INTERCONNECTEDNESS AND SYSTEMIC RISK OF INDIAN SHADOW BANKS - DETECTION MEASURES AND EFFECT ANALYSIS

Abstract

The Indian Non-Banking Financial Companies (NBFC, also Indian shadow banks) crisis of 2018-19 is a systemic event that affected the entire financial in India. The crisis started in FY 2018-19 when Infrastructure Leasing & Financial Services (IL&FS), one of the largest shadow banks in India, defaulted on its debt obligations. This led to a loss of confidence in the shadow banking sector and made it difficult for other shadow banks to raise funds. The crisis had a significant impact on the Indian economy. It led to a slowdown in economic growth, a decline in investment, and a rise in unemployment. The crisis also had a negative impact on the Indian financial system, as it eroded confidence in the shadow bank sector and made it difficult for banks to lend to shadow banks. The shadow banks play an important role in the Indian financial system and economy by providing financial services to individuals, small and medium-sized enterprises (SMEs), and other businesses that are not served by traditional banks. They offer a wide range of products and services, including loans, investments, and insurance. They account for over one-fifth of total assets held by the Indian financial system. They are the largest credit provider to the micro, small and medium enterprises. They help in financing of important infrastructure projects like roads, bridges, energy power plants, dams and real estate. They contribute to over five percent of GDP of India. They help in creating millions of jobs directly and indirectly. Thus the present study models the interconnectedness and systemic risk of the shadow banks and its effect on the Indian financial system and the economy. The study models the financial interconnectedness and systemic risk of shadow banks using Granger-causal network-based measures and takes the Indian shadow bank crisis of 2018–2019 as a systemic event. The study employs pairwise linear Granger-causality tests on return series adjusted for heteroskedasticity and autocorrelation on a rolling window of weekly returns data of 52 financial institutions from 2016 to 2019 to construct network-based measures and calculate network centrality. The empirical result demonstrated that the shadow bank complex network during the crisis is denser, more interconnected, and more correlated than the tranquil period. The network centrality established the systemic risk transmitter and receiver roles of institutions. The financial institutions that are more central and hold prestigious positions due to their incoming links will suffer maximum loss. The shadow bank network also showed v small-world phenomena similar to social networks. The Granger-causal network-based measure ranking of financial institutions in the pre-crisis period (explanatory variable) is rank-regressed with the ranking of financial institutions based on maximum percentage loss suffered by them during the crises period (dependent variable). The network-based measures have out-of-sample predictive properties and can predict the systemic risk of financial institutions. Supervisors and financial regulators can use the proposed measures to monitor the development of systemic risk and swiftly identify and isolate contagious financial institutions in the event of a crisis. Also, it is helpful to policymakers and researchers of an emerging economy where bilateral exposures' data between financial institutions are often not present in the public domain, plus there is a gap or delay in financial reporting. The out-of-sample predictive property of network-based measures is compared with firm level variables: size, leverage, short-term funding, non-interest income, non performing asset. The shadow bank leverage and non-performing asset is not a significant predictor but size, non-interest income and short-term-funding are significantly related to the maximum loss an institution faced during the crisis. However, network-based measures‘ out-of-sample is more statistically significant, supporting the importance of ―too-central-to-fail‖ and ―too-connected-to-fail‖ over ―too-big-to-fail‖ approach in identifying systemically important financial institution rather than relying on approach. To study the impact of shadow banking on the financial market distress, we modeled the rollover risk caused by over reliance on short term debt to fund the complex operations of the shadow bank. The rollover risk of the Indian shadow banks caused the increase in the default risk and market volatility of the institutions. However, rollover risk is non-significant in predicting the systemic risk of the institutions The study also examined the impact of Indian shadow bank on the real economy of India. The shadow bank incremental credit growth has surpassed commercial banks in the last decade. The real GDP is negatively affected by the shadow bank crisis. The sectors like consumer durable, MSME, automobiles, commercial housing and large industries have witnessed the degrowth during the shadow bank crisis. Despite a lower share in credit supply than banks, shadow banking negatively affects the real output productivity due to its specialized lending and securitization services.