Examining the Time Varying Spillover Dynamics of Indian Financial Indictors from Global and Local Economic Uncertainty

Abstract

The research aims to excavate the role of global (Fed Rate, Crude, Real Dollar Index) and endogenous economic variables (GDP and Consumer Price Index) in shaping the spillover amongst the major Indian Financial indicators, viz. Nifty Index, MCX Gold, USDINR, Govt. Bond 10Y maturity and agricultural index N-Krishi. To facilitate cross-comparison decomposition of time-varying spillover output generated from Time-Varying Vector Autoregression (TVP-VAR) with aggregation at three layers is performed. The research finds that Indian Financial Indicators are vulnerable to spillover shocks from global variables predominantly driven by Fed Rate and Real Dollar Index. USDINR turns out to be most sensitive to global shocks and transgresses the shock to other financial indicators. Importantly, persistently high inflation has brought volatility spikes in the directional spillover to financial indicators. Though spillover subsidence is observed post-2014, with an all-time high during GFC, a sudden spurt in all financial indicators has been observed post-Covid-19, with Govt. bonds showing a sporadic rise. An important observation relates to staunch spillover from GDP during GFC with reoccurrence post-Covid. Additionally, a closely knit spillover tie is observed among USDINR, N-Krishi, and Crude. The study is beneficial to RBI to proactively monitor the weakening rupee along with Fed tapering to manage the rising spillover post-Covid-19. The effort of RBI has to be reciprocated by the government in inflation targeting to reinforce the curbing efforts of rising shock spillover.

Introduction

The global financial crisis of 2008 has generated interest among policymakers, investors, and academicians on cross-asset spillover linkages covering equity, currency, commodity, and bonds. Umpteen studies circumventing cross-financial market spillover linkage have showcased different asset classes to cascade the shocks. However, off lately, researchers have focussed on excavating the endogenous and global macroeconomic variables providing a breeding ground for the transgression of crisis (Eichengreen et al., 1996; Berkmen et al., 2012; Jiang et al., 2022). More research attributed to the spread of crisis via trade channels or financial market, right from the contagion approach propagated by Forbes and Rigobon (2001), reciprocated further by Haidar (2012), Shikimi, and Yamada (2019). Theoretically, the interdependence of assets during the period of crisis is found to increase, and contagion/spillover caused due to turbulence in one asset class transmits to other assets class not even directly linked (Klößner and Sekkel, 2014). Notably, the interdependence spillover connectedness structure of asset classes covering equity, currency, commodity, and bonds is highly intertwined and includes several transmission channels (Singh et al. (2019). Besides the financial channel, macroeconomic channels are too found to play a pivotal role in spreading crises (Davidson, 2020).

Importantly, it was also observed that some assets like crude oil, fed rates, US stock market index, etc., are quick to blame for causing volatility in other asset classes. Eventually, these assets have been classified as global economic indicators. Their dramatic price movements not only have a profound impact on all four asset class segments but could considerably impact the other economic factors of developing nations like India. Additionally, heavy reliance on importing goods such as Crude and Gold drags the exchange rate into the picture. The volatility in INR is bound to send shock waves in the Indian Financial market, which reverberates through the system. In the long run, a relationship exists whereby shockwaves from these global economic indicators disseminate to local financial indicators via various channels, primarily via high-intensity idiosyncratic shocks, which global economic indicators transmit during periods of crisis. It has generated interest to know the degree of linkages across economic and financial indicators in times of non-crisis and whether this is different compared to financial crisis times. Though the Contagion effects within the same assets class are most apparent (Diebold and Yilmaz, 2014; Singh et al., 2018), intensification during crisis times is plausible, provided economic interconnectedness exists in parallel with market interconnectedness.

A strong economic interconnectedness of financial indicators with domestic economy has been explored umpteen for developed economies (Antonakakis et al., 2018). However, there are cases of interdependence of economic and financial indicators for developing economies too, having relatively higher liquid financial markets. Lakdawala (2021) while investigating impact of US Monetary policy on emerging financial markets find Indian exchange rate and stock market to be more responsive to Fed fluctuations. Some other pairwise studies of global variable of significance to India has been done such as Ghosh (2009), Ghosh and Kanjilal (2016) investigated Crude oil linkage with stock market. Some research incorporated more Indian financial indicators such as Bond, Forex, Stock and Gold by Roy and Roy (2017). Yet no such study exist that performs a cross comparison of global and endogenous variables of importance to Indian financial indicators, exploring their role in shock propagation amid crisis times.

For this research, we have factored in the major asset class important for the Indian economy and serve as a proxy for financial indicators. Nifty Index (Stock Index), USDINR (exchange rate), Govt. Bond 10Y (Fixed income), MCX Gold (Bullion market), and N-Krishi Index (for an agricultural commodity). Apart from Financial market variables, domestic macroeconomic variables factored in are the GDP at a constant price and Consumer Price Index (indicator for inflation level) have been taken. The global variables are the Fed rate, Real Dollar Index, Crude Oil, and SPX. The choice of global variables is driven by the economic relationship they bear either with India. In the context of India, the global economic indicators like crude oil, dollar exchange rates, fed rates, US stock market index, etc., play a pivotal role. Fed rates like Libor serve as a benchmark rate for interest rate revision by the Reserve Bank of India. Fed tapering often leads to interest rate revision, further shaping the interest earned on Govt. bonds. The next obvious choice is the inclusion of the United States stock index SPX, as the financial markets worldwide share interconnection. Moreover, keeping SPX with economic variables such as Fed Rate will aid in comparing cross-border spillover linkage via financial and macroeconomic determinants.

