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Understanding Systemic Symptoms of Non-banking Financial Companies

Wasim Ahmad ( and Bhaskar Pathak ( are with the Department of Economic Sciences, Indian Institute of Technology, Kanpur. N R Bhanumurthy ( is with National Institute of Public Finance and Policy, New Delhi.

The riskiness of banks (public and private) and non-banking financial companies listed on the stock exchange is examined by measuring their extent of interconnectedness at the lowest tail (1%) quantile. Using the macro risk and balance sheet variables under the directional connectedness framework, this study finds the underperforming periods of Indian banks and NBFCs. The findings are consistent with the systemic risk rankings of the Reserve Bank of India for the domestic banks and systemically important NBFCs.

Since February 2016, the Indian financial system, most specifically banking, seems to be experiencing a rise in risk and uncertainty across its different spectrum. In the second week of February 2016, the State Bank of India (SBI) reported a loss of net profit of 62%, while the Bombay Stock Exchange (BSE) Sensex-30 simultaneously fell by 3,000 points, which had a ripple effect on other segments of the financial system.1 Again, in September 2018 the Indian financial system faced another shock on the back of default on the repayments by Infrastructure Leasing & Financial Services (IL&FS), and it accelerated further by the news of DSP Mutual Fund selling commercial papers of Dewan Housing Finance Corporation Limited (DHFL) on discount. Since then outlook of the non-banking financial companies (NBFCs) has been gloomy as the top 15 NBFCs estimated to have lost over ₹ 75,000 crore in the third week of September 2018. Some estimates suggest that the Housing Development Finance Corporation (HDFC) lost around ₹ 18,600 crore, Bajaj Finance around ₹ 13,800 crore and Bajaj Financial Services ₹ 4,200 crore.2

These developments have led to a ubiquitous concern about the systemic risk in the Indian financial system. The unfolding of banking and NBFC crises also opens new avenues to identify the possible symptoms of systemic risk by taking into account not only the banking sector but also the NBFCs.3 Given the nature of the IL&FS crisis, one may argue the crisis to be an outcome of the spiralling of the banking crisis to NBFCs. This can be justified in the light of higher dependence of NBFCs on banks. As of September 2018, NBFCs were the largest borrowers of about ₹ 7,458 billion from the scheduled commercial banks (SCBs) followed by the mutual funds and insurance companies (RBI 2018). In this context, the current article explores the nature and direction of interconnectedness between the banks and the NBFCs. The connectedness at the lowest tail could also help decipher the spillover risk from banks to NBFCs or otherwise.

The findings of this study can contribute siginificantly to the literature on Indian shadow banking as not many studies have examined NBFCs. To our knowledge, Acharya et al (2013) is the only systematic study that sheds a deeper light on the shadow banking system in India. The study identifies major determinants of non-deposit taking NBFCs and finds that the bank lendings form a significant proportion in NBFCs liabilities. On the banking system, some of the notable studies that covered different issues of banking research, including performance of banks (Das and Ghosh 2006), asset quality (Ahmed 2017), credit risk (Gulati et al 2018), non-performing assets (Sengupta and Vardhan 2017), bank competition (Sinha and Sharma 2018), systemic risk (Verma et al 2019).


This study uses the network theory to estimate the interconnectedness across financial institutions by inferring upon the magnitudes of nodes and edges. This is an emeging approach for estimating systemic risks. The widespread use of network models to identify the Systemically Important Financial Instituions (SIFIs) has gained momentum post-2008 crisis as it helps reveal the interdependence by taking into account a host of indicators. The Financial Stability Report of every country also mentions and higlights the extent of connectedness across its financial spectrum. Unfortunately, in the case of India, the identification of SIFIs has not attracted as much attention as it has been found in cases of other emerging countries such as China and Latin America. To the best of our knowledge, Verma et al’s study (2019) is the only study that employs the network model to identify the SIFIs, but only with respect to 31 banks. Using tail-event driven risk model (TENET), the study identifies the systemic risk emitters and receivers which is consistent with the Reserve Bank of India (RBI) ranking of domestic systemically important banks (D-SIBs).

The present study takes a similar perspective with respect to methodology, but includes major NBFCs to examine the extent of connectedness and idenitfies the SIFIs. In the absence of the data on bilateral exposure of banks and NBFCs from the public domain, this study attempts to build the network on individual banks and NBFCs by taking into account their balance sheet variables.

