Degiannakis, Stavros and Livada, Alexandra (2013): Realized Volatility or Price Range: Evidence from a discrete simulation of the continuous time diffusion process. Published in: Economic Modelling No. 30 (2013): pp. 212-216.
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Abstract
The study provides evidence in favour of the price range as a proxy estimator of volatility in financial time series, in the cases that either intra-day datasets are unavailable or they are available at a low sampling frequency. A stochastic differential equation with time varying volatility of the instantaneous log-returns process is simulated, in order to mimic the continuous time diffusion analogue of the discrete time volatility process. The simulations provide evidence that the price range measures are superior to the realized volatility constructed at low sampling frequency. The high-low price range volatility estimator is more accurate than the realized volatility estimator based on five, or less, equidistance points in time. The open-high-low-close price range is more accurate than the realized volatility estimator based on eight, or less, intra-period log-returns.
Item Type: | MPRA Paper |
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Original Title: | Realized Volatility or Price Range: Evidence from a discrete simulation of the continuous time diffusion process |
Language: | English |
Keywords: | Integrated Volatility, Intra-day Volatility, Price range, Realized volatility, Stochastic Differential Equation. |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation |
Item ID: | 80449 |
Depositing User: | Dr. Stavros Degiannakis |
Date Deposited: | 30 Jul 2017 12:34 |
Last Modified: | 07 Oct 2019 01:48 |
References: | Alexander, C.O. (2008). Market Risk Analysis: Quantitative Methods in Finance, Volume 1. John Wiley and Sons, New York. Alizadeh, S., Brandt, M.W. and Diebold, F.X. (2002). Range-Based Estimation of Stochastic Volatility Models. Journal of Finance, LV11, 1047-1091. Andersen, T. and Bollerslev, T. (1998). Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts. International Economic Review, 39, 885-905. Andersen, T. and Benzoni, L. (2009). Realized Volatility. In (eds.) Andersen, T.G, Richard A.D., J.P. Kreiss and T. Mikosch, in Handbook of Financial Time Series, Springer-Verlag, 555-575. Andersen, T., Bollerslev, T., Diebold, F.X. and Ebens, H. (2001a). The Distribution of Realized Stock Return Volatility. Journal of Financial Economics, 61, 43-76. Andersen, T., Bollerslev, T., Diebold, F.X. and Labys, P. (2001b). The Distribution of Realized Exchange Rate Volatility. Journal of the American Statistical Association, 96, 42-55. Andersen, T., Bollerslev, T., Diebold, F.X. and Labys, P. (2003). Modeling and Forecasting Realized Volatility. Econometrica, 71, 529-626. Andersen, T., Bollerslev, T. and Diebold, F.X. (2010). Parametric and Nonparametric Volatility Measurement. In (eds.) Yacine Ait-Sahalia and Lars Peter Hansen, Handbook of Financial Econometrics, Amsterdam, Elsevier Science B.V, 67-128. Baillie, R.T., Bollerslev, T. and Mikkelsen, H.O. (1996). Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 74, 3-30. Barndorff-Nielsen, O.E. and Shephard, N. (2001). Non-Gaussian Ornstein-Uhlenbeck based Models and Some of their Uses in Financial Economics. Journal of the Royal Statistical Society, Series B, 63, 197-241. Barndorff-Nielsen, O.E. and Shephard, N. (2002). Econometric Analysis of Realised Volatility and its Use in Estimating Stochastic Volatility Models, Journal of the Royal Statistical Society, Series B, 64, 253–280. Barndorff-Nielsen, O.E. and Shephard, N. (2004). Power and Bipower Variation with Stochastic Volatility and Jumps, Journal of Financial Econometrics, 2, 1–37. Barndorff-Nielsen, O.E. and Shephard, N. (2005). How Accurate is the Asymptotic Approximation to the Distribution of Realised Volatility? In (eds.) Andrews, D., Powell, J., Ruud, P., and Stock, J., Identification and Inference for Econometric Models. Cambridge University Press, Cambridge. Drost, F.C. and Werker, B.J.M. (1996). Closing the GARCH Gap: Continuous Time GARCH Modeling. Journal of Econometrics, 74, 31-57. French, K.R., Schwert, G.W. and Stambaugh, R.F. (1987). Expected Stock Returns and Volatility. Journal of Financial Economics, 19, 3-29. Garman, M. and Klass, M. (1980). On the Estimation of Security Price Volatilities from Historical Data. Journal of Business, 53, 67-78. Hansen, P.R. and Lunde, A. (2005). A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)? Journal of Applied Econometrics, 20(7), 873-889. Hansen, P.R. and Lunde, A. (2006). Consistent Ranking of Volatility Models. Journal of Econometrics, 131, 97-121. Madhavan, A. (2000). Market Microstructure: A Survey. Journal of Financial Markets, 3, 205-258. McAleer, M. and Medeiros, M.C. (2008). Realized Volatility: A Review, Econometric Reviews, 27(1), 10-45. Merton, R.C. (1980). On Estimating the Expected Return on the Market: An Explanatory Investigation. Journal of Financial Economics, 8, 323-361. Nison, S. (2001). Japanese Candlestick Charting Techniques: A Contemporary Guide to the Ancient Investment Techniques of the Far East. New York Institute of Finance, New York. Nelson, D. (1990). ARCH Models as Diffusion Approximations. Journal of Econometrics, 45, 7-38. Oomen, R. (2001). Using High Frequency Stock Market Index Data to Calculate, Model and Forecast Realized Volatility. Department of Economics, European University Institute, Manuscript. Parkinson, M. (1980). The Extreme Value Method for Estimating the Variance of the Rate of Return. Journal of Business, 53(1), 61-65. Schwert, G.W. (1989). Why Does Stock Market Volatility Changes Over Time. Journal of Finance, 44, 1115-1153. Schwert, G.W. (1990). Stock Volatility and the Crash of ‘87. Review of Financial Studies, 3, 77-102. Schwert, G.W. and Seguin, P. (1990). Heteroskedasticity in Stock Returns. Journal of Finance, 45, 1129–1155. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/80449 |