Stock market volatility may be a function of company, industry, or world over information made public. The present study has investigated the volatility of Indian banking sectoral indices with the general banking index for two shocking events: the Sub-prime crisis and COVID-19. A comparative analysis of both the shocks leading to these indices’ volatility has been conducted using symmetric and asymmetric models. This study’s findings show that these indices’ volatile behaviour has been strong enough to persist in the market with the leverage effect present during the sub-prime crisis. This effect disappeared for Nifty Bank Indices and Private Sector Bank Indices as compared to Public Sector Undertaking Bank Indices during COVID-19 (probably because the pandemic is not over yet). With GARCH and EGARCH models, the study suggests that the investors may use the diversification approach, in the long run, to safeguard their portfolio values to survive from global shocks.
AlexanderC. (2009) Practical financial econometrics. John Wiley & Sons, Ltd.
2.
AnoopP., ParabN., & ReddyY. V. (2018). Analyzing the impact of demonetization on the Indian stock market: Sectoral evidence using the GARCH model. Australasian Accounting, Business and Finance Journal, 12(2), 104–116.
3.
BaeleL. (2005). Volatility spillover effects in European equity markets. Journal of Financial and Quantitative Analysis, 40(2), 373–401.
4.
BernardinoW., BritoL., OspinaR., & MeloS. (2019). A GARCH-VaR investigation on the Brazilian sectoral stock indices. Brazilian Review of Finance, 16(4), 573–610.
5.
BhatS. A., & NainM. Z. (2014). Modeling the conditional heteroscedasticity and leverage effect in the BSE sectoral indices. I.U.P. Journal of Financial Risk Management, 11(2), 49.
6.
BhatiaP., & RamasubramanianH. (2019). Cointegration between sensex and other popular indices: A decadal study. FIIB Business Review, 8(2), 108–117.
7.
BhattaraiS., ChatterjeeA., & ParkW. Y. (2020). Global spillover effects of U.S. uncertainty. Journal of Monetary Economics, 114, 71–89.
8.
BirauR., TrivediJ., & AntonescuM. (2015). Modeling S&P Bombay Stock Exchange BANKEX index volatility patterns using GARCH model. Procedia Economics and Finance, 32(1), 520–525.
BoswijkH. P., & Van der WeideR. (2011). Method of moments estimation of Go-GARCH models. Journal of Econometrics, 163(1), 118–126.
11.
BrooksC., BurkeS. P., & PersandG. (2001). Benchmarks and the accuracy of GARCH model estimation. International Journal of Forecasting, 17(1):45–56.
12.
BrownS. J., GoetzmannW. N., & KumarA. (1998). The Dow theory: William Peter Hamilton’s track record reconsidered. The Journal of Finance, 53(4), 1311–1333.
13.
BrunettiC., ScottiC., MarianoR. S., & TanA. H. (2008). Markov switching GARCH models of currency turmoil in Southeast Asia. Emerging Markets Review, 9(2), 104–128.
14.
ChakrabartyA., DeA., & BandyopadhyayG. (2015). A wavelet-based MRA-EDCC-GARCH methodology for the detection of news and volatility spillover across sectoral indices—Evidence from the Indian financial market. Global Business Review, 16(1), 35–49.
15.
CheongC. W. (2008). Time-varying volatility in Malaysian stock exchange: An empirical study using multiple-volatility-shift fractionally integrated model. Physica A: Statistical Mechanics and its Applications, 387(4), 889–898.
16.
CheungY. W., & LaiK. S. (1995). Lag order and critical values of the augmented Dickey–Fuller test. Journal of Business & Economic Statistics, 13(3), 277–280.
17.
DeyS., & SampathA. (2020). Returns, volatility and spillover—A paradigm shift in India?The North American Journal of Economics and Finance, 52, 101110.
18.
DrakosK. (2010). Terrorism activity, investor sentiment, and stock returns. Review of Financial Economics, 19(3), 128–135.
19.
ElyasianiE., MansurI., & WetmoreJ. L. (2010). Real-estate risk effects on financial institutions’ stock return distribution: A bivariate GARCH analysis. The Journal of Real Estate Finance and Economics, 40(1), 89–107.