Taking the perspective of equity market and augmenting it with the sets of macro and balance sheet indicators, this study examines the interconnectedness between banks (both public and private) and the major NBFCs at the tail (1% quantile). The main objective is to examine whether the interconnectedness allows us to trace the evolution of banking and NBFCs crises by taking into account the major turn of events in an ultra-high dimensional set-up. The main feature of this study is that it not only takes into account the macro risk factors but also the balance sheet indicators of banks and NBFCs. One of the main advantages of TENET model is that it augments the conditional value at risk (CoVaR) model of Adrian and Brunnermeier (2016) with balance sheet indicators as a control. The inclusion of asset returns, macro and balance sheet indicators makes the TENET model high-dimensional and therefore employs a linear least absolute shrinkage and selection operator (LASSO) method to estimate CoVaR and further it for networks using a weighted adjacency matrix.

By adopting this approach, this study takes a lead and examines the symptoms of systemic risk of NBFCs using networks. The banks and NBFCs network could be justified by the fact that several banks have come into the limelight due to their financial linkages with the NBFCs. For instance, Yes Bank, Punjab National Bank (PNB), SBI, Bank of Baroda (BOB), Bank of India (BOI) and Union Bank of India (UBI) have recently sought the attention of RBI owing to their loan exposure to IL&FS and DHFL.4 The ripple effect of IL&FS episode is also visible on other NBFCs. The contagion fear is to some extent contained due to the timely policy intervention, but the non-banking sector is still experiencing an unprecedented contagion, which majorly includes Bajaj Finance, M&M Financial Services, Shriram Transport Finance and Edelweiss Financial Services.5

However, one may argue that since the Indian banks are heavily regulated the impact of IL&FS episode has not been as severe as a systemic risk, but the point of explanation could be to check further if we could decipher the channel/network of the Indian banking crisis and also Il&FS. Such analysis may also provide a holistic overview of India’s shadow banking. Due to limited availability of data on NBFCs, the study considers 17 NBFCs that fall in the categories of deposit-taking and non-deposit taking as classified by the RBI. The study, unfortunately, does not consider IL&FS because of unavailability of data and its complex institutional structure. However, the study considers other NBFCs with which networks could be formed.

Figure 1 shows the connectedness based on the bilateral exposures (asset and liabilities) among SCBs, scheduled urban cooperative banks (SUCBs), asset management companies–mutual funds (AMC–MFs), NBFCs, insurance companies, housing finance companies (HFCs), pension funds (PFs) and all-India financial institutions (AIFIs). The sample included 201 entities covered include 80 SCBs; 20 SUCBs; 22 AMC–MFs (which cover more than 90% of the AUMs of the mutual fund sector); 32 NBFCs (both deposit taking and non-deposit taking systemically important companies which represent about 60% of total NBFC assets); 21 insurance companies (that cover more than 90% of assets of the insurance companies); 15 HFCs (which represent more than 90% of total HFC assets); seven PFs and four AIFIs (NABARD, EXIM, NHB and SIDBI).

Data and Variables

The present study employs weekly data that covers 31 Indian banks, out of which 18 are public banks and 13 are private banks (Table 1, p 61). The study also considers 17 NBFCs listed on stock exchange and that have sufficient historical data of at least 10 years so that the rolling window analysis and high-dimension set-up is not altered (Table 2). Both the tables show the descriptive statistics of the logarithmic returns of sample institutions expressed in percentage. The banks which exhibit negative returns are crucial for the analysis. The sample series is retrieved from Thomson Reuters DataStream and RBI.

Following Härdle et al (2016) and Wang et al (2017), we consider the balance sheet variables, which include leverage, size, market to book ratio, debt to maturity, return on assets (ROA). The list of macro variables shall include short-term liquidity spread, immediate period changes in the 90-day treasury bill rate, the spread between 10-year and three-month treasury bill rate, credit spread, stock market returns, market volatility, interbank lending rate and weekly equity returns of each sample bank. The time period covered for this study is the weekly data from 12 January 2007 to 31 March 2017. To capture the global economic uncertainty, we consider the news based economic policy uncertainty index of US (US–EPU). The EPU series is based on the news extraction and its linkages with the uncertainty in the US economy. Since anecdotal event further amplifies the systemic risk analysis, we consider PNB, which reported a maximum loss in the fourth quarter of 2015, to perform tail-event driven network analysis.

