Quantile forecasts for financial volatilities based on parametric and asymmetric models.

*(English)*Zbl 1417.37296The authors introduce a parametric quantile forecast strategy for interval and Value-at-Risk forecasts. The strategy aims to address asymmetries in three components including the mean, the volatility and the distribution. The authors apply the proposed strategy to the realized volatilities and volatility indices. The main methodology for quantile forecast is discussed in Section 2, where the models for asymmetries in the three components are presented. In Section 3, the asymmetric features are identified using some descriptive statistics from the realized volatility data sets, and the in-the-sample estimation results are provided and discussed. The out-of-sample forecast performance of the proposed method is discussed in Section 4. Specifically, forecast intervals for realized volatilities and volatility indices are studied.

Reviewer: Tak Kuen Siu (Sydney)

##### MSC:

37N40 | Dynamical systems in optimization and economics |

37M10 | Time series analysis of dynamical systems |

62P20 | Applications of statistics to economics |

91B30 | Risk theory, insurance (MSC2010) |

##### Keywords:

realized volatility; volatility index; forecast interval; value at risk; LHAR model; EGARCH model; skew-t distribution##### Software:

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\textit{J.-E. Choi} and \textit{D. W. Shin}, J. Korean Stat. Soc. 48, No. 1, 68--83 (2019; Zbl 1417.37296)

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