20.
EngleR. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 50, 987–1007.
21.
EngleR. F., & SheppardK. (2001). Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH (No. w8554). National Bureau of Economic Research.
22.
EzzatH. (2013). The application of GARCH methods in modeling volatility using sector indices from the Egyptian exchange. Journal of Money, Investment and Banking, 27, 68–85.
23.
GallagherL. A., & TwomeyC. E. (1998). Identifying the source of mean and volatility spillovers in Irish equities: A multivariate GARCH analysis. The Economic and Social Review, 29, 341–356.
24.
GhoshT., SahuS., & ChattopadhyayS. (2020). Inflation expectations of households in India: Role of oil prices, economic policy uncertainty, and spillover of global financial uncertainty. Bulletin of Economic Research, 1–22. https://doi.org/10.1111/boer.12244
25.
GulzarS., KayaniG. M., XiaofengH., AyubU., & RafiqueA. (2019). Financial cointegration and spillover effect of global financial crisis: A study of emerging Asian financial markets,Economic Research [Ekonomska Istraživanja], 32(1), 187–218. https://doi.org/10.1080/1331677X.2018.1550001
26.
HabibaU. E., PeilongS., ZhangW., & HamidK. (2020). International stock markets Integration and dynamics of volatility spillover between the USA and South Asian markets: Evidence from Global financial crisis. Journal of Asia Business Studies. https://doi.org/10.1108/JABS-03-2019-0071
27.
HammoudehS. M., YuanY., & McAleerM. (2009). Shock and volatility spillovers among equity sectors of the Gulf Arab stock markets. The Quarterly Review of Economics and Finance, 49(3), 829–842.
28.
HassanS. A., & MalikF. (2007). Multivariate GARCH modeling of sector volatility transmission. The Quarterly Review of Economics and Finance, 47(3), 470–480.
29.
HoK. Y., & TsuiA. K. (2004). An analysis of the sectoral indices of Tokyo Stock Exchange: A multivariate GARCH approach with time varying correlations. Stochastic Finance, Autumn School and International Conference. https://fmwww.bc.edu/repec/mmfc04/12.pdf
30.
HoK. Y., TsuiA. K., & ZhangZ. (2009). Volatility dynamics of the U.S. business cycle: A multivariate asymmetric GARCH approach. Mathematics and Computers in Simulation, 79(9), 2856–2868.
31.
KatzkeN. (2013). South African sector return correlations: Using DCC and ADCC multivariate GARCH techniques to uncover the underlying dynamics (Working Papers 17/2013). Stellenbosch University, Department of Economics.
32.
KaushikN. (2018). Do global oil price shocks affect Indian metal market?Energy & Environment, 29(6), 891–904.
33.
KendallM. G., & HillA. B. (1953). The analysis of economic time-series—Part I: Prices. Journal of the Royal Statistical Society. Series A (General), 116(1), 11–34.
34.
KohlscheenE. and MojonB., & ReesD. (2020). The macroeconomic spillover effects of the pandemic on the global economy. https://ssrn.com/abstract=3569554orhttp://dx.doi.org/10.2139/ssrn.3569554
35.
KorenM., & TenreyroS. (2007). Volatility and development. The Quarterly Journal of Economics, 122(1), 243–287.
36.
KoulakiotisA., DasilasA., & MolyneuxP. (2007). Does trading volume influence GARCH effects?—Some evidence from the Greek market with special reference to banking sector. Investment Management and Financial Innovations, 4, 33–38.
37.
LeeJ. H., & KingM. L. (1993). A locally most mean powerful based score test for ARCH and GARCH regression disturbances. Journal of Business & Economic Statistics, 11(1), 17–27.
38.
LeeY. H. (2013). Global and regional range-based volatility spillover effects. Emerging Markets Review, 14, 1–10.
39.
MalikF., & HassanS. A. (2004). Modeling volatility in sector index returns with GARCH models using an iterated algorithm. Journal of Economics and Finance, 28(2), 211–225.
40.
MalkielB. G., & FamaE. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417.
41.
