APLIKASI METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) PADA PERAMALAN STABILITAS BANK SYARIAH DI INDONESIA

The international financial crisis in has increased the world's interest in Islamic banking. Forecasting the stability of Islamic Banks is important to prevent cost crisis in the future. Z-score can explain the possible bankruptcy of a bank that measures the number of standard deviations a return realization has to fall in order to deplete equity. Autoregressive Integrated Moving Average (ARIMA) has the advantage of accuracy and precision in forecasting. Analysis result showed that ARIMA (24,1,5) is the best model for forecastng the z-score of the Islamic bank with the following equation: Ì‡ ̇ ̇
The model was used to predict predicts the z-score from September 2016 to December 2017. The result showed that z-score of Islamic banks have a downward trend until January 2017 and upward trend from June until November 2017 and then drop in December 2017. The main factor is the changes of retained earnings at each period.

REFERENCES

Alamsyah, Halim. 2012. Perkembangan dan Prospek Perbankan Syariah Indonesia: Tantangan Dalam Menyongsong MEA 2015. Ceramah Ilmiah Ikatan Ahli Ekonomi Islam (IAEI), Milad ke-8 IAEI 2012, (Online), (http://www.bi.go.id, di akses 21 Oktober 2016)

Ariefianto, Moch. Doddy. 2012. Ekonometrika: Esensi dan Aplikasi dengan Menggunakan EVIEWS. Jakarta: Erlangga.

Arifin dkk (editor). 2004. IMF dan Stabilitas Keuangan Internasional: Suatu Tinjauan Kritis. Jakarta: PT Alex Media Komputindo

Bank Indonesia. Kajian Stabilitas Keuangan No. 27 September 2016, (Online), (www.bi.go.id, diakses 19 Februari 2017)

Berger, Allen N. dkk. 2008. Bank Competition and Financial Stability. The World Bank Policy Research Working Paper 4696. (Online), (https://papers.ssrn.com, di akses 14 Januari 2017)

Bowerman, Bruce L.dan Richard T. O'Connell. 1993. Forcasting and Time Series: An Applied Approach. Third Edition. Belmont: Duxbury Press.

Budisantoso, Totok dan Nuritomo. 2014. Bank dan Lembaga Keuangan Lain. Edisi 3. Jakarta:  Salemba Empat

ÄŒihák, Martin, dan Heiko Hesse. 2008. Islamic Banks and Financial Stability: An Empirical Analysis. IMF Working Paper 08/16, (Online), (https://papers.ssrn.com, diakses 10 November 2016)

Contreas, Javier dkk. 2003. ARIMA Models to Predict Next-Day Electricity Prices. IIEE Transactions on Power Systems, Vol. 18, (Online), (http://stat.fsu.edu, diakses 5 Desember 2016)

Crockett, Andrew. 1997. The Theory and Practice of Financial Stability. Essay in International Finance. (Online), (http://link.springer.com, diakses 19 Januari 2017)

Ghassan, Hassan B dkk. 2013. Financial Stability of Islamic and Conventional Banks in Saudi Arabia: a Time Series Analysis. DSS Empirical Economics and Econometrics Working Papers Series, (Online), (https://ideas.repec.org, diakses 20 Oktober 2016)

Gujarati, Damondar N. dan Dawn C. Porter. Tanpa Tahun. Dasar-Dasar Ekonometrika. Buku 2 Edisi 5. Terjemahan oleh Raden Carlos Mangunsong. 2012. Jakarta: Salemba Empat

Karim, Norzitah Abdul dkk. 2015. Macroeconomics Indicators and Bank Stability: A Case of Banking in Indonesia. Conference Paper, (Online), (http://www.researchgate.net, di akses 7 Desember 2016)

Lepetit, Laetitia dan Frank Strobel. 2013. Bank Insolvency Risk and Time-Varying Z-Score Measures. Journal of International Financial Markets, Institutions and Money, (Online), (https://hal.archives- ouvertes.fr, diakses 16 November 2016)

Morris, Verlis C. 2011. Measuring and Forecasting Financial Stability The Composition of an Aggregate Financial Stability Index for Jamaica. Business, Finance & Economics in Emerging Economies Vol 6,  (Online), (http://ccmfuwi.org, di akses 7 Desember 2016)

Rais. 2009. Pemodelan Data Time Series Dengan Metode Box-Jenkins. JIMT, (Online), (http://jurnal.untad.ac.id, diakses 14 Januari 2017)

Suryani dan Hendyadi. 2015. Metode Riset Kuantitatif: Teori dan Aplikasi pada Penelitian Bidang Manajemen dan Ekonomi Islam. Jakarta: Prenadamedia Group Jakarta

Wei, William W.S. 1994. Time Series Analysis: Univariate and Multivariate Methods. Redwood City: Addison-Wesley.

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