Empirical Framework

First step: The study calculates the VaR of sample banks and firms i at given τ ϵ (0,1) at time t as: P(Bi,t≤ VaRi,t,τ ) = τ where τ is the quantile level, in our case it is 0.01 (1%), Bi,t shows the log returns of banks and NBFCs i at time t. Following Härdle et al (2016), the CoVaR will be estimated using two-step linear quantile regression (hereafter, LQR):

Bi,t = αi + βiMt-1 + εi,t …(1)

Bj,t = αj|t + λj|iMt-1 + βj/iBj,t + εj|i,t …(2)

where Mt-1 consists of macroeconomic variables. We then apply the quantile regression of return of a bank i on the macro variables to determine the VaR of a bank i . Following the steps of Adrian and Brunnermeie (2016), the VaR of sample banks and firms are calculated by applying LQR at 1% and 5% quantiles on macro indicators. After estimation of equation (2) βj|i is interpreted as same as linear regression coefficient, exhibiting the sensitivity of a bank j to changes in tail event log return of a bank i. Similarly, the CoVaR is obtained by plugging in VaR of bank i at level τ estimated in (3) into the equation (4):

VaRi,t, τ = αi+ βiMt-1 …(3)

CoVaRj|i,t,τ = αj|t + γˆj|iMt-1 + βj|iVaRi,t, τ …(4)

Thus, the risk of a bank and firm j is calculated using macro variables and a VaR of a bank i. The coefficient βj|i reflects the magnitude of interconnectedness. The directional connectedness moving from one bank to the whole system of banks and firms is obtained by setting j to be the return of the whole banking system and NBFCs and i to be the return of a bank. We call this as the contribution CoVaR. The reverse exhibits the connectedness moving from an individual bank j to the whole bank system i. It is called an exposure CoVaR, that is, the extent to which a single bank/firm is exposed to the overall risk of a banking system.

Second step: The second step introduces the high dimensional set-up by introducing both macros as well as balance sheet variables of banks and firms and applies the linear LASSO-based variable selection procedure of Hautsch et al (2015).6,7 The step then will build the systemic risk connectedness by adopting the directional connectedness approach. The connectedness derivations are given below:



where Rj,t={X–jt, Mt–1, Sj,t–1} is the information set which includes k variables. X–jt = {X1t, X2t,..., Xm,t} are the explanatory variables, which include the returns of all the sample banks and NBFCs except for bank (PNB) in our consideration j, m shows the number of banks and NBFCs. Sj,t-1 consists balance sheet indicators banks and NBFCs. We define the parameters as βj|Rj = {βj|-j, βj|M,βj|Sj}T which are static. To get the dynamic estimates, 48 weeks rolling window is used to estimate different window to capture the one-year cycle. Following Härdle et al (2016), smaller windows are recommended keeping in mind the stationarity of the data process and degrees of freedom. We define βˆj|R˜j= {β˜j|-j , β˜j|M, β˜j|Sj }T. According to Adrian and Brunnermeier (2016), CoVaR not only shows the influence of banks except for j but also incorporates non-linearity reflected in the shape of a link function f(.) as defined in equation (8). Härdle et al (2016) called it as the Tail-Event Driven Network risk with single index model. Dj|j shows the gradient that measures the marginal effect of covariates
evaluated at Rj,t = R˜j,t and the component-wise expression is Dj|j = {Dj|-j, Dj|M, Dj|Sj }T. Dj|-j shows the spillover effects across sample banks and NBFCs and characterise their networks. In our set-up, we only consider the partial derivatives of j with respect to the other banks and NBFCs (that is, Dj|-j ) and exclude the partial derivatives with respect to macro ( Dj|M ) and balance sheet (Dj|Sj ) variables as our main objective is to examine the interconnectedness using networks between banks and NBFCs. Further, the incoming and outgoing links of three groups’, namely government-owned banks (GOBs), privately-owned banks (POBs) and NBFCs are derived from the elements (Dj|i ) of weighted adjacency matrix calculated using Dj|–j. The sum

of columns shows the outgoing links and

sum of rows depicts the incoming links . The

individual elements expressed in absolute values reveal the pairwise connectedness and from these elements, a network is formed.

Third step: Unlike Adrian and Brunnermeie (2016) that calculates system return by taking the average market valued asset returns weighted by lagged market valued total assets, Härdle et al (2016) propose two broad indices weighted by market capitalisation of respective banks and NBFCs, namely systemic risk receiver (SRR) and systemic risk emitter (SRE). They do this to measure the systemic risk relevance of a financial institution by its total, in and out connections weighted by market capitalisation. The SRR for a bank j therefore defined as:


The SRE for a bank j is defined as


where ksIN and ksOUT are the set of banks and NBFCs connected with bank j (reference bank and NBFC) by incoming and outgoing networks at window s, respectively. MCi,s represents the market capitalisation of bank and firm i at the starting point of window s. |D˜sj|i| and |D˜si|j | are absolute partial derivatives which represent row (incoming) and column (outgoing) directional connectedness of bank and NBFCs j to i.

