Islamic banks in Indonesia exist side by side with their conventional counterparts within a dual banking system. The central bank aims to achieve price stability in the economy using both conventional and Islamic monetary instruments within this dual monetary system. This creates a unique environment for Islamic banks. This research aims to examine the role of Islamic banks in the monetary policy transmission mechanism using Granger Causality and Autoregressive Distributed Lag (ARDL). The balance sheet components of deposit and financing are hypothesized to function in the monetary transmission process within the bank financing channel. Granger causality reveals that the Islamic interbank overnight rate Granger causes Islamic deposits and financing, and that these in turn Granger cause the industrial production index. This index Granger causes inflation, Islamic deposits, and the Islamic interbank overnight rate. Islamic deposits and inflation then Granger cause the Islamic interbank overnight rate. The ARDL results show cointegrating relationships in the output and inflation model. Long-term convergence could be achieved to correct deviations in output and inflation by way of Islamic banks' deposits and financing. However, there is only a short-term influence of Islamic bank deposits on output. In the short-run, these deposits do not contribute to inflation. Islamic bank financing does not have a short-term relationship with output and inflation; therefore, there is declining effectiveness of Islamic banks' financing contribution to the economy.
Purpose: This research purports to forecast the number of foreign tourists arriving at major airport in Indonesia. The airports chosen are Soekarno Hatta, Juanda, I Gusti Ngurah Rai, and Kualanamu international airports. Design/methodology/approach: The data used were foreign tourists arrival at major airports located in Jakarta, Surabaya, Medan, and Denpasar. The data extended from January 2014 until December 2018. Two time-series methods were employed, namely Holt-Winter Seasonality and Exponential Smoothing with maximum likelihood. The forecasts would reveal the fitted numbers of foreign tourists arriving from January 2019 until December 2019. The fitted numbers would then be compared to the actual numbers of January 2019 to December 2019. Findings: The results showed that, overall, Holt-Winters seasonality excel at forecasting foreign tourists arrival at Soekarno Hatta and Juanda international airports. While Exponential Smoothing perform better for prediction at I Gusti Ngurah Rai and Kualanamu international airports. The MAPE for Holt-Winters at Soekarno Hatta and Juanda international airports were 26.1585% and 14.538%. The MAPE for Exponential Smoothing at at I Gusti Ngurah Rai and Kualanamu international airports were 7.76% and 15.6791%. Research limitations/implications: Forecasting for foreign tourist arrival at Soekarno Hatta and Juanda international airports should employ Holt-Winters approach. Forecasting for foreign tourists arrival at I Gusti Ngurah Rai and Kualanamu international airports should employ Exponential Smoothing with maximum likelihood. Practical implications: Certain forecasting methods work better than the others at certain international airports. Many forercasting methods are available. Two methods are specifically prominent for detecting seasonality and trend, i.e Holt-Winters and Exponential Smoothing with maximum likelihood. Originality/value: Most research focus on one method at a time. This research compares two methods so that we can know better which method is suitable for certain airports. Four international airports are sampled in this study. Not many research focus on several places at a time. Paper type: Research paper
The research aims to investigate the dynamics among rural banks’ capital, macroeconomic variables and microconomic variables. Macroeoconomic variable consists of infllation and interest. Microeconomic variables consist of loan to deposi ratio, nonperforming loans, and return on assets. The data are excerpted from OJK and BI’s website. The data are monthly data extending from January 2010 until May 2021. The testing method used is vector error correction model (VECM). The results show that rural banks’ capital is significantly affected the previous state of capital and profitability. This indicates the importance of sustainability of capital in rural banks and how it is very much dependent upon the profitability of the rural banks. Further, the research results show that there ar two cointegrating functions in the model. Both cointegration functions are influential to inflation. The speed adjustment derived from the residuals of capital function is 0.6754% and 13.5669% for residual from inflation function itself. The slow adjustment process is due to the small market share and assets of rural banking sector. In addition, capital, nonperforming loans, and return on assets are pivotal for central bank monetary policy to control inflation.