On the other hand, crude oil is quick to blame for causing volatility in the Indian economy. The ripples of shock spillover from crude oil profoundly impact Indian financial and banking markets. The same is evident from the fact that India, the sixth largest economy in the globe in terms of GDP and the second-largest emerging economy after China, imports more than 80 percent of its total crude oil consumption requirements every year. Since crude oil is primarily traded in foreign currency, namely the "Petrodollar," to withstand the short-term volatility of crude oil prices, a country like India requires a wide cushion in forex reserves. As crude oil price rises, so do its import bill, which negatively impacts the trade deficit. The widening of the trade deficits drags its local currency down further. As per the Reserve Bank of India (RBI)1  report, it is a settled proposition that for every $10 rise in crude oil, India's oil trade deficit rises by about $15 bn, which is roughly 43 bps of India's GDP.2 Furthermore, the RBI report informs that the rising crude oil price also affects the government's fiscal health and increases the government fiscal deficit in almost similar proportion. In addition, high crude oil prices drive up fuel prices and, in turn, transportation and logistics costs, which make domestic goods and services more expensive. Furthermore, a sustained rise in crude oil prices also affects inflation.

As we can observe, the inclusion of Crude has brought many financial and economic channels of shock transmission, viz., inflation and exchange rate. Hence Real Dollar index has been included as part of the global variable to capture the Spillover of dollar fluctuation. The inclusion of the Real Dollar index is also reinforced by the heavy dependency of India on Gold imports, which have risen in recent years. The connectedness approach considered to capture the shock spillover is Time Varying Vector Autoregression proposed by Antonakakis and Gabauer (2017, 2020). The model is an improvement in the workhorse model of Spillover proposed by Diebold and Yilmaz (2012), as a rolling window size does not restrict it for time-varying estimation. In order to incorporate the domestic macroeconomic variable GDP, our periodicity of data is limited to quarterly. Hence, TVP-VAR estimation comes in handy in time-varying shock spillover estimation. Additionally, the constraint of the arbitrary setting of the rolling window is resolved without any observation loss. The findings about the shock reverberation across the financial system could be further enhanced to capture the short-term response to shocks from the most significantly affecting variables via Impulse response analysis. However, in this research we restrict ourselves to evolution of spillover shocks with time, rather than how soon the shock dies out.

We further perform decomposition of shock spillover output, followed by the aggregation for cross-comparison. The decomposition is performed at three layers of granularity. The first decomposition followed by aggregation is at the macro level involving spillover within the financial indicators. After that, the decomposition of spillover from each global and domestic variable to financial indicators is followed by aggregation. Third, decomposition of directional spillover from each global and domestic variable on a pairwise basis. Noteworthy, other approaches to spillover estimation, such as cross-correlation, VaR, and copula, would restrict the decomposition as addendum and decomposition are not empirically viable with such estimations. Hence, cross-comparison would be curtailed. Additionally, we have focussed on exploring time domain spillover linkage, though frequency domain or a joint approach estimation can never be negated. Nevertheless, the findings may motivate us to explore the spillover linkage from other approaches too.

The remainder manuscript is divided into the following subparts. The first part discusses the relevant literature circumventing the spillover linkage of Indian financial indicators. After that, the following section performs exploratory analysis, primary data description, and required transformation for model application. The following section discusses the empirical methodology applied in the manuscript. After that, a discussion follows involving cross comparison at different decomposition layers. Finally, the manuscript concludes with a policy suggestion along with the future line of research.

Literature Review

The inclusion of macroeconomic channels for spillover propagation is not new for academia. Eichengreen et al. (1996) advocated the behavioral preference of investors for cross-border investments based on similarity/dissimilarity of macroeconomic fundamentals. With time, financial and economic channels of spillover linkage cropped up in academic literature (Berkmen et al., 2012; Lutchtenberg and Vu, 2015). Yarovaya et al. (2022) summarized that international contagion provides a channel for spillover propagation and is responsible for spillover intensification; however, their exploration should be done in tandem with other determinants involving local macroeconomic factors. Negating the concept of macroeconomic similarities, researchers (Adrian and Brunnermeier, 2011; Giglio et al., 2016; Silva et al., 2017; Gkillas et al., 2019) find spillage of shock waves from disturbed European market owing to the debt crisis. They conclude that a highly interconnected economic environment deems necessary for spillover to realize.

In the context of India, more studies have focused either on exploring the spillover linkage from international financial markets to Indian financial markets or inclusion of certain asset classes with significant economic importance to India, such as Crude. Ghosh (2009) explored the pairwise analysis of the financial indices vis-à-vis crude oil. Ghosh (2011) empirically tested the impact of price fluctuations in Crude over the Rupee. Later, Ghosh and Kanjilal (2016) investigated the co-movement between international oil prices and the Indian stock market. The study deploys the indirect exchange rate channel to study the cointegration between crude prices and the Indian stock market. They found the Indian stock market got integrated with international crude oil prices post 2009. Some researchers worked on a comprehensive scale incorporating more financial and economic indicators for the study. For example, Jain and Biswal (2016) explored the dynamic linkage between oil price, Gold, exchange rate, and the stock market in India. Apart from the crude oil crisis episodes, the study focused on finding the causality relationship among the asset class. However, a significant segment of the Indian economy, the commodity market, was missing in the analysis. Bouri et al. (2017) used the implied volatility perspective to investigate the causality among the same set of variables. The study finds the positive non-linear impact of implied volatilities of Crude and Gold on the Indian stock market. Roy and Roy (2017) brought commodity to the center stage of the study, as the inflation in food prices has never been an ending debate in India. They empirically tested the contagion and volatility spillover effect on the Indian commodity market by other major financial indicators such as Bond, Forex, Stock, and Gold. Applying DCC-MGARCH on daily return, they found the highest degree of contagion to commodity market is from stocks and most minor from Gold.

