital market attract the investor from around the world to make investment. Investment require good, clear information and trustworthy to make decision. The information that investor received may vary to other investor. These differences could lead to herd behavior. Good herd behavior will lead to economic growth otherwise will lead to crisis. These research examine the effect of herd behavior of investors to the financial crisis of 2008 and 2013 of the Indonesian capital market. Variables used in this research is financial crisis was measured using Exchange Market Pressure Index (EMPI) and herd behavior measured with LSV formula. The method used is a model of Vector Auto Regression (VAR) with a stationary test phase, co-integration test, VAR estimations, impulse response analysis, analysis of variance decomposition, and causality test. The findings is indicate that investors in Indonesia stock market has irrational behavior that leads to herd behavior, especially during financial crisis furthermore, herding behavior affecting the occurrence of financial crisis in Indonesia. These findings provide knowledge about the effect of herding behavior in financial crisis Indonesia and provide input for academics in the field of behavioral finance management, especially in the development of capital markets and for investors to give feedback on the importance of the behavior of investors in the Indonesian capital market.