Degiannakis, Stavros and Livada, Alexandra (2016): Evaluation of Realized Volatility Predictions from Models with Leptokurtically and Asymmetrically Distributed Forecast Errors. Published in: Journal of Applied Statistics , Vol. 5, No. 43 (2016): pp. 871-892.
This is the latest version of this item.
Preview |
PDF
MPRA_paper_67968.pdf Download (1MB) | Preview |
Abstract
Accurate volatility forecasting is a key determinant for portfolio management, risk management and economic policy. The paper provides evidence that the sum of squared standardized forecast errors is a reliable measure for model evaluation when the predicted variable is the intra-day realized volatility. The forecasting evaluation is valid for standardized forecast errors with leptokurtic distribution as well as with leptokurtic and asymmetric distribution. Additionally, the widely applied forecasting evaluation function, the predicted mean squared error, fails to select the adequate model in the case of models with residuals that are leptokurtically and asymmetrically distributed. Hence, the realized volatility forecasting evaluation should be based on the standardized forecast errors instead of their unstandardized version.
| Item Type: | MPRA Paper |
|---|---|
| Original Title: | Evaluation of Realized Volatility Predictions from Models with Leptokurtically and Asymmetrically Distributed Forecast Errors |
| Language: | English |
| Keywords: | integrated volatility, intra-day, predicted mean squared error, realized volatility, standardized prediction error criterion, simulating forecast errors, ultra-high frequency, volatility forecasting evaluation. |
| Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods G - Financial Economics > G1 - General Financial Markets > G15 - International Financial Markets G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation |
| Item ID: | 96281 |
| Depositing User: | Dr. Stavros Degiannakis |
| Date Deposited: | 05 Nov 2019 16:52 |
| Last Modified: | 05 Nov 2019 16:52 |
| References: | 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., Bollerslev, T. and Lange, S. (1999). Forecasting Financial Market Volatility: Sample Frequency vis-à-vis Forecast Horizon. Journal of Empirical Finance, 6, 457-477. Baillie, R.T. (1996). Long Memory Processes and Fractional Integration in Econometrics. Journal of Econometrics, 73, 5-59. 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. (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. Bernanke, B.S. and Gertler, M. (2001). Should Central Banks Respond to Movements in Asset Prices? The American Economic Review, 91(2), 253-257. Corsi, F., Mittnik, S., Pigorsch, C. and Pigorsch, U. (2008). The Volatility of Realised Volatility. Econometric Reviews, 27(1-3), 46-78. Degiannakis, S. (2008). ARFIMAX and ARFIMAX- TARCH Realized Volatility Modeling. Journal of Applied Statistics, 35(10), 1169-1180. Degiannakis, S. and Floros, C. (2012). Modeling CAC40 Volatility Using Ultra-high Frequency Data, Research in International Business and Finance, 28, 68-81. Degiannakis, S. and Xekalaki, E. (2005). Predictability and Model Selection in the Context of ARCH Models. Journal of Applied Stochastic Models in Business and Industry, 21, 55-82. Degiannakis, S., Dent, P. and Floros, C. (2014). A Monte Carlo Simulation Approach to Forecasting Multi-period Value-at-Risk and Expected Shortfall Using the FIGARCH-skT Specification. The Manchester School, 82(1), 71-102. Degiannakis, S., Livada A. and Panas, E. (2008). Rolling-sampled parameters of ARCH and Levy-stable models. Applied Economics, 40(23), 3051-3067. Engle, R.F., Hong, C.H., Kane A. and Noh, J. (1993). Arbitrage valuation of variance forecasts with simulated options. Advances in Futures and Options Research, 6, 393-415. Fernandez, C. and Steel, M.F.J. (1998). On Bayesian modelling of fat tails and skewness, Journal of the American Statistical Association, 93, 359–371. Granger, C.W.J. and Pesaran, M.H. (2000). Economic and Statistical Measures of Forecasting Accuracy. Journal of Forecasting, 19, 537-560. Greene, W.H. (1997). Econometric Analysis, 3rd edition, Prentice Hall, New Jersey. Johnson, N.L., Kotz, S. and Balakrishnan, N. (1995). Continuous Univariate Distributions, Vol. 2, 2nd edition, John Wiley & Sons Ltd., New York. Kullback, S. and Leibler, R.A. (1951). On Information and Sufficiency. Annals of Mathematical Statistics, 22, 79-86. Mann, H.B. and Whitney, D.R. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics, 18, 50-60. Müller, U.A., Dacorogna, M.M., Davé, R.D., Olsen, R.B., Pictet, O.V. and VonWeizsäcker, J.E. (1997). Volatilities of Different Time Resolutions – Analyzing the Dynamics of Market Components. Journal of Empirical Finance, 4, 213-239. Pagan, A.R. and Schwert, G.W. (1990). Alternative Models for Conditional Stock Volatility. Journal of Econometrics, 45, 267-290. Rigobon, R. and Sack, B. (2003). Measuring the Reaction of Monetary Policy to the Stock Market. The Quarterly Journal of Economics, 118(2), 639-669. Schwarz, G. (1978). Estimating the Dimension of a Model. Annals of Statistics, 6, 461-464. Xekalaki, E. and Degiannakis, S. (2005). Evaluating Volatility Forecasts in Option Pricing in the Context of a Simulated Options Market. Computational Statistics and Data Analysis, 49(2), 611-629. Xekalaki, E. and Degiannakis, S. (2010). ARCH Models for Financial Applications. John Wiley & Sons Ltd., New York. |
| URI: | https://mpra.ub.uni-muenchen.de/id/eprint/96281 |
Available Versions of this Item
-
Evaluation of Realized Volatility Predictions from Models with Leptokurtically and Asymmetrically Distributed Forecast Errors. (deposited 21 Nov 2015 14:41)
- Evaluation of Realized Volatility Predictions from Models with Leptokurtically and Asymmetrically Distributed Forecast Errors. (deposited 05 Nov 2019 16:52) [Currently Displayed]
All papers reproduced by permission. Reproduction and distribution subject to the approval of the copyright owners.
 |
View Item |
