Publication: How are VIX and Stock Index ETF Related?
Loading...
Files
Official URL
Full text at PDC
Publication Date
2016
Advisors (or tutors)
Editors
Journal Title
Journal ISSN
Volume Title
Publisher
Citations
Exportar
Abstract
As stock market indexes are not tradeable, the importance and trading volume of Exchange Traded Funds (ETFs) cannot be understated. ETFs track and attempt to replicate the performance of a specific index. Numerous studies have demonstrated a strong relationship between the S&P500 Composite Index and the Volatility Index (VIX), but few empirical studies have focused on the relationship between VIX and ETF returns. The purpose of the paper is to investigate whether VIX returns affect ETF returns by using vector autoregressive (VAR) models to determine whether daily VIX returns with different moving average processes affect ETF returns. The ARCH-LM test shows conditional heteroskedasticity in the estimation of ETF returns, so that the diagonal BEKK model is used to accommodate multivariate conditional heteroskedasticity in the VAR estimates of ETF returns. Daily data on ETF returns that follow different stock indexes in the USA and Europe are used in the empirical analysis. The estimates show that daily VIX returns have: (1) significant negative effects on European ETF returns in the short run; (2) stronger significant effects on single market ETF returns than on European ETF returns; and (3) lower impacts on the European ETF returns than on S&P500 returns.
Description
For financial support, the first author wishes to thank the National Science Council, Taiwan, and the third author is grateful to the National Science Council, Taiwan and the Australian Research Council.
UCM subjects
Unesco subjects
Keywords
Citation
Arik, A. (2011), Modeling Market Sentiment and Conditional Distribution of Stock Index Returns under GARCH Process, Ph.D. Dissertation, Department of Economics, Claremont Graduate University.
Baba, Y., R.F. Engle, D. Kraft and K.F. Kroner (1985), Multivariate Simultaneous Generalized ARCH. Unpublished manuscript, Department of Economics, University of California, San Diego, CA, USA.
Black, F. (1976), Studies of Stock Market Volatility Changes, in Proceedings of the American Statistical Association, Business and Economic Statistics Section,
Washington, DC, USA, pp. 177-181.
Bollerslev, T. (1986), Generalized Autoregressive Conditional Heteroskedasticity, Journal of Econometrics, 31, 307-327.
Bollerslev, T. (1990), Modelling the Coherence in Short-run Nominal Exchange Rate: A Multivariate Generalized ARCH Approach, Review of Economics and Statistics, 72, 498-505.
Borovkova, S. and F.J. Permana (2009), Implied Volatility in Oil Markets, Computational Statistics and Data Analysis, 53, 2022-2039.
Boussama, F. (2000), Asymptotic Normality for the Quasi-maximum Likelihood Estimator of a GARCH Model, Comptes Rendus de l’Academie des Sciences, 331, 81-84.
Caporin, M. and M. McAleer (2012), Do We Really Need both BEKK and DCC? A Tale of Two Multivariate GARCH Models, Journal of Economic Surveys, 26(4), 736-751.
Caporin, M. and M. McAleer (2013), Ten Things You Should Know About the Dynamic Conditional Correlation Representation, Econometrics, 1(1), 115-126.
Chang, C.-L., Y. Li, and M. McAleer (2015), Volatility Spillovers between Energy and Agricultural Markets: A Critical Appraisal of Theory and Practice, Tinbergen Institute Discussion Papers 15-077/III, Tinbergen Institute.
Chang, C.-L. and M. McAleer (2015) (eds.), Econometric Analysis of Financial Derivatives, Journal of Econometrics, 187(2), 403-633.
Cochran, S.J., I. Mansur, and B. Odusami (2015), Equity Market Implied Volatility and Energy Prices: A Double Threshold GARCH Approach, Energy Economics, 50, 264-272.
Cox, J.C., S. A. Ross, and M. Rubinstein (1979), Option Pricing: A Simplified Approach, Journal of Financial Economics, 7, 229-263.
Dickey, D.A. and W.A. Fuller (1979), Distribution of the Estimators for Autoregressive Time Series with a Unit Root, Journal of the American Statistical Association, 74 (366), 427-431.
Dumas, B., J. Fleming, and R.E. Whaley (1998), Implied Volatility Functions: Empirical Tests, Journal of Finance, 53, 2059-2106.
Engle, R.F. (1982), Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation, Econometrica, 50, 987-1007.
Engle, R.F. (2002), Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Hereoskedasticity Models, Journal of Business and Economic Statistics, 20, 339-350.
Engle, R.F. and K.F. Kroner (1995), Multivariate Simultaneous Generalized ARCH, Econometric Theory, 11, 122-150.
Fernandes, M., M.C. Medeiros, and M. Scharth (2014), Modeling and Predicting the CBOE Market Volatility Index, Journal of Banking & Finance, 40, 1-10.
Giot, P. (2005), Relationships between Implied Volatility Indexes and Stock Index Returns, Journal of Portfolio Management, 31, 92-100.
Glosten, L., R. Jagannathan, and D. Runkle (1993), On the Relation between the Expected Value and the Volatility Nominal Excess Return on Stocks, Journal of Finance, 46, 1779-1801.
Hamilton, J.D. (1994), Time Series Analysis, Princeton University Press, Princeton, NJ.
Kanas, A. (2013), The Risk-Return Relation and VIX: Evidence from the S&P 500, Empirical Economics, 44(3), 1291-1314.
Ling, S. and M. McAleer (2003), Asymptotic Theory for a Vector ARMA-GARCH Model, Econometric Theory, 19, 278-308.
Lütkepohl, H. (2006), New Introduction to Multiple Time Series Analysis, Springer, New York.
McAleer, M. (2005), Automated Inference and Learning in Modeling Financial Volatility, Econometric Theory, 21(1), 232-261.
McAleer, M. (2014), Asymmetry and Leverage in Conditional Volatility Models, Econometrics, 2, 145-150.
McAleer, M., F. Chan, S. Hoti and O. Lieberman (2008), Generalized Autoregressive Conditional Correlation, Econometric Theory, 24(6), 1554-1583.
McAleer, M. and C.M. Hafner (2014), A One Line Derivation of EGARCH, Econometrics, 2, 92-97.
McAleer, M., S. Hoti and F. Chan (2009), Structure and Asymptotic Theory for Multivariate Asymmetric Conditional Volatility, Econometric Reviews, 28, 422-440.
Nelson, D.B. (1991), Conditional Heteroskedasticity in Asset Returns: A New Approach, Econometrica, 59, 347-370.
Phillips, P.C.B. and P. Perron (1988), Testing for a Unit Root in Time Series Regression, Biometrika, 75(2), 335-346.
Poon, S.H. and C. Granger. (2003), Forecasting Volatility in Financial Markets: A Review, Journal of Economic Literature, 41, 478-539.
Rosenberg, J.V. (1999), Implied Volatility Functions: A Reprise, New York University, Leonard N. Stern School Finance Department Working Paper Series, 99-027.
Sarwar, G. (2012), Is VIX an Investor Fear Gauge in BRIC Equity Markets? Journal of Multinational Financial Management, 22(3), 55-65.
Tsay, R.S. (1987), Conditional Heteroscedastic Time Series Models, Journal of the American Statistical Association, 82, 590-604.
Tse, Y.K. and A.K.C. Tsui (2002), A Multivariate GARCH Model with Time-varying Correlations, Journal of Business and Economic Statistics, 20, 351-362.