MallikarjunaM., & RaoR. P. (2017). Volatility behaviour in selected sectoral indices of Indian stock markets. Asian Journal of Research in Banking and Finance, 7(2), 23–34.
42.
MensiW., HammoudehS., NguyenD. K., & KangS. H. (2016). Global financial crisis and spillover effects among the U.S. and BRICS stock markets. International Review of Economics & Finance, 42, 257–276. https://doi.org/10.1016/j.iref.2015.11.005
43.
MukherjeeS., DasA., & SivarethinamohanR. (2020). Econometric models and tests of volatility, cointegration and randomness in Indian equity indices and other vista countries post global financial crisis. Asian Journal of Research in Banking and Finance, 10(4), 1–20.
44.
NandyS., & ChattopadhyayA. K. (2019). ‘Indian Stock Market Volatility’: A study of inter-linkages and spillover effects. Journal of Emerging Market Finance, 18(2_suppl), S183–S212.
45.
NelsonD. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica:Journal of the Econometric Society, 59(2), 347–370.
46.
NgA. (2000). Volatility spillover effects from Japan and the U.S. to the Pacific-Basin. Journal of International Money and Finance, 19(2), 207–233.
47.
NgS. L., ChinW. C., & ChongL. L. (2017). Multivariate market risk evaluation between Malaysian Islamic stock index and sectoral indices. Borsa Istanbul Review, 17(1), 49–61.
48.
PrabheeshK. P., AnglingkusumoR., & JuhroS. M. (2020). The dynamics of global financial cycle and domestic economic cycles: Evidence from India and Indonesia. Economic Modelling. https://doi.org/10.1016/j.econmod.2020.02.024
49.
RamkumarR. R., SelvamM., VanithaS., GayathriJ., & KarpagamV. (2012). An analysis of market efficiency in sectoral indices: A study with a special reference to Bombay Stock Exchange in India. European Journal of Scientific Research, 69(2), 290–297.
50.
RehmanM. U., & ApergisN. (2020). Do global sentiment shocks spill over towards emerging and frontier markets?Journal of Economic Studies, 47(3), 433–465.
51.
SaleemK. (2014). Modeling long memory in the Russian stock market: Evidence from major sectoral indices. Journal of Applied Business Research (JABR), 30(2), 567–574.
52.
SariannidisN., GiannarakisG., LitinasN., & KonteosG. (2010). Α GARCH examination of macroeconomic effects on U.S. stock market: A distinction between the total market index and the sustainability index. European Research Studies Journal, 13(1), 129–142.
53.
SarkarA., ChakrabartiG., & SenC. (2009). Indian stock market volatility in recent years: Transmission from global market, regional market and traditional domestic sectors. Journal of Asset Management, 10(1), 63–71.
54.
ShareefA. O., & PrabheeshK. P. (2020). Do foreign banks in India respond to global monetary policy shocks? A SVAR analysis. Studies in Economics and Finance. https://doi.org/10.1108/SEF-10-2019-0417
55.
SheppardK. (2003). Multi-step estimation of multivariate GARCH models. Proceedings of the International ICSC Symposium on Advanced Computing in Financial Markets. https://photos.kevinsheppard.com/images/2/29/ACFM2001.pdf
56.
SinghV. (2017). An analysis of the demonetization effect on sectorial indices in India. International Journal of Engineering Technology, Management and Applied Sciences, 5(4), 378–382.
57.
SuiL., & SunL. (2016). Spillover effects between exchange rates and stock prices: Evidence from BRICS around the recent global financial crisis. Research in International Business and Finance, 36, 459–471.
58.
TimmermannA., & GrangerC. W. (2004). Efficient market hypothesis and forecasting. International Journal of forecasting, 20(1), 15–27.
59.
TseY. K., & TsuiA. K. (2001). A multivariate GARCH model with time-varying correlations. Mathematics Preprint Archive, 2001(6), 1184–1217.
60.
WestT., & WorthingtonA. C. (2006). Macroeconomic risk factors in Australian commercial real estate, listed property trust and property sector stock returns: A comparative analysis using GARCH-M. University of Wollongong, Faculty of Commerce Papers.
