South Asian Journal of Management

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

An Analysis of Spillover of Return and Asymmetric Spillover of Volatility between NIFTY and India VIX


Affiliations
1 International Management Institute (IMI), B-10, Qutab Institutional Area, Tara Crescent, New Delhi, Delhi 110016, India
     

   Subscribe/Renew Journal


Volatility in stock markets is a phenomena arising from change in stock prices due to new information that arrives continuously, causing changes in market scenario. Volatility is the key measure of risk to value assets like stocks, commodities, derivatives, etc. option prices reflect the volatility of underlying and these have been used in developing Volatility Index (VIX), which is quite popularly being considered to be a measure of market volatility. This research study measures the spillover of volatility from VIX to stock market and examines whether there exists an asymmetric response from by Indian stock market to VIX, i.e., whether stock market reacts differently towards positive and negative shocks from VIX. It also tends to determine whether this asymmetry exists during bullish and bearish phases as well as during a combination of sign and phase asymmetry. VIX is compared against other methods, viz., EWMA, GARCH and EGARCH to evaluate its effectiveness in measuring volatility by use of in sample tests. Along with this TGARCH was used to measure volatility spillover and asymmetry in spillover. Finally, the study concluded as VIX being the best measure of volatility and presence of sign but no phase asymmetry in volatility transmission from NIFTY to VIX.

Keywords

Asymmetry, NIFTY, Spillover, VIX, Volatility.
User
Subscription Login to verify subscription
Notifications
Font Size

  • Alberg, D., Shalit, H., & Yosef, R. (2008). Estimating stock market volatility using asymmetric GARCH models. Applied Financial Economics, 18(15), 1201-1208.

  • Badhani, K. N. (2009). Response Asymmetry in Return and Volatility Spillover from the US to Indian Stock Market. IUP Journal of Applied Finance, 15(9), 22-45.

  • Baker, M., & Wurgler, J. (2006). Investor sentiment and the crosssection of stock returns. The Journal of Finance, 61(4), 1645-1680.

  • Baker, M., & Wurgler, J. (2012). Comovement and predictability relationships between bonds and the cross-section of stocks. The Review of Asset Pricing Studies, 2(1), 57-87.

  • Banerjee, A. & Sarkar, S. (2006). Modeling daily volatility of the Indian stock market using intra-day data (IIMK Working Paper Series, No. 588).

  • Baur, D. G. (2010). Stock-bond co-movements and cross-country linkages. Int. J. Bank Account. Finance, 2(2), 111-129.

  • Bierne, J., Caporale, G. M., Schulze-Ghattas, M. & Spagnolo, N. (2009). Volatility spillovers and contagion from mature to emerging stock markets (ECB Working Paper No. 1113). Frankfurt, Germany: European Central Bank (ECB). Retrieved from https://www.econstor.eu/bitstream/10419/153547/1/ecbwp1113.pdf

  • Bodart, V., & Candelon, B. (2009). Evidence of interdependence and contagion using a frequency domain framework. Emerging Markets Review, 10(2), 140-150.

  • Christoffersen, P., Feunou, B., Jacobs, K., & Meddahi, N. (2014). The Economic value of realized volatility: Using high frequency returns for option valuation. Journal of Financial and Quantitative Analysis, 49(3), 663-697.

  • Collins, D., & Biekpe, N. (2003). Contagion and interdependence in African stock markets. South African Journal of Economics, 71(1), 181-194.

  • Coudert, V., Couharde, C., & Mignon, V. (2011). Exchange rate volatility across financial crises. Journal of Banking & Finance, 35(11), 3010-3018.

  • De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990). Noise trader risk in financial markets. Journal of political Economy, 98(4), 703-738.

  • French, K. R., Schwert, G. W., & Stambaugh, R. F. (1987). Expected stock returns and volatility. Journal of Financial Economics, 19(1), 3-29.

  • Garg, S., & Vipul. (2014). Volatility forecasting performance of two-scale realized volatility. Applied Financial Economics, 24(17), 1111-1121.

  • Golab, A. (2013). An investigation into the volatility and co-integration of emerging European stock markets (Unpublished doctoral dissertation). Joondalup, Australia: Edith Cowan University.

  • Hegerty, S. W. (2012). How integrated are the exchange markets of the Baltic Sea Region? An examination of market pressure and its contagion. Baltic Journal of Economics, 12(2), 109-122.

  • Kahneman, D. & Tversky, A. (1979) Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.

  • Kambouroudis, D. S., & McMillan, D. G. (2016). Does VIX or volume improve GARCH volatility forecasts?. Applied Economics, 48(13), 1210-1228.

  • Koutmos, G. (1998). Asymmetries in the conditional mean and the conditional variance: Evidence from nine stock markets. Journal of Economics and Business, 50(3), 277-290.

  • Koutmos, G., & Booth, G. G. (1995). Asymmetric volatility transmission in international stock markets. Journal of international Money and Finance, 14(6), 747-762.

  • Kumar, K. K., & Mukhopadhyay, C. (2007). Volatility Spillovers from the US to Indian Stock Market: A Comparison of GARCH Models. ICFAI Journal of Financial Economics, 5(4), 7-30.

  • Kumar M. S. & Persaud A. (2001). Pure contagion and investors shifting risk appetite: Analytical issues and empirical evidence (IMF Working Paper No. 01/134). Retrieved from https://www.imf.org/en/Publications/WP/Issues/2016/12/30/Pure-Contagion-and-Investors-Shifting-Risk-Appetite-Analytical-Issues-and-Empirical-Evidence-15347

  • Kumar, S. S. S. (2006). Comparative performance of volatility forecasting models in Indian markets. Decision, 33(2), 25-40.

