Forewarning Indicator System for Banking Crisis in India
Forewarning Indicator System for Banking Crisis in India
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印度银行业危机预警指标体系
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
Tanima Niyogi Sinha Roy
中科院分区：
文献类型：
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作者：
B. Bhattacharya;Tanima Niyogi Sinha Roy
国际金融危机的经验表明，当前全球金融失衡的外溢效应损害了各国的金融稳定。在这种新出现的情况下，确定金融危机主要指标的国别研究似乎很重要。本文提出了一种基于“信号”方法和多元概率回归模型的印度银行业危机预警模型。最初，使用指数方法确定每月危机日期，发现1994-2007年间发生了四次系统性银行危机。通过“信号”方法调查这些危机事件，结果显示银行利率和91天国库券利率之间的息差增加，短期外债与外汇储备的比率增加，基础货币供应扩大，经济放缓，REER高估和LIBOR上调是银行危机的一些“领先”指标。研究还发现，如果由“领先”指标构成的加权综合指标的值超过0.205，那么未来几个月发生银行危机的可能性就会惊人地增加。使用概率回归，由“信号”方法确定的识别指标已被发现是稳健的，预警模型反映了令人满意的表现，无论是样本内还是样本外。本文还基于有序概率回归模型确定了与银行脆弱性低、中、高状态相关的相关变量。总体而言，研究结果证实了全球经济状况对国内宏观经济变量和银行业对危机的敏感性的影响。此外，研究中确定的大多数指标开始发出足够的早期信号，表明即将到来的银行业脆弱性，这可能表明当局采取了必要的先发制人措施。
The experiences of the global financial crisis reveal that the spillover effects of the current global financial imbalances undermine the financial stability of different countries. In this emerging scenario, country-specific studies for identifying leading indicators of financial crisis appear important. This paper sets out an approach that develops an early warning model for banking crisis prediction in India, based on the ‘signals’ approach and multivariate probit regression model. Initially, using the Index method for identifying monthly crisis dates, four episodes of systemic banking crisis have been found to have occurred during 1994-2007. Investigating these crisis episodes by the ‘signals’ approach, results revealed increase in the spread between Bank Rate and 91-day T-Bill rate, increase in the ratio of short-term foreign debt to foreign exchange reserves, expansion of base money supply, economic slowdown, REER overvaluation and hike in LIBOR as some of the ‘leading’ indicators for banking crisis. The study also finds that if the value of the weighted composite indicator, constructed with the ‘leading’ indicators, exceeds 0.205, then the probability of banking crisis increases alarmingly in future months. Using probit regression, the identified indicators, determined by the ‘signals’ approach, have been found to be robust and the early warning model reflects satisfactory performance, both in-sample and out-of-sample. The paper also identifies the relevant variables associated with low, medium and high states of banking fragility based on an ordered probit regression model.Overall, the results confirm the significance of global economic conditions working upon domestic macroeconomic variables and the sensitivity of the banking sector to crisis. Further, most of the identified indicators, in the study, started emitting sufficient early signals of an upcoming banking fragility, which could be indicative of necessary pre-emptive measures on the part of the authorities.