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Multiple Days Ahead Realized Volatility Forecasting: Single, Combined and Average Forecasts

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  • Degiannakis, Stavros

Abstract

The task of this paper is the enhancement of realized volatility forecasts. We investigate whether a mixture of predictions (either the combination or the averaging of forecasts) can provide more accurate volatility forecasts than the forecasts of a single model.We estimate long-memory and heterogeneous autoregressive models under symmetric and asymmetric distributions for the major European Union stock market indices and the exchange rates of the Euro. The majority of models provide qualitatively similar predictions for the next trading day’s volatility forecast. However, with regard to the one-week forecasting horizon, the heterogeneous autoregressive model is statistically superior to the long-memory framework. Moreover, for the two-weeks-ahead forecasting horizon, the combination of realized volatility predictions increases the forecasting accuracy and forecast averaging provides superior predictions to those supplied by a single model. Finally, the modeling of volatility asymmetry is important for the two-weeks-ahead volatility forecasts.

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  • Degiannakis, Stavros, 2018. "Multiple Days Ahead Realized Volatility Forecasting: Single, Combined and Average Forecasts," MPRA Paper 96272, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:96272
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    2. Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna, 2022. "A moving average heterogeneous autoregressive model for forecasting the realized volatility of the US stock market: Evidence from over a century of data," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 384-400, January.
    3. M. Dashti Moghaddam & Jiong Liu & R. A. Serota, 2021. "Implied and realized volatility: A study of distributions and the distribution of difference," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2581-2594, April.
    4. Liang, Chao & Li, Yan & Ma, Feng & Wei, Yu, 2021. "Global equity market volatilities forecasting: A comparison of leverage effects, jumps, and overnight information," International Review of Financial Analysis, Elsevier, vol. 75(C).
    5. Chen, Zhonglu & Liang, Chao & Umar, Muhammad, 2021. "Is investor sentiment stronger than VIX and uncertainty indices in predicting energy volatility?," Resources Policy, Elsevier, vol. 74(C).

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    More about this item

    Keywords

    averaging forecasts; combining forecasts; heterogeneous autoregressive; intra-day data; long memory; model confidence set; predictive ability; realized volatility; ultra-high frequency;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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