Giulio, Cifarelli (2004): Yes, implied volatilities are not informationally efficient: an empirical estimate using options on interest rate futures contracts. Published in: Studi e Discussioni Dipartimento di Scienze Economiche Università di Firenze No. n. 137 (February 2004)
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The accuracy of volatility forecast estimators has been assessed using daily overlapping and non overlapping observations on two major short-term interest rate futures contracts traded in London. The use of a panelized data set has eliminated some of the drawbacks usually associated with non overlapping data estimation, such as the lack of accuracy due to an insufficient number of observations or the arbitrariness of the choice of tenor. In the same way non stationarity and long memory characteristics of daily overlapping time series are disposed of. Information content estimation in levels associated with the Hansen (1982) variance covariance matrix estimator provides reasonably accurate estimates, broadly similar to the corresponding benchmark panel data ones.
|Item Type:||MPRA Paper|
|Original Title:||Yes, implied volatilities are not informationally efficient: an empirical estimate using options on interest rate futures contracts|
|Keywords:||Options; stochastic volatility; panel data analysis|
|Subjects:||G - Financial Economics > G1 - General Financial Markets > G14 - Information and Market Efficiency ; Event Studies ; Insider Trading
C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C23 - Panel Data Models ; Spatio-temporal Models
C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes
|Depositing User:||Giulio Cifarelli|
|Date Deposited:||09 Feb 2011 19:49|
|Last Modified:||05 Oct 2016 15:36|
Alexander C. O. (2001), “Market Models, A Guide to Financial Data Analysis”, John Wiley & Sons, Sussex.
Alexander C. O. and C. T. Leigh (1997), “On the Covariance Matrices used in Value-at-Risk Models”, Journal of Derivatives, 4, 50-62.
Amin K. and V. K. Ng (1997), “Inferring Future Volatility from the Information in Implied Volatility in Eurodollar Options: A New Approach”, Review of Financial Studies, 10, 333-367.
Andrews D. W. K. (1991), “Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation”, Econometrica, 59, 817-858.
Ané T. and H. Geman (1998), “Stochastic Volatility and Transaction Time: An Activity - Based Volatility Estimator”, mimeo, “Forecasting Financial Markets” 1998 Conference, London.
Ap Gwilym O. and M. Buckle (1999), “Volatility Forecasting in the Framework of the Option Expiry Cycle”, European Journal of Finance, 5, 73-94.
Bahra B. (1998), “Implied Risk–Neutral Probability Density Functions from Option Prices: A Central Bank Perspective”, in “Forecasting Volatility in the Financial Markets”, J. Knight and S. Satchell (eds.), Butterworth – Heinemann, Oxford.
Beran J. (1994), Statistics for Long Memory Processes. Chapman and Hall, New York.
Beran J. (1995), “Maximum Likelihood Estimation of the Differencing Parameter for Invertible Short and Long Memory ARIMA Models”, Journal of the Royal Statistical Society Series B, 57, 659-672.
Black F. (1976), “The Pricing of Commodity Contracts”, Journal of Financial Economics, 3, 167-179.
Black F. and M. Scholes (1973), “The Pricing of Options and Corporate Liabilities”, Journal of Political Economy, 81, 637-659.
Canina L. and S. Figlewski (1993), “The Informational Content of Implied Volatility”, Review of Financial Studies, 6, 659-681.
Christensen B. J. and N. R. Prabhala (1998), “The Relation Between Implied and Realized Volatility”, Journal of Financial Economics, 50, 125-150.
Day T. and C. Lewis (1993), “Forecasting Futures Markets Volatility”, Journal of Derivatives, 1, 51-63.
Fair R. C. and R. J. Shiller (1990), “Comparing Information in Forecasts from Econometric Models”, American Economic Review, 80, 375-389.
Feinstein S. (1989), “The Black-Scholes Formula is Nearly Linear in for At-The-Money Options”, Presentation at the AFA Conference, Washington, D.C..
Figlewski S. (1997), “Forecasting Volatility”, Financial Markets Institutions & Instruments, 6, 1-88.
Fleming J. (1993), “The Quality of Market Volatility Forecasts Implied by S&P 100 Index Option Prices”, Working Paper, Duke University, N.C..