  • Lee, C., Shleifer, A., & Thaler, R., (1991). Investor sentiment and the closedend fund puzzle. The Journal of Finance, 46(1), 75-109.

  • Lim, C. M., & Sek, S. K. (2013). Comparing the performances of GARCH-type models in capturing the stock market volatility in Malaysia. Procedia Economics and Finance, 5, 478-487.

  • Liu, H. C., Lee, Y. H., & Lee, M. C. (2009) Forecasting China stock markets volatility via GARCH models under skewed-GED distribution. Journal of Money, Investment and Banking, 1(7), 542-547.

  • MacKinnon, J. G. (1996). Numerical distribution functions for unit ischolar_main and cointegration tests. Journal of Applied Econometrics, 11(6), 601-618.

  • Mapa, D. S. (2004). A forecast comparison of financial volatility models: GARCH (1,1) is not enough (MPRA Paper No. 21028). Retrieved from https://mpra.ub.unimuenchen.de/21028/1/MPRA_paper_21028.pdf

  • Matei, I., (2010) Contagion and causality an empirical analysis on sovereign bond spreads. Economics Bulletin, 30(3), 1885-1896.

  • McAleer, M., & Medeiros, M. C. (2008). Realized volatility: A review. Econometric Reviews, 27(1-3), 10-45.

  • Mozumder, N., De Vita, G., Kyaw, S., & Larkin, C. (2015). Volatility spillover between stock prices and exchange rates: New evidence across the recent financial crisis period. Economic Issues, 20(1), 43-64

  • Nair, A. S., & Ramanathan, A. (2003). Analysis of co movements of select US & Indian stock price indexes. Working Paper presented at UTI Capital Market Conference. Retrieved from : http://www.utiicm.com/Cmc/PDFs/2002/abhilashnair%5E13.pdf

  • Nath, G. C. & Verma, S. (2003). Study of common stochastic trend and co-integration in the emerging markets: A case study of India, Singapore and Taiwan (NSE Research Paper No. 72). New Delhi, India: National Stock Exchange (NSE) of India Ltd. Retrieved from: https://www.nse-india.com/content/research/Paper72.pdf

  • Neal, R., & Wheatley, S. M. (1998). Do measures of investor sentiment predict returns?. Journal of Financial and Quantitative Analysis, 33(4), 523-547.

  • Oberholzer, N., & Venter, P. (2015). Univariate GARCH models applied to the JSE/FTSE stock indices. Procedia Economics and Finance, 24, 491-500.

  • Sulin, P., Feiqi, D., & Yanming, W. (2007). A comparison of forecasting models of the volatility in Shenzhen stock market. Acta Mathematica Scientia, 27(1), 125-136.

  • Sharma, P. & Vipul. (2016). Forecasting stock market volatility using Realized GARCH model: International evidence. The Quarterly Review of Economics and Finance, 59, 222-230.

  • Shiller, R. J. (1997). Human behavior and the efficiency of the financial system. Retrieved from http://www.econ.yale.edu/~shiller/online/handbook.html

  • Sims, C. A. (1980). Macroeconomics and reality. Econometrica: Journal of the Econometric Society, 48(1), 1-48.

  • Skintzi, V. D., & Refenes, A. N. (2006). Volatility spillovers and dynamic correlation in European bond markets. Journal of International Financial Markets, Institutions and Money, 16(1), 23-40.

  • Suganuma, R. (2000). Reality check for volatility models. San Diego, CA: University of California, San Diego. Retrieved from http://www.econ.pucrio.br/pdf/suganuma.pdf

  • Tang, K., & Xiong, W. (2012). Index investment and the financialization of commodities. Financial Analysts Journal, 68(5), 54-74.

  • Taylor, J. W. (2004). Volatility forecasting with smooth transition exponential smoothing. International Journal of Forecasting, 20(2), 273-286.

  • Thenmozhi., & Chandra. (2014). India Volatility Index (India VIX) and risk management in the Indian stock market. Decision, 42(1), 33-55.

  • Vortelinos, D. I. (2013). Portfolio analysis of intraday covariance matrix in the Greek equity market. Research in International Business and Finance, 27(1), 66-79.

  • Zakoian, J. M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.


Abstract Views: 182

PDF Views: 0




  • An Analysis of Spillover of Return and Asymmetric Spillover of Volatility between NIFTY and India VIX

Abstract Views: 182  |  PDF Views: 0

Authors

Monika Chopra
International Management Institute (IMI), B-10, Qutab Institutional Area, Tara Crescent, New Delhi, Delhi 110016, India

Abstract


Volatility in stock markets is a phenomena arising from change in stock prices due to new information that arrives continuously, causing changes in market scenario. Volatility is the key measure of risk to value assets like stocks, commodities, derivatives, etc. option prices reflect the volatility of underlying and these have been used in developing Volatility Index (VIX), which is quite popularly being considered to be a measure of market volatility. This research study measures the spillover of volatility from VIX to stock market and examines whether there exists an asymmetric response from by Indian stock market to VIX, i.e., whether stock market reacts differently towards positive and negative shocks from VIX. It also tends to determine whether this asymmetry exists during bullish and bearish phases as well as during a combination of sign and phase asymmetry. VIX is compared against other methods, viz., EWMA, GARCH and EGARCH to evaluate its effectiveness in measuring volatility by use of in sample tests. Along with this TGARCH was used to measure volatility spillover and asymmetry in spillover. Finally, the study concluded as VIX being the best measure of volatility and presence of sign but no phase asymmetry in volatility transmission from NIFTY to VIX.

Keywords


Asymmetry, NIFTY, Spillover, VIX, Volatility.

References