Quite recently, Lakdawala (2021) investigated the impact of US monetary policy on the Indian stock market, exchange rate, and 10Y Govt bond. He finds these asset classes more responsive to US monetary policy post-2000. In yet another study, Kim and Nguyen (2008) explored the reaction of the Australian Financial market to the US Fed. Notably, spillover linkage amongst the financial market can be done at a short tenure due to the availability of data high periodicity. Whereas macroeconomic fundamentals response captured via variables such as GDP and inflation have low periodicity. Hence, a comprehensive analysis incorporating economic variable suffer from data insufficiency. Cukierman (2019) performed a retrospective study about the economic repercussions post-GFC and explored the low inflation angle, thus questioning the policy decisions. Spillover linkage amid financial asset class has already been explored. Quite recently, Singh et al. (2019) investigated the complex volatility spillover transmission amid Crude and global asset indicators (GAIs) characterized by asset classes belonging to equity, government bonds, major currency pairs, agricultural commodities, and metals Gold. The empirical findings gave exciting insights into the idiosyncratic transmission amongst the GAIs with Crude.

Data Interpretation

For this study, the time-series data of benchmark financial indices and domestic macroeconomic and global macroeconomic indicators have been taken. The local financial benchmark indices taken are "Nifty 50", "USDINR," "MCX Gold," "Govt Bond 10Y maturity," and "N-Krishi Index." Each index is considered to represent the "Stock Index," "Exchange Rate," "Commodity," "Fixed Income," and "Agricultural Index." The data for the same has been collated from Bloomberg Terminal. Macroeconomic indicators considered are "GDP at constant price" and "Consumer Price Index" (a measure of domestic Inflation level) as domestic macroeconomic indicators. Whereas global macroeconomic indicators considered are "Fed Rate," "Crude Oil," "SPX index," and "Real Dollar Index." Noteworthy, Crude is an important commodity for India; hence amongst the global commodities, "Crude" has been factored in. Notably, data for macroeconomic indicators have been fetched from Federal Reserve Economic Data (FRED—St. Louis). The time frame for the data ranges from 2007Q2 to 2022Q1. The reason for such a time frame is the availability of data for the N-Krishi Index (erstwhile Dhaanya Index), available from 2007 onwards.

Figure 1 plots the Index value along with the logarithmic change for financial and macroeconomic indicators. As we can observe, 10Y Govt. Bond show relatively less volatility captured via a logarithmic change in comparison to the rest of the local financial indicators.

Fig. 1.

Time series plots and logarithmic change of Indian Financial Indicators and Macroeconomic Indicators. Note: The left y-axis represents the original data series, whereas right axis is logarithmic change except for Fed Rate and inflation Fed rate—First difference, inflation—% change in CPI value

Moreover, a commonality is observed in terms of sharp falls or spikes in financial indicators during times of economic downturns such as the GFC (2008–2009), Eurozone Sovereign Debt Crisis (2010), Chinese Slow down, and Oil crisis (2014–2015), Brexit and COVID-19 pandemic (2019–2020). Apart from co-movement among the financial indicators themselves, sporadic spikes are observed in global and domestic economic indicators. Though asymmetry in volatility is more prominently observed for global variables, implying they could potentially impact the Indian financial indicators more prominently. The co-movements indicate the existence of short-run impulse existing for volatility fluctuations amongst the financial indicators themselves as well as domestic and global variables. Nevertheless, medium to long-term impact is worth investigating.

Further exploration is done via descriptive statics and the correlation values. Notably, an adequate transformation has been done for the stationarity of the data series. The data series viz. Financial Indicators {Nifty, USDINR, Gold, N-Krishi, and Govt. Bond}, GDP, Crude Oil, Real Dollar Index, and SPX have logarithmic changes, whereas the first difference for Fed rate and Percentage change for CPI to have inflation rate has been performed. Table 1 represents the descriptive statistics. We can observe that the highest dispersion across the mean is for Crude Oil followed by Nifty, whereas central tendency is observed highest for N-Krishi, SPX and Gold. Though, the values remain close to zero for all the data series. The ADF test shows the data series to be I(0), thus paving the way for multivariate analysis while fulfilling the assumption. Table 2 presents the Pearson correlation matrix between the variables. Amongst the Financial indicators, a high negative correlation is observed between USDINR-Nifty and USINR-Gold. The economic linkage between Gold and INR relates to India's heavy reliance on Gold Imports, whereas co-movement with the Nifty is obvious. With domestic indicators, the highest correlation is observed for Govt Bond-GDP, whereas concerning global variables, Nifty, USDINR, and Gold show decent correlation with almost all. Interestingly, Crude Oil also shows a significant correlation with N-Krishi, showcasing the importance of Crude in the context of the Indian economy.

Table 1.

Descriptive summary statistics of log-return series of benchmark financial indies and global and local macroeconomic indicators