Results and Discussion

This section analyses the empirical results of tail-event driven risk model. The study begins with the analysis of the magnitude of total connectedness results shown in Figure 2 (p 63). The total connectedness shows increasing trend post 2008 as both connectedness and averaged λ increased sharply which could be linked to the global financial crisis (2008). The connectedness again takes an upward turn with a mild increase in average λ values during 2014 which corresponds to the periods of banking sector reforms particularly when the RBI restructured its norms with regard to the detection of NPAs and when the new system was put in place of reporting the stressed assets when the interest and principal are not paid for 30 days and 60 days.8 Before 2016, the interconnectedness takes a substantial dip suggesting the possible impact of information asymmetry and panic in the inter-bank and inter-institution borrowing and lending. The post-2016 rise could be linked to the confidence-building measures in the forms of recapitalisation and improved credit market conditions undertaken by the government.

To explore further, the study also calculates and plots the incoming and outgoing links among GOBs, POBs and NBFCs. Figure 3 shows the incoming links. It appears that the GOB received the maximum shocks post 2016 followed by NBFCs and then POB. Seemingly, the outgoing links representing the trigger of shocks from one entity to another indicate that post 2016, NBFCs are better compared to the GOB and POB. It captures the tumultuous phases of deteriorating health of NBFCs. The possible explanation could be because of the adverse loan market conditions and credit squeezing owing to the demonetisation at the end of 2016. The findings support the study of Acharya et al (2013), which suggested a considerably large share of banks in NBFC liabilities. It is noteworthy that the post-2016 trends clearly suggest the tumultuous phases of the Indian banking sector and post AQR conditions. The incoming and outgoing links reveal the phases of the subdued performance of GOBs and POBs as reported by the Financial Stability Report of RBI (2016). The FSR (2016) revealed the issues of reforms and recapitalisation of Indian banks and balance sheet slowdown.9

The total directional connectedness of the top 10 banks and NBFCs based on their magnitude is shown in Table 3. Among the groups, IFCI transmits the highest risk to all other banks and NBFCs. It also means that in the event of uneven developments at IFCI it may emit the relatively larger degree of shocks to all other entities. IFCI is followed by OBC, SREI and Yes Bank. It is noteworthy that among the top 10 risk emitting institutions four are NBFCs and six are banks (see panel A). It is further substantiated by panel C. Similarly, among banks, OBC is the highest risk emitting banks followed by YES, UBI and UCO (see panel B). The key takeaway from this analysis is that ranking of most of the banks and NBFCs seem appropriate as after the IL&FS episode, these institutions are already making efforts to address risk. Another takeaway is the presence of smaller banks in the risk-emitting categories, which also speak about the spread of the banking crisis. It also validates the choice of our model and the lowest quantile.

The table also shows the higher risk receiver banks and NBFCs at 1% quantile. Amongst, SREI is the most risk receiving NBFC followed by IFCI and MAGMA. Among the banks, OBC and YES Bank could be closely watched (see panel D). Panels E and F exhibit the top risk receiving banks and NBFCs respectively. Among NBFCs, SREI, IFCI and CENTRUM are the institutions to look for. What is most surprising is the appearance of OBC, DCB, and YES Bank among banks and IFCI, SREI and JMFIN from the NBFC list. The pairwise results may also add value to this ranking. Table 4 shows the pairwise directional connectedness of the top 10 banks and NBFCs. Panel A shows the rankings of banks and NBFCs based on their directional connectedness at 0.01 quantile. Among banks and NBFCs, IFCI is strongly connected with IDBI and DCB. YES Bank is connected with BAJAJ and LIC. This could possibly be explained with respect to LIC stake in YES Bank. Such results also make the choice of balance sheet variables appropriate. Among banks, YES Bank, IDBI and DHAB appear to be strongly paired with NBFCs. Similarly, among NBFCs, IFCI takes the prominent lead followed by M&M and HOUSING. GIC is bilaterally connected to the CANFIN followed by IFCI and DHFL (see panel E).

