Degiannakis, Stavros
(2008):
*ARFIMAX and ARFIMAX-TARCH Realized Volatility Modeling.*
Published in: Journal of Applied Statistics
, Vol. 10, No. 35
(2008): pp. 1169-1180.

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## Abstract

ARFIMAX models are applied in estimating the intra-day realized volatility of the CAC40 and DAX30 indices. Volatility clustering and asymmetry characterize the logarithmic realized volatility of both indices. ARFIMAX model with time-varying conditional heteroscedasticity is the best performing specification and, at least in the case of DAX30, provides statistically superior next trading day’s realized volatility forecasts.

Item Type: | MPRA Paper |
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Original Title: | ARFIMAX and ARFIMAX-TARCH Realized Volatility Modeling |

Language: | English |

Keywords: | ARFIMAX, Realized Volatility, TARCH, Volatility Forecasting. |

Subjects: | C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes 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 |

Item ID: | 80465 |

Depositing User: | Dr. Stavros Degiannakis |

Date Deposited: | 01 Aug 2017 05:33 |

Last Modified: | 30 Sep 2019 04:47 |

References: | Andersen, T. and Bollerslev, T. (1998). Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts. International Economic Review, 39, pp. 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, pp. 457-477. Andersen, T., Bollerslev, T., Diebold, F.X. and Ebens, H. (2001a). The Distribution of Realized Stock Return Volatility. Journal of Financial Economics, 61, pp. 43-76. Andersen, T., Bollerslev, T., Diebold, F.X. and Labys, P. (2001b). The Distribution of Exchange Rate Volatility. Journal of the American Statistical Association, 96, pp. 42-55. Anderson, T.W. and Darling, D.A. (1954). A Test of Goodness of Fit. Journal of the American Statistical Association, 49, pp. 765-769. Angelidis, T. and Degiannakis, S. (2009). Volatility Forecasting: Intra-day vs. Inter-day Models. Journal of International Financial Markets, Institutions and Money, forthcoming. Baillie, R.T., Chung, C.F., and Tieslau, M.A. (1996). Analysing Inflation by the Fractionally Integrated ARFIMA-GARCH Model. Journal of Applied Econometrics, 11, pp. 23–40. Barndorff-Nielsen, O.E. and Shephard, N. (2005). How Accurate is the Asymptotic Approximation to the Distribution of Realised Volatility? in D. Andrews, J. Powell, P. Ruud, and J. Stock (Eds.) Identification and Inference for Econometric Models, Cambridge: Cambridge University Press. Bollerslev, T. and Wright, J.H. (2001). Volatility Forecasting, High-Frequency Data and Frequency Domain Inference. Review of Economics and Statistics, 83, pp. 596-602. Corsi, F., Kretschmer, U., Mittnik, S. and Pigorsch, C. (2005). The Volatility of Realised Volatility. Center for Financial Studies, Working Paper, 33. Doornik, J.A. and Ooms, M. (2006). A Package for Estimating, Forecasting and Simulating Arfima Models: Arfima Package 1.04 for Ox. Nuffield College, Oxford, Working Paper. Ebens, H. (1999). Realized Stock Volatility. Johns Hopkins University, Department of Economics, Working Paper, 420. Giot, P. and Laurent, S. (2004). Modelling Daily Value-at-Risk Using Realized Volatility and ARCH Type Models. Journal of Empirical Finance, 11, pp. 379 – 398. Glosten, L., Jagannathan, R. and Runkle, D. (1993). On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. Journal of Finance, 48, pp. 1779–1801. Granger, C.W.J. (1980). Long Memory Relationships and the Aggregation of Dynamic Models. Journal of Econometrics, 14, pp. 227-238. Hansen, P.R. (2005). A Test for Superior Predictive Ability. Journal of Business and Economic Statistics, 23, pp. 365-380. Hansen, P.R. and Lunde, A. (2006). Consistent Ranking of Volatility Models. Journal of Econometrics, 131, pp. 97-121. Hauser, M.A. and Kunst, R.M. (1998). Fractionally Integrated Models With ARCH Errors: With an Application to the Swiss 1-Month Euromarket Interest Rate. Review of Quantitative Finance and Accounting, 10, pp. 95-113. Karatzas, I. and Shreve, S.E. (1988). Brownian Motion and Stochastic Calculus. Springer Verlag. Kayahan, B., Saltoglu, T. and Stengos, T. (2002). Intra-Day Features of Realized Volatility: Evidence from an Emerging Market. International Journal of Business and Economics, 1(1), pp. 17-24. Koopman, S.J., Jungbacker, B., and Hol, E. (2005). Forecasting Daily Variability of the S&P100 Stock Index Using Historical, Realised and Implied Volatility Measurements. Journal of Empirical Finance, 12, pp. 445–475. Laurent S. and Peters, J.-P. (2006). G@RCH 4.2, Estimating and Forecasting ARCH Models, London: Timberlake Consultants Press. Lilliefors, H.W. (1967). On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown. Journal of the American Statistical Association, 62, pp. 399-402. Martens, M. (2002). Measuring and forecasting S&P500 index-futures volatility using high-frequency data. Journal of Futures Markets, 22, pp. 497–518. Politis, D.N. and Romano, J.P. (1994). The Stationary Bootstrap. Journal of the American Statistical Association, 89, pp. 1303-1313. Schwarz, G. (1978). Estimating the Dimension of a Model. Annals of Statistics, 6, pp. 461-464. |

URI: | https://mpra.ub.uni-muenchen.de/id/eprint/80465 |