	NIFTY	Gold	USDINR	Govt. Bond	N-Krishi	GDP	Inflation	Fed	Crude	Real Dollar Index	SPX
Mean	0.0160	0.0191	0.0093	0.0026	0.0315	0.0148	0.0176	− 0.0008	0.0070	0.0025	0.0193
Standard error	0.0176	0.0093	0.0049	0.0112	0.0099	0.0102	0.0019	0.0005	0.0336	0.0039	0.0112
Median	0.0190	0.0178	0.0034	0.0022	0.0309	0.0240	0.0186	0.0001	0.0391	0.0032	0.0349
Standard deviation	0.1361	0.0718	0.0376	0.0868	0.0768	0.0786	0.0145	0.0037	0.2600	0.0299	0.0864
Sample variance	0.0185	0.0052	0.0014	0.0075	0.0059	0.0062	0.0002	0.0000	0.0676	0.0009	0.0075
Kurtosis	1.8105	1.1911	− 0.0866	18.9912	3.4799	7.1178	1.3516	9.8013	5.4644	2.2497	1.5683
Skewness	− 0.4617	− 0.5967	0.4310	2.4331	1.3731	− 1.7847	0.5035	− 2.9478	− 1.7825	0.8445	− 1.1225
Range	0.8123	0.3845	0.1566	0.7823	0.4105	0.5486	0.0807	0.0197	1.5987	0.1635	0.4376
Minimum	− 0.4040	− 0.2227	− 0.0654	− 0.2870	− 0.0876	− 0.3459	− 0.0124	− 0.0165	− 1.0162	− 0.0483	− 0.2556
Maximum	0.4084	0.1618	0.0911	0.4953	0.3229	0.2026	0.0683	0.0032	0.5824	0.1152	0.1819
ADF	− 5.6241	− 3.5672	− 5.1894	− 4.3274	− 3.7567	− 4.5641	− 3.2523	− 3.6459	− 4.7242	− 4.9477	− 4.5256

For Fed rate (first difference) and for Inflation (% change) has been taken for stationarity

Table 2.

Correlation matrix of log-return series of benchmark financial indies and global and local macroeconomic indicators

	NIFTY	Gold	USDINR	Govt. Bond	N-Krishi	GDP	Inflation	Fed	Crude	Real Dollar Index	SPX
NIFTY
Gold	0.216
USDINR	− 0.721	− 0.383
Govt. Bond	0.136	− 0.035	− 0.167
N-Krishi	0.154	0.072	− 0.024	− 0.001
GDP	− 0.049	− 0.077	− 0.165	0.538	− 0.093
Inflation	0.016	− 0.014	0.233	0.118	0.164	− 0.065
Fed	0.456	− 0.155	− 0.187	0.140	0.138	0.027	− 0.038
Crude	0.519	0.189	− 0.243	− 0.103	0.208	− 0.230	0.016	0.294
Real Dollar Index	− 0.498	− 0.504	0.593	− 0.102	0.066	0.009	0.010	0.037	− 0.431
SPX	0.681	0.013	− 0.435	0.048	0.174	− 0.097	0.167	0.437	0.652	− 0.393

For Fed rate (first difference) and for Inflation (% change) has been taken for stationarity

Noteworthy, the initial exploratory analysis showcases that empirical investigation of Indian financial indicators vis-à-vis domestic and global economic variables deems necessary. Importantly, we aim to explore how the shock to the variables, especially the economic variable, reverberates through the system. Through Impulse Response, we can have a short-run response to shocks. In contrast, Forecast Error Variance Decomposition will estimate the share in the variance of financial indicators by the economic variables. Thus, it would aid in cross-comparison too. Since the shock contribution evolves with time, a time-varying VAR model would fit appropriately. The following section explores the methodology behind the same. In addition, a decomposition of the shock spillover has been performed to segregate the net shock from domestic and global economic variables.

Methodology

Time Varying Parameter Vector Autoregression (TVP-VAR)

TVP-VAR methodology has been adopted to capture the time varying parameter of Vector Autoregression (Antonakakis and Gabauer 2017), which serves as an extension Generalized Forecast Error Variance Decomposition (GFEVD) measure proposed by Diebold and Yilmaz (2012). It incorporates stochastic volatility Kalman Filter estimation. Mathematically it can be deduced as following

$${\text{Y}}_{\text{t}}={\alpha }_{\text{t}}{\text{X}}_{\text{t}-1}+{\alpha }_{\text{t}}{\alpha }_{\text{t}}|F_{t-1}\sim N(0,{\alpha }_t)$$

$$Vec({\alpha }_{\text{t}})=Vec({\alpha }_{\text{t}-1})+v_t{v}_t|F_{t-1}\sim N(0,{\alpha }_t)$$

where, Y_i i ε [1…N] is a N × 1 dimensional vector, X_i is a N_p × 1 dimensional, whereas the parameter α_i forms a matrix with dimension (N × N_p). The error matrix captured in yet another column vector ε_i with dimensions (N_p × 1), whereas covariance matrix for the same is captured in σ_t. Whereas, Vec(α_i) and v_i are $N_p^2$ × 1 dimensional vector along with the covariance matrix captured in ω_i with dimensions as $N_p^2\text{x}{N}_p^2$.

Thereafter the GFVED with H-step forecast horizon is estimated with VAR transformed to its moving average

$${\text{Y}}_{\text{t}}=\sum _{j=0}^{\infty }{L}^{\prime }{W}_t^j\text{L}{\in }_{t-j}$$

$${\text{Y}}_{\text{t}}=\sum _{j=0}^{\infty }{A}_{\mathit{it}}{\in }_{t-j}$$

where L = [I_N,…..,0_p]’ is an N_p × N dimensional matrix, and W = [α_t; I_N(p-1), 0 _{N(p-1) x N}] is an N_p × N_p dimensional matrix and A_it is N × N dimensional matrix.