Penelitian ini bertujuan untuk memodelkan volatilitas return indeks saham perusahaan dividen tertinggi di Indonesia (DIV 20) sebelum dan sesudah pandemi COVID 19. Model keluarga ARCH (Autoregressive Conditional Heteroscedasticity) digunakan dalam hal ini. Periode penelitian diperpanjang dari 18 Mei 2018 hingga 18 Februari 2022. Batas waktu dimulainya pandemi adalah 1 April 2020. Data pengembalian adalah pengembalian mingguan. Hasilnya menunjukkan bahwa sebelum pandemi, GJR-GARCH(1,1) dapat memetakan dan melacak volatilitas dengan sangat baik karena mencetak AIC dan SIC pra-pandemi terendah. Oleh karena itu, penelitian ini menguatkan bukti adanya reaksi asimetris dari partisipasi pasar terhadap kemunculan dan penyebaran berita baik dan buruk di pasar. Setelah pandemi, efek ARCH menjadi kurang jelas. Angka signifikansi menurun meskipun efek ARCH masih signifikan pada 0,15. Performa model ARCH(1) secara signifikan lebih tinggi daripada model lain pasca-pandemi. Hasil tersebut menjadi bukti bahwa pascapandemi ketidakpastian yang dihadapi pelaku pasar sangat tinggi. Hal ini mengakibatkan meningkatnya volatilitas. Model keluarga ARCH menjadi kurang signifikan karena pengembaliannya lebih acak. Analisis lebih lanjut, bagaimanapun, menunjukkan bahwa pengembalian belum mengikuti model random walk meskipun keacakan meningkat. Oleh karena itu, ARCH(1) masih sesuai untuk memodelkan volatilitas setelah Pandemi. This research aims at modeling the volatility of Indonesian highest paying dividend companies stock index (DIV 20) returns before and after pandemic COVID 19. The ARCH (Autoregressive Conditional Heteroscedasticity) family models were employed in this regard. The research period extended from 18 May 2018 to 18 February 2022. The cutoff for the commencement of pandemic was 1st April 2020. The return data were weekly returns. The results suggested that before pandemic, GJR-GARCH(1,1) could map and trace the volatility very well since it scored the lowest AIC and SIC pre-pandemic. Therefore, this research corroborated the evidence that there existed asymmetric reaction from the market participation toward the emergence and spread of good and bad news in the market. After pandemic, the ARCH effect became less obvious. The significance number was decreasing although the ARCH effect was still significant at 0.15. ARCH(1) model performance was significantly higher than the other models post-pandemic. The result presented evidence that after pandemic the uncertainty facing the market participants was very high. This resulted in the increase of the volatility. The ARCH family model was becoming less significant because the returns were more random. Further analysis, however, showed that the returns did not yet follow the random walk model despite the increasing randomness. Therefore, ARCH(1) was still appropriate to model the volatility after Pandemic.
This research aims to compare the performance of Holt Winters and Seasonal Autoregressive Integrate Moving Average (SARIMA) models in predicting inflation in Balikpapan and Samarinda, two biggest cities in East Kalimantan province. The importance of East Kalimantan province cannot be overstated since it has been declared as the venue for the capital of Indonesia. Hence, inflation prediction of the two cities will give valuable insights about the economic nature of the province for the country’s new capital. The data used in this study extended from January 2015 to September 2021. The data were divided into training and test data. The training data were used to model the time series equation using Holt winters and SARIMA models. Later, the models derived from training data were employed to produce forecasts. The forecasts were compared to the actual inflation data to determine the appropriate model for forecasting. Test data were from January 2015 to December 2020 and test data extended from January 2021 to September 2021. The result showed that Holt-Winters performed better than SARIMA in prediction inflation. The Root Mean Squared Error (RMSE) values are lower for Holt-Winters Exponential Smoothing for both cities. It also predicts better timing of cyclicality than SARIMA model.