Fleming J, B. Ostdiek and R. E. Whaley (1995), “Predicting Stock Market Volatility: a New Measure”, Journal of Futures Markets, 15, 265-302.
Gemmill G. (1986), “The Forecasting Performance of Stock Options on the London Traded Option Market”, Journal of Business Finance and Accounting, 13, 535-546.
Granger C. W. J. (1980), “Long Memory Relationships and the Aggregation of Dynamic Models”, Journal of Econometrics, 14, 227-238.
Granger C. W. J. and P. Newbold (1974), “Spurious Regressions in Econometrics”, Journal of Econometrics, 2, 111-120.
Granger C. W. J. and R. Joyeux (1980), “An Introduction to Long Memory Time Series Models and Fractional Differencing”, Journal of Time Series Analysis, 1, 15-39.
Greene W. H. (1993), Econometric Analysis, Prentice-Hall, New Jersey.
Guo D. (1996), “The Predictive Power of Implied Stochastic Variance from Currency Options”, Journal of Futures Markets, 8, 915-942.
Hamilton J. D. (1994), “Time Series Analysis”, Princeton University Press, New Jersey.
Hansen L. P. (1982), “Large Sample Properties of Generalized Methods of Moments Estimators”, Econometrica, 50, 1029-1054.
Hansen L. P. and R. J. Hodrick (1980), “Forward Exchange Rates as Optimal Predictors of Future Spot Rates: an Econometric Analysis”, Journal of Political Economy, 88, 829-853.
Hansen L.P. and R. J. Hodrick (1983), “Risk Averse Speculation in Forward Exchange Markets: an Econometric Analysis”, in “Exchange Rates and International Macroeconomics”, J. A. Frankel (ed.), University of Chicago Press, Chicago.
Hull J. and A. White (1987), “The Pricing of Options on Assets with Stochastic Volatilities”, Journal of Finance, 42, 281-300.
Hwang S. and S. E. Satchell (1998), “Implied Volatility Forecasting: A Comparison of Different Procedures Including Fractionally Integrated Models with Applications to UK Equity Options”, in “Forecasting Volatility in the Financial Markets”, J. Knight and S. Satchell (eds.), Butterworth - Heinemann, Oxford.
Im K. S, M. H. Pesaran and Y. Shin (2003), “Testing for Unit Roots in Heterogeneous Panels”, Journal of Econometrics, 115, 53-74.
Jorion P. (1995), “Predicting Volatility in the Foreign Exchange Market”, Journal of Finance, 50, 507-528.
Lamoureux C. G. and W. D. Lastrapes (1993), “Forecasting Stock - Return Variance: Toward an Understanding of Stochastic Implied Volatilities”, Review of Financial Studies 6, 293-326.
Li K. (2002), “Long-Memory versus Option-Implied Volatility Predictions”, Journal of Derivatives, 9, 9-25.
Lieu D. (1990), “Option Pricing with Futures-Style Margining”, Journal of Futures Markets, 10, 327-338.
Lo A. W. (1991), “Long Term Memory in Stock Market Prices”, Econometrica, 59, 1279-1313.
Neuhaus H. (1995), “The Information Content of Derivatives for Monetary Policy”, Discussion Paper 3/95, Economic Research Group of the Deutsche Bundesbank.
Pesaran M. H. (2003), “A Simple Panel Unit Root Test in the Presence of Cross Section Dependence”, mimeo, University of Southern California & Cambridge University. Available at http://www.econ.cam.ac.uk./faculty/pesaran/panelcadf.pdf.
Phillips P. C. B. and B. Hansen (1990), “Statistical Inference in Instrumental Variables Regression with I(1) Processes”, Review of Economic Studies, 57, 473-495.
Richardson M. and T. Smith (1991), “Tests of Financial Models in the Presence of Overlapping Observations”, Review of Financial Studies, 4, 227-254.
Scott L. O. (1992), “The Information Content of Prices in the Derivative Security Markets”, International Monetary Fund Staff Papers, 39, 596-625.
Stein J. C. (1989), “Overreactions in the Option Market”, Journal of Finance, 44, 1011-1023.