$$\text{GFEVD variance share estimated as}{\mathrm{\Phi }}_{ij,t}^g\left(\text{H}\right)=\frac{{\sum }_{t=1}^{H-1}{\mathrm{\Psi }}_{ij,t}^{2,g}}{{\sum }_{j=1}^N{\sum }_{t=1}^{H-1}{\mathrm{\Psi }}_{ij,t}^{2,g}}$$

$$\text{Total Connectedness}{C}_t^g\left(\text{H}\right)=\frac{{\sum }_{i,j=1,i\ne j}^N{\mathrm{\Phi }}_{ij,t}^g}{N}$$

$$\text{TO Connectedness}{C}_{i\to j,t}^g\left(\text{H}\right)=\frac{{\sum }_{j=1,i\ne j}^N{\mathrm{\Phi }}_{ji,t}^g\left(H\right)}{{\sum }_{j=1}^N{\mathrm{\Phi }}_{ji,t}^g\left(H\right)}$$

$$\text{FROM Connectedness}{C}_{j\to i,t}^g\left(\text{H}\right)=\frac{{\sum }_{j=1,i\ne j}^N{\mathrm{\Phi }}_{ij,t}^g\left(H\right)}{{\sum }_{j=1}^N{\mathrm{\Phi }}_{ij,t}^g\left(H\right)}$$

$$\text{NET Connectedness}-\left\{\text{TO}-\text{FROM}\right\}=C_{i\to j,t}^g\left(\text{H}\right)-C_{j\to i,t}^g\left(\text{H}\right)$$

Decomposition of Connectedness Measure for Internal, Domestic and Global Shocks

The technique is based on decomposition proposed by Gabauer and Gupta (2018). Let (H) denote the static connectedness spillover sub-matrix deduced after GFEVD.

where, I_i represent the Financial Indicator for India i.e. Nifty Index, MCX Gold, USDINR, Govt. Bond and N-Krishi Index. D_i reflects the domestic macroeconomic variable viz. GDP and Inflation. G_i reflects the Global macroeconomic variables taken for the study i.e., Fed Rate, SPX, Real Dollar Index, and Crude Oil. Whereas C_ij represents pairwise directional spillover values for any time “t” estimated from TVP-VAR. For decomposition first we replace the diag(C_ii) with NULL thereafter aggregate the pairwise directional spillover for Financial Indicators, Domestic Variables and Global Variables with respect to Financial Indicators.

$$\begin{array}{c}\text{Pairwise Aggregate Directional Spillover from domestic or global}\\ \text{variable to Financial Indicators}=\sum C_{i,j},\text{j}\epsilon \left[\text{n}+1\dots .\text{m}\dots .\text{p}\right]\\ \\ \end{array}$$

$$\text{Aggregate Internal Spillover}\left(\text{AIS}\right)=\sum _{i=1}^k\sum _{j=1}^n{C}_{\mathit{ij}}$$

$$\text{Aggregate Domestic Spillover}\left(\text{ADS}\right)=\sum _{i=1}^k\sum _{j=n+1}^m{C}_{\mathit{ij}}$$

$$\text{Aggregate Global Spillover}\left(\text{AGS}\right)=\sum _{i=1}^k\sum _{j=m+1}^p{C}_{\mathit{ij}}$$

$$\text{Henceforth},\text{TOTAL Spillover received by Indian Financial Indicators}=\text{AIS}+\text{ADS}+\text{AGS}$$

Results

Static Spillover Decomposition Analysis

Table 3 shows the connectedness averaged from 2007 Q2 to 2022 Q1 as we can observe that within the financial indicators Nifty Index is the most vital transmitter, followed by USDINR. At the same time, Gold, followed by N-Krishi, is the strongest receptor among the financial indicators. Noteworthy, a high reception or transmission of shock spillover is indicative of high market interconnectedness. As observed, N-Krishi, created off lately and an indicator of the Agricultural index, has little market interconnectedness compared to other financial indicators, offering hedging opportunities with agricultural commodities in the portfolio. However, N-Krishi's high spillover connectedness relationship with Bond alarms the investors to have agricultural commodities as well as fixed asset investment during times of loom in their portfolio. Another indicator pair to watch out for is USDINR and Gold. Theoretically, heavy dependence on Gold as a safe investment has widened the CAD for India. Nevertheless, at the same instant, rising rupee (Fig. 1), volatility would alleviate the adverse economic relationship between USDINR and Gold share. The spillover matrix ascertains that, on average, volatility in INR is more vicious than rising gold prices coupled with gold imports, as we can witness the pairwise spillover relationship between them to be highly unbalanced.

Table 3.

Static connectedness statistics of benchmark financial indies and global and local macroeconomic indicators

	NIFTY	Gold	USDINR	Govt. Bond	N-Krishi	From within self	GDP	Inflation	Fed	Crude	Real dollar index	SPX	FROM
NIFTY	19.26	5.06	16.67	6.96	4.3	32.99	3.13	7.44	8.74	10.05	8.32	10.06	80.74
Gold	17.39	7.53	15.77	7.48	4.75	45.39	3.17	7.38	8.17	9.81	8.62	9.94	92.47
USDINR	19.1	5.26	17.45	7.02	4.27	35.65	2.48	7.6	8.28	10.01	8.65	9.89	82.55
Govt. Bond	11.51	5.63	7.98	18.69	10.58	35.7	6.03	5.95	11.87	7.36	4.65	9.75	81.31
N-Krishi	12.09	4.87	9.59	10.67	14.23	37.22	6.27	6.24	12.17	7.65	5.77	10.45	85.77
TO Within Self	60.09	20.82	50.01	32.13	23.9	Total
NET	27.1	− 24.57	14.36	− 3.57	− 13.32	37.39
GDP	8.81	4.1	7.35	9.52	10.62		14.81	3.24	13.76	9.16	4.22	14.4	85.19
Inflation	11.23	4.8	10.29	7.77	8.25		9.02	11.42	10.27	8.97	6.19	11.78	88.58
Fed	12.58	4.64	9.18	10.41	11.17		6.93	4.85	15.32	8.44	5.15	11.32	84.68
Crude	16.33	6.24	14.52	7.5	6.24		4.72	6.88	8.53	10.51	8.2	10.33	89.49
Real Dollar Index	17.54	5.69	15.06	8.18	7.29		3.57	7.17	8.43	9.24	8.65	9.17	91.35
SPX_	16.1	4.73	14.5	7.23	4.47		4.24	5.84	10.38	11.77	7.37	13.38	86.62
TO	142.69	51.03	120.92	82.76	71.95		49.56	62.58	100.59	92.46	67.15	107.07	Total
NET	61.96	− 41.44	38.36	1.45	− 13.82		− 35.64	− 26.01	15.91	2.97	− 24.2	20.45	86.25

