This article investigates the cointegration level, and changes in the existence and direction of causality among volatilities. Vector autoregressive (VAR) model enables us to conduct Granger-causality and impulse response analysis, and determine the pattern of causality. The empirical findings uncover that ex-ante volatility best impounds the market-wide information to explain the ex-post volatility. Vector autoregression results make clear that unidirectional causality exists among ex-ante and ex-post volatilities. Impulse response analysis explains that realized volatility declines significantly initially in the first two periods and remains constant for all other periods. Findings, emphasizes that implied volatility is more informative on volatility forecasting, useful for successful volatility traders and pricing of options.
BeckerR.ClementsA.E.WhiteS.I. (2006). On the informational efficiency of S&P 500 implied volatility. The North American Journal of Economics and Finance, 17(2), 139–153.
2.
BeckersS. (1981). Standard deviations implied in option prices as predictors of future stock price variability. Journal of Banking and Finance, 5(3), 363–381.
3.
BlackF.ScholesM. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81(3), 637–654.
4.
BoxG.E.JenkinsG.M. (1976). Time series analysis: Forecasting and control, revised edition. San Francisco: Holden-Day.
5.
CaninaL.FiglewskiS. (1993). The informational content of implied volatility. Review of Financial Studies, 6(3), 659–681.
ChirasD.P.ManasterS. (1978). The information content of option prices and test of market efficiency. Journal of Financial Economics, 6(2–3), 213–234.
8.
ChristensenB.J.HansenC.S. (2002). New evidence on the implied-realized volatility relation. The European Journal of Finance, 8(2), 187–205.
9.
ChristensenB.PrabhalaN. (1998). The relation between implied and realized volatility. Journal of Financial Economics, 50(2), 125–150.
10.
ChungS.L.TsaiW.C.WangY.H.WengP.S. (2011). The information content of the S&P 500 index and VIX options on the dynamics of the S&P 500 index. Journal of Futures Markets, 31(12), 1170–1201.
11.
CorradoC.J.MillerT.W. (2005). The forecast quality of CBOE implied volatility indexes. Journal of Futures Markets, 25(4), 339–373.
12.
DayT.E.LewisC.M. (1992). Stock market volatility and the information content of stock index options. Journal of Econometrics, 52(1–2), 267–287.
13.
EderingtonL.GuanW. (2005). The information frown in option prices. Journal of Banking & Finance, 29(6), 1429–1457.
14.
EndersW. (1995). Applied econometric time series. New York: Wiley.
15.
EngleR.F.GrangerC.W. (1987). Cointegration and error correction: Representation, estimation and testing. Econometrica, 55(2), 251–276.
16.
FilisG. (2009). An analysis between implied and realised volatility in the Greek derivative market. Journal of Emerging Market Finance, 8(3), 251–263.
17.
FlemingJ. (1998). The quality of market volatility forecasts implied by S&P 100 index option prices. Journal of Empirical Finance, 5(4), 317–345.
18.
GodbeyJ.M.MaharJ.W. (2005). Forecasting power of implied volatility: Evidence from individual equities (http://ssrn.com/abstract=762644), July14. Retrieved February9, 2012, from http://papers.ssrn.com:http://papers.ssrn.com/sol3/papers.cfm?abstract_id=762644.
19.
GwilymO.A.BuckleM. (1999). Volatility forecasting in the framework of the option expiry cycle. The European Journal of Finance, 5(1), 73–94.
20.
HansenC.S. (2001). The relation between implied and realized volatility in the Danish option and equity markets. Accounting and Finance, 41(3), 197–228.
21.
HarveyC.R.WhaleyR.E. (1991). S&P 100 Index Option Volatility. The Journal of Finance, 46(4), 1551–1561.
22.
HeP.YauS.S. (2007). Forecasting stock market volatility using implied volatility. Proceedings of the 2007 American Control Conference, (pp. 1823–1828). New York City, USA.
23.
JackwerthJ.RubinsteinM. (1996). Recovering probability distributions from option prices. The Journal of Finance, 51( 5), 1611–1631.
24.
JohansenS. (1991). Estimation and hypothesis testing of cointegrating vectors in Gaussian vector autoregressive models. Econometrica, 59(6), 1551–1580.
25.
JohansenS.JuseliusK. (1990). Maximum likelihood estimation and inference on cointegration–with applications to the demand for money. Oxford Bulletin of Economics and Statistics, 52(2), 169–210.
26.
JorionP. (1995). Predicting volatility in the foreign exchange market. The Journal of Finance, 50(2), 507–528.
27.
KoopmanS.JungbackerB.HolE. (2005). Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements. Journal of Empirical Finance, 12(3), 445–475.
28.
KumarS. (2008). Information content of option implied volatility: Evidence from the Indian market. Decision, 35(2), 1–16.
29.
LamoureuxC.G.LastrapesW.D. (1993). Forecasting stock-return variance: Toward an understanding of Stochastic implied volatilities. The Review of Financial Studies, 6(2), 293–326.
30.
LatanéH.A.RendlemanR.J. (1976). Standard deviations of stock price ratios implied in option prices. The Journal of Finance, 31(2), 369–381.
31.
LiS.YangQ. (2009). The relationship between implied and realized volatility: Evidence from the Australian stock indexoption market. Rev Quant Finan Acc, 32(4), 405–419.
32.
MertonR.C. (1973). Theory of rational option pricing. Bell Journal of Economics and Management Science, 4(1), 141–183.
33.
MuzzioliS. (2007). The relation between implied and realised volatility: are call options more informative than put options? evidence from the DAX index options market. CEFIN Working Papers No 4, CEFIN–Centro Studi di Banca e Finanza, MODENA (Italy).
34.
MuzzioliS. (2010). Option-based forecasts of volatility: An empirical study in the DAX index options market. The European Journal of Finance, 16(6), 561–586.
35.
PandaS.P.SwainN.MalhotraD.K. (2008). Relationship between implied and realized volatility of S&P CNX. Frontiers in Finance and Economics, 5(1), 85–105.
36.
SchwertG.W. (1990). Stock Volatility and the Crash of ʼ87. Review of Financial Studies, 3(1), 77–102.
37.
ShuJ.ZhangJ.E. (2003). The relationship between implied and realized volatility of S&P 500 index. Wilmott magazine, 4(November28), 83–91.
38.
SimsC.A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48.
39.
SzakmaryA.OrsE.KimJ.K.DavidsonW.N. (2003). The predictive power of implied volatility: Evidence from 35 futures markets. Journal of Banking & Finance, 27(11), 2151–2175.
40.
ShanmughamR., & Zabiulla (2012). Pricing efficiency of Nifty BeES in bullish and bearish markets. Global Business Review, 13(1), 109–121.
41.
TaylorS.J.YadavP.K.ZhangY. (2010). The information content of implied volatilities and model-free volatility expectations: Evidence from options written on individual stocks. Journal of Banking & Finance, 34(4), 871–881.
42.
VerbeekM. (2004). A guide to modern econometrics. West Sussex, England: John Wiley & Sons Ltd.
43.
WongW.K.TuA.H. (2009). Market imperfections and the information content of implied and realized volatility. Pacific-Basin Finance Journal, 17(1), 58–79.
