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Quantile forecasts for financial volatilities based on parametric