Note: The values in the cell reflect pairwise directional connectedness; row values for a variable reflect spillover shocks received by it; column values reflect the spillover transmission sent by it

In the context of spillover received by the financial indicators from domestic and global factors, we observe a substantial upshoot in “Total” spillover connectedness when economic variables are factored in. It showcases that, in aggregate economic indicators supersede the spillover that financial indicators send amongst themselves. Thus, macroeconomic conditions in India as well as globally are to be watched more closely while investing in Indian financial markets. However, the spillover from global variables supersedes domestic economic indicators in the same instance. It showcases that the sensitivity of Indian financial indicators is more inclined toward the global economic situation. The high sensitivity of Foreign Institutional Investors (FIIs) to economic recovery in the west indicates the same. Nevertheless, at the same instant, uncertain domestic economic conditions invite fewer FIIs than expected in the Indian financial market. Appendix (I) plots the histogram for Net FIIs inflow/outflow for India, where “Net” refers to the difference between Inflow and Outflow. The Net FIIs distribution has thicker left tails with very few high extrema on the right side. It signifies that the overall occurrence of outflow is more than the inflow.

However, another global variable causing turmoil in the interest rate of a nation is the Fed rate. Fed tapering post-GFC has not only made the interconnected market more volatile but, at the same instant, has macroeconomic repercussions. A study by Kim and Nguyen (2008) investigates Fed rate news on the Australian financial market, where they find a significant impact on the Australian debt, equity, and forex markets. In the case of India, on average, we find the same. The agricultural commodity index bears the brunt, too, along with Gold. While Gold offers an alternative for a safe investment, the commodities bearing low correlation with other asset classes offer hedging opportunities to mitigate loss. Since interest rate tapering leads to an overnight flight of capital, the risk mitigation alternatives executed while investing in Indian financial markets are done away with the withdrawal of capital.

Crude, a crucial commodity for India, both in an economic and political sense, sends spillover to USDINR and the Nifty Index. Crude, like Gold, induces pressure on CAD, further propagating weakening INR instead of rising fuel exports. Thus, Crude-Gold-USINR time-varying relationship is worth investigating to figure out the direction and quantum of spillover pairwise. Spillover shocks from SPX to Indian financial markets are pretty obvious. However, an interesting observation is strong shock spillover to the newly created N-Krishi index too. Though on average, each financial indicator is getting affected. Notably, the static decomposition illustrates the relationship averaged over time. However, quite a sometimes inference from averaged value could be dubious. Hence for a more vibrant and promising relationship, a time-varying spillover relationship comes in handy. The following section covers the same.

Time Varying Spillover with Decomposition

The section is subdivided into four sub-sections. The first sections focus on the decomposition of Total spillover shocks into “Aggregate” spillover within the financial indicators due to self-interconnectedness (AIS), followed by “Aggregate” spillover from Domestic economic indicators to the Indian Financial Indicators (ADS) and “Aggregate” spillover from Global economic indicators to the Financial Indicators (AGS). The second section performs a cross-comparison of aggregate spillover from global and Domestic variables to financial indicators. The third sub-section deals with Pairwise Directional Spillover from each Global and Domestic variable with each financial indicator. The last subsection investigates the Net Pairwise Directional Spillover amongst the Financial Indicators.

Aggregate Internal Spillover vs Aggregate Domestic Spillover vs Aggregate Global Spillover

Figure 2 presents the time-varying spillover comparison for the aggregated spillover within the system {Financial Indicators} (AIS), Aggregate Spillover from Domestic indicators to Financial Indicators (ADS), and Aggregate Spillover from Global indicators to Financial Indicators (AGS). Interestingly, in contrast to the observation in Table 2, the AIS and AGS cross each other twice during the time frame, with AIS having a lead amid GFC 2008–2009 and up till late 2013. After that, the AGS dominated AIS as well as ADS, though ADS throughout the time frame has remained significantly lower. It further validates that Indian Financial Indicators push each other volatility due to cross-market interconnectedness and are significantly impacted by the global macroeconomic indicators. Another observation is the widening of the aggregate spillover gap between AIS and AGS post-COVID-19 era, implying a radical increment in spillover pattern from outside. Another area worth investigating is an all-time high spillover within the system, i.e., AIS between 2010 and 2012. Late in 2012, AIS subsided and went below the AGS. Nevertheless, the observation paves the way to explore further the contribution of shock proportion by each variable constituting the domestic and global indicators.

Fig. 2.

Cross comparison between the “Aggregate” Spillovers

Aggregate Directional Pairwise Spillover Analysis from Domestic and Global variables to Financial Indicators

Figure 3 aggregates the directional spillover from each domestic and global variables to financial indicator and plots the same. Mathematically it can be defined as.

Fig. 3.