Purpose: This research aims to forecast JII returns by employing various Holt-Winters models. The models used in this research are Holt-Winters seasonality, Holt-Winters damped method, and Holt-Winters with maximum likelihood approach. Holt-Winters model is capable of recognizing and modeling trends and seasonality. Therefore, it is suitable for forecasting purposes.Methodology: Three models are employed in this research. The first one is Holt-Winters seasonality, also known as triple exponential smoothing. This model analyzes the level, trend, and seasonality components in the return series. The second model is the Holt-Winters damped method that uses smoothing parameters to lower the overstatement effect that usually occurs within Holt-Winters seasonality. The third model is Holt-Winters with Maximum Likelihood. Holt-Winters seasonality estimates parameters by choosing the least-squares. At the same time, Holt-Winters with Maximum Likelihood uses maximum likelihood to fit in the series with certain distributions and generate forecasts by determining distributions with the most likelihood.Findings: The result showed that Holt-Winters seasonality forecasts better than the other methods. The model could recognize the seasonal pattern and trend of the JII returns. It has the lowest Root Mean Squared Error (RMSE) as the parameter for forecast accuracy. Holt-Winters damped method has accuracy right below Holt-Winters seasonality. It can also map the pattern and trend of the returns. Holt-Winters with Maximum likelihood predicts less accurately. However, it can recognize the random walk inclination of the return, although it failed to generate the seasonal pattern and trend of the JII returns.Originality: This research attempted to apply Holt-Winters models to predict JII returns. Most research concerning the Islamic stock index focuses on volatility and forecast based on the level of volatility. Therefore, this research can fill in the gaps in the literature in which forecast of Islamic stock index can be conducted by modeling the seasonality and trend using Holt-Winters models.Practical implications: Investors always try to find the best generating investment return. Investors concerned with the shariah rules will always find lawful investment tools such as Islamic stocks or the Islamic stock index. Returns of the Islamic stock index can be forecast by using the Holt-Winters model. Therefore, investors might know the pattern of returns generated by investing in Islamic stocks.
This paper investigates the exchange rate volatility model in Southeast Asian countries. The countries selected were Indonesia, Malaysia, Thailand, The Philippines, Vietnam and Singapore. This paper aims to model the volatility of the regional currencies exchange rate against the international currency, i.e. US Dollar. The period covered in this study extended from 1 January 2013 until 31 July 2019. These were the daily exchange rate of 7 currencies of Southeast Asian countries. The currency involved were Indonesian Rupiah (IDR), Malaysian Ringgit (MYR), Thai Baht (THB), The Philippine Peso (PHP), Vietnam Dong (VND), and Singaporean Dollar (SGD). All currencies were measured in the exchange rate against the US Dollar (USD). The result indicated that PARCH model is the best method to explain the movement of MYR, VND, and SGD. GARCH can model THB and PHP. Only IDR that has volatility explainable by TARCH.
The purpose of this research is to model the volatility of Stock Indices in Indonesian capital market. This research focuses on two stock indices namely SRI-KEHATI and LQ45. SRI_KEHATI is a stock index that consists of companies whose operations are sustainable and environmentally friendly. This stock index is also known as “green index†due to its environment and sustainability concern. This is the novelty of this research that fills in the gap in the literature in which not much known regarding this green index. As the comparison, LQ45 stock index was modeled. The data used in this model were daily returns data of both index. The research period extended from 2 January 2019 to 1 November 2021. The research employed four models i.e. ARCH (1), ARCH (2), GARCH (1,1) and GJR-GARCH (1,1) for both indices returns. The ARCH and GARCH model were employed to capture the conditional variance of the indices return, while GJR-GARCH was specifically chosen to investigate whether there exists asymmetric effect in which return reacts more to bad news than good news. Akaike Information Criterion (AIC) and Schwarz Information Criterion (SIC) were chosen as the parameters for choosing the best models. Data analysis showed that GJR-GARCH was the best model for modeling the returns volatility of SRI-KEHATI and LQ45. This model was able to capture the essential property of asymmetric effect present in both models. The second best model was ARCH (2). Apparently, returns variance of Indonesian stock indices are affected more by lagged residuals. The limitation of this research lies in its research period that covered both pre-pandemic and post-pandemic period. Stock market behavior might be very different between these two periods. Future research may endeavor to investigate how the volatility of stock differs between pre-pandemic and post-pandemic period.
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