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Study on early Warning Indicators of Bank Runs: Markov-Switching Approach

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  • Iskandar Simorangkir

Abstract

A run on a bank occurs when a large number of depositors, fearing that their bank will be unable to repay their deposits in full and on time, simultaneously try to withdraw their funds immediately. A run on a particular bank can lead to a banking crisis if it spreads to other banks (contagious effect). Bank runs and banking crises have become a global phenomenon and occurred repeatedly in many countries since the era of modern banking. In the case of Indonesia, bank runs have also reoccurred time and again. In 1992, bank runs affected several national banks, subsequently precipitating the liquidation of one bank. Then in 1997/1998, bank runs developed into the worst banking crisis ever witnessed in the banking history of Indonesia. The closure of 16 banks by the government on 1st November 1997 undermined customer confidence in their banks, particularly private banks believed by the public to have the least favourable financial performance. A loss of confidence in the banks encouraged large swathes of the public to immediately withdraw their funds (bank runs). The subsequent bank runs spread systemically to other banks, especially private bank, thus developing in to a banking crisis. Considering the extent of losses precipitated by bank runs and the banking crisis, extensive studies on the early warning indicators of bank runs are urgently required to prevent future bank runs and banking crises. This paper aims to comprehensively analyze the early warning indicators of bank runs for all banks, both during the sample period of 1990-2005 as well as during the banking crisis in 1997-1998. The study of early warning indicators of bank runs uses Markov-Switching model. To calculate transition probability from tranquil state to bank runs state is used Markov-Switching process through auto-regressive approach. The change of deposit in each bank is used as variable of bank runs. The Markov-Switching (MS) results show that the MS model gives robust as the result of early warning indicator of bank runs. That condition is reflected by testing which was done on an actual incident of 102 banks showing that the MS results only give false signal to an estimated 0.69% until 2.08%. Markov-Switching The Markov-Switching (MS) results show that the MS model gives robust as the result of early warning indicator of bank runs. That condition is reflected by testing which was done on an actual incident of 102 banks showing that the MS results only give false signal to an estimated 0.69% until 2.08%.

Suggested Citation

  • Iskandar Simorangkir, 2012. "Study on early Warning Indicators of Bank Runs: Markov-Switching Approach," EcoMod2012 4147, EcoMod.
  • Handle: RePEc:ekd:002672:4147
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    1. Demosthenes Tambakis, 2021. "A Markov chain measure of systemic banking crisis frequency," Applied Economics Letters, Taylor & Francis Journals, vol. 28(16), pages 1351-1356, September.
    2. Thibaut Duprey & Benjamin Klaus, 2017. "How to Predict Financial Stress? An Assessment of Markov Switching Models," Staff Working Papers 17-32, Bank of Canada.
    3. Ayi Supriyadi, 2015. "External vulnerability indicators: the case of Indonesia," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Indicators to support monetary and financial stability analysis: data sources and statistical methodologies, volume 39, Bank for International Settlements.
    4. Vakhtina, Elena & Wosnitza, Jan Henrik, 2015. "Capital market based warning indicators of bank runs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 304-320.
    5. Berry A. Harahap & Pakasa Bary & Anggita Cinditya M. Kusuma, 2020. "The Determinants of Indonesia’s Business Cycle," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 9(special i), pages 215-235.

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