Aggregate directional spillover from domestic and global economic variables to indian financial indicators

${\sum }_{i=1}^k{C}_{i,j}$, j ε [n + 1….m….p],where C_i,j is the directional pairwise “TO” spillover connectedness from a domestic or a global variable. Notably, aggregation is the summation of directional spillover from Domestic and Global variables to Financial Indicators. Asymmetry is observed in terms of any individual variables leading to sending spillover shocks prior to 2012. However, post-2012, the Fed rate, and Real Dollar Index dominate in terms of Aggregate spillover. A sporadic rise around 2008–2009 followed by a sharp dip around 2010 is observed for Real Dollar Index before stabilization post-2014. In a recent study, Kim and Nguyen (2008) explored the domestic channels of contagion propagation from one country to another other than the financial market. A high shock spillover from the Real dollar index indicates alleviation shock spillover happening via domestic factors such as GDP and inflation. As observed from Fig. 4, almost all the global variables make the shock spillover to domestic variables around GFC turbulent.

Fig. 4.

Directional spillover from global economic variables to domestic variables

Nevertheless, at the same instant, a similar pattern is observed in terms of shock spillover from domestic variables GDP and inflation sporadic with high fluctuations up till 2010. However, another finding in the same research highlights that high inflation levels during crisis time could make a country more vulnerable to external crises. In the case of India, we witnessed the same during GFC, with high inflation levels (Fig. 1). However, the role of GDP in crisis propagation has to be investigated further in the context of India. Yet the high inflation level accompanied by heavy spillover from inflation opens a new gate for exploration of the influence of inflation in shock propagation in the context of India.

In general, during the time frame around GFC (2008), Eurozone sovereign debt crisis (2010), and before the Chinese slowdown (2014–2015), the shock spillover is more turbulent, with sporadic rise and fall for each variable. Fed tapering and recovery in the US market accompanied by a strengthening Dollar vis-à-vis other currencies led to an overnight flight of capital by Qualified portfolio investors, thus inducing a fall in local financial indices. Due to market interconnectedness, the situation gets exaggerated due to feedback spillover transmission within the financial indicators (Table 3, quadrant 1). Crude, an important commodity with relatively high shock spillover observed on average (Table 3), shows signs of shock subsidence post-2014. A typical pattern is an uprise in spillover contribution by all the variables around the 2020 COVID -19 pandemic era. Yet, hastened dollar outflow from India due to market recovery in the US, accompanied by a strengthening dollar, would widen the CAD; thus, impact of Crude could exaggerate. Subsequently, revision of interest rate will hurt the inflation more, causing goods to be dearer to Indian customers and impacting the interest earned on Govt. Bond. Thus, cascading events could trigger engulfing the domestic and financial indicators of the Indian economy. The findings pave the way to explore the pairwise impact of domestic and global variables on Financial Indicators, covered in the next section.

Pairwise Directional Spillover Analysis from Domestic and Global variables to Financial Indicators

Figure 5 plots the pairwise directional spillover from Domestic and Global variables to the Financial Indicators, computed from the time-varying “TO” connectedness of the TVP-VAR output. It indicates how much shock transmission happens from Var 1—> Var 2, where Var 1 includes the Domestic and Global variables, whereas Var2 includes the Financial Indicators. Most importantly, the empirical finding should theoretically justify the economic linkage we observe from the plots. On a directional pairwise basis, the most sensitive financial indicator is the exchange rate, i.e., USDINR. It is not only sensitive to domestic economic shocks, but it is also significantly affected by global indices such as SPX, Real Dollar Index, and Crude price fluctuations. Like, Inflation, depreciation of local currency could also create a cascading effect, thus triggering the effect of the crisis.

Fig. 5.

Pairwise directional spillover from domestic and global variables to financial indicators

A second common observation is subsidence in the volatility of spillover pattern post-2014, indicative of the Financial Indicators being less uncertain amid recent times. Though spillover from Inflation remains an exception, volatility spikes materialize more often throughout the time frame. Despite subsidence in spikes in shocks, during the COVID-19 pandemic, a sudden spurt in shock spillover is again observed for all the financial indicators, with the highest spurt observed in Govt. Bond. An alarming observation co-occurring is the strengthening dollar, rise in inflation level in India, and high sensitivity of USDINR to global variables, especially post COVID-19, paves the way to investigate the vulnerability of the Indian economic system. Notably, investment in Govt. Bond often witness a rise in case of economic distress, often reflected via an inverted yield curve. The shock spillover spurt in govt bonds from domestic as well as global variables is indicative of a possible radical shift in investment and more trust in long-term fixed income instruments over uncertain macroeconomic prevalence.

On the other hand, the N-Krishi index, a barometer of agricultural commodity prices, receives comparatively less shock spillover from global as well as domestic variables. Still, an iota of doubt persists about commodity index fallout if the situation ripens enough to enhance the vulnerability of the Indian economic system. Crude oil, an important commodity in India’s context, is not lagging behind other variables in sending shock waves to financial indicators, especially the Nifty, followed by USDINR. Notably, both Gold and Crude heavy imports and spiraling prices put pressure on the USD. However, at the same instance, strengthening the dollar makes the import dearer; as a result, spillover has to be observed. On the contrary, a sharp dip in Crude oil prices post-2014 has offset the pressure-induced via the strengthening dollar. As a result, spillover from Crude to USDINR is not staunch.

Though Haile and Pozo (2008) and Dasgupta et al. (2011) find less significance in the financial market spread of contagion from one nation to another, instead advocate the macroeconomic fundamentals to route the same across the economy, the investigation of pairwise directional spillover amongst the financial indicators deems necessary. However, we have witnessed the role of global and domestic macroeconomic variables in spillover intensification. The following section covers the net directional pairwise within the financial indicators.

Net Pairwise Directional Spillover Analysis Within Financial Indicators

Figure 6 plots the net pairwise directional spillover between the financial indicators themselves. It has been computed as the difference between the “TO” and “FROM” spillover connectedness for a variable pair {Var 1, Var2}. Var 1 and Var 2 here represent the financial indicators. For the Va1-Var2 pair, the density plot above the zero lies signifies that Var1 is a net transmitter for that time frame as we can observe that apart from the time frame 2010–2014, Nifty is a net receptor with all the other financial variables. For this time frame, the TOTAL spillover of the system captured via Aggregate Internal Spillover (refer, Fig. 2) was an all-time high. However, for this time frame, Nifty is a net receptor of shock. However, over the whole-time frame, the Nifty is the strongest transmitter of shock in the Indian Financial System, followed by USDINR. Importantly, N-Krishi shows high spillover connectedness with USDINR and Bond. Yet, in the same instance, we can observe the tightly knit spillover connectedness between Crude and USDINR (refer to Fig. 5B). In a study by Singh et al. (2019), they find Commodity and exchange rate of nations share a strong spillover connectedness with Crude. The observation here further facilitates excavating the linkage amid Crude-N-Krishi-USDINR.

Fig. 6.

Net pairwise directional spillover within financial indicators

Thus, we observe that, apart from the Spillover shocks from macroeconomic Global and Domestic variables, spillover is also observed amongst the Financial Indicators. However, post-GFC, there is general subsidence in the internal spillover. Nevertheless, the pandemic era of COVID-19 saw a sharp spurt in terms of spillover reception from Global and Domestic variables; what worries more is the significant spillover in the continuum from Real Dollar Index and Fed rate, coupled with high inflation. Consequently, Govt. Bond shows a highly sharp spike post-COVID-19, indicative of possibly strong linkage with the global variables. For policymakers, this could be an alarm bell, as a crisis trigger could traverse financial markets owing to shaky domestic and global macroeconomic situations.

Conclusion

Numerous studies related to spillover linkage between financial markets of a nation have been done; however, quite recently, scholars have garnered attention to study the role of global and endogenous macroeconomic factors in cascading the effect of the crisis. In the context of India, scant literature exists that explores the spillover linkage amongst the major financial indicators of India regarding global and domestic endogenous macroeconomic factors. This manuscript contains five major Financial Indicators of the Indian financial market, viz. Nifty Index, USDINR, MCX Gold, Govt. Bond 10Y maturity and agricultural commodity index N-Krishi has been explored vis-à-vis four major global variables important for the Indian economy, i.e., Fed rate, Crude Oil, Real Dollar Index, and SPX (US stock index). Apart from it, two endogenous economic variables viz. GDP and Consumer Price Index (indicator for inflation) have been factored in too. To capture the time-varying evolution of spillover parameters, “Time-Varying Parameter Vector Autoregression (TVP-VAR” has been used. Further, the spillover output has been decomposed at three layers of granularity. The first segregates the Aggregate spillover within the Financial Indicators from Aggregate spillover from Global and Domestic variables. The second decomposition Aggregates the Directional Spillover from each Global and Domestic variable to Financial Indicators for cross-comparison. The third decomposition further captures the pairwise directional spillover connectedness from each Global and Domestic variable to each financial indicator. Notably, the decomposition approach aids in cross-comparison at different layers and systematically dissects from macro underpinnings up to the micro level. The findings indicate that Indian financial markets are more vulnerable to global macroeconomic factors, primarily Fed Rate and Real Dollar Index. Even though subsidence of spillover post-GFC and Eurozone sovereign debt crisis, a continuum increase in spillover is observed for Fed and Real Dollar index post-2014. USDINR turns out to be the most sensitive financial indicator to global shocks, coupled with the strong transmitter of shock within the Financial Indicators. In the context of Domestic variables, the study finds persistent high inflation levels causing volatility spikes in spillover patterns from inflation to Financial Indicators. Quite significantly, frequent Fed tapering hurt the inflation more with a fall in interest rate observed in Govt. Bonds too. During COVID, a spurt in spillover from domestic variables to financial indicators has been observed. It has been reciprocated with a sharp spike in spillover in Govt. Bonds and reoccurrence of volatility spikes in spillover from inflation. An interesting finding is a sharp spike in shock spillover from GDP to financial indicators. With spikes around GFC and a spurt in Bond during COVID paves the way to investigate GDP's role in channelling financial contagion in India's context. Studies related to other nations have found less significance of GDP as a contributing factor in a cascading crisis, unlike inflation (Jiang et al., 2022). Another observation is the tight-knit spillover connection amid USDINR—N-Krishi—Crude—Gold, which aligns with the study by Singh et al. (2019). The study opens gates for the policymakers and regulators of the Indian Financial markets and the Reserve Bank of India (RBI) to closely monitor the building spillover intensification post-COVID-19. The combined role of global and domestic economic factors in cascading the effect of the Crisis (GFC) has been witnessed, which remained persistent until 2014. With conditions adverse to India, such as rapid Fed Tapering and depreciating Rupee, a proactive intervention by the RBI to tame the Rupee volatility along with the revision of interest rate has to be done to stop the crisis before fire build up again. Nevertheless, the Central government should focus on inflation targeting as persistently high inflation offers a breeding ground for contagion to affect more. Most importantly, in the case GDP plays a significant role in channelizing crisis, ignorance of global and local macroeconomic conditions could trap India into stagflation.

Appendix I

Histogram of NET FIIs.

Data Source: Bloomberg

Net = {Inflow – Outflow}

Data availability

The data for the Indian Financial Indicators {Nifty, Gold, USDINR, Government Bond, NKrishi}, Crude Oil, SPX has been collected and collated from Bloomberg. Note: Bloomberg is licensed. The data for Macroeconomic indicators {Fed Rate, Real Dollar Index, GDP, Consumer Price Index} has been sourced from Federal Reserve Economic Data (FRED-St. Louis) which is open source. Complete data used for empirical analysis can be shared on request.