Tables 4 and 5 rank the banks and the NBFCs based on their directional spill overweighed by their dynamic size (market capitalisation). SRR and SRE rankings are critically important to map it with the systemically important rankings of banks and NBFCs of the RBI. Table 5 shows the top 10 Indian banks and NBFCs. Panels A & B exhibit the SRR, it appears that among banks, HDFC, ICICI, AXIS, SBI and YES are first five systemically important domestic banks, which are the same as the rankings of RBI. Since August 2015, the RBI has been publishing the list of the D-SIBs as part of its Financial Stability Report. As of April 2016, the D-SIB included SBI, ICICI and HDFC banks.10 Similarly, the SRR rankings of NBFCs include HOUSING, BAJAJ, LIC, SHRIT and CHOLA.11 The SRE rankings of banks include ICICI, HDFC, AXIS, YES and SBI. The SRE list of NBFCs include HOUSING, LIC, BAJAJ, SHRIT and M&M. The key takeaway from SRR and SRE rankings are: (i) the rankings are similar to that of the rankings of RBI; and (ii) YES and AXIS banks needs further investigation with regard to their exposure to different banking and NBFC domains. The rankings of NBFCs are as per our expectation as the ranking is based on the size of the NBFC.

To confirm this ranking, the study also plots the networks. Figure 4 (p 65) shows the overall network of sample banks and NBFCs. ANDB appears to be strongly connected to ALLA followed by OBC to UBI. As mentioned above, among NBFCs, IFCI shows strong interconnectedness with IDBI and DCB banks. GIC shows bilateral connectedness with CANFIN. Similarly, Figure 5 (p 65) exhibits the interconnectedness among banks. Confirming the results shown in Table 4, it appears that the direction of interconnectedness is ownership specific. For instance, GOBs exhibit stronger connection with GOBs and exhibit a weak interdependence with POBs. Similar pattern is observed with POBs. The prominently figured banks such as YES seem to be unilaterally connected with AXIS and ICICI. Seemingly, the study also plots the interconnectedness networks of NBFCs. It appears that the GIC and CANFIN bilaterally connect each other. GIC connected with CANFIN and LIC. IFCI connects with SREI and DHFL appears to be strongly connected with SREI and LIC (Figure 6).

Overall, it can be inferred from the above discussion that the banks which need further understanding are DCB, IDBI, and YES Bank with respect to their interconnectedness with NBFCs. Among NBFCs, IFCI, SREI, GIC and DHFL are the institutions, which may need further research.

To confirm the ranking of NBFCs based on their interconnectedness, the study analyses the case of IFCI, SREI and DHFL. Since 2016, IFCI is reeling under the pressure of losses. In September 2018, IFCI reported a net loss of ₹ 340 crore.12 SREI Infrastructure Financing Company is in deep trouble and even filed insolvency proceedings. Similarly, in September 2018, the shares of DHFL crashed to almost 40% on the concerns of possible default on outstanding debt repayment. Owing to IL&FS episode, the NBFC sector experienced an unprecedented contagion spread across firms, including Bajaj Finance, M&M Financial Services and Shriram Transport Finance and even to the Edelweiss Financial.13 For banks, SRR and SRE rankings are almost same as that of Verma et al (2019).


This study is interestingly designed to understand the NBFC crisis without considering the IL&FS and traces the sequence of events related to the RBI policy in 2016. The study explores the ultra-high dimensional set-up to capture the interconnectedness between banks and NBFCs at the lowest possible quantile 0.01(1%). The empirical results suggest that among banks, YES, DCB and IDBI are the most interconnected with major NBFCs. Among NBFCs, IFCI, SREI and the DHFL have the strongest exposure to banks. These results support the systemic rankings of RBI for the domestic banks and systemically important NBFCs. However, it is worth mentioning the limitations of this study particularly with respect to the choice of window size and the selection of quantiles. It could be addressed in future research to analyse this in Indian context.


1 (accessed on 12 January 2019).

2 (accessed on 12 January 2019).

3 Systemic risk deals with the risk spillover due to the failure or collapse of company, financial institution or industry in an economy.

4 See for details, and (accessed on 4 March 2019).

5 (accessed on 5 March 2019).

6 LASSO is widely used in Machine Learning (ML)

7 For further discussion, refer Härdle et al (2016).

8 (accessed on 10 march 2019).

9 See for details: (accessed on 5 March 2019).

10 For more details, refer: (accessed on 5 March 2019).

11 (accessed on 5 March 2019).

12 (accessed on 5 March 2019).

13 (accessed on 5 March 2019).


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Updated On : 29th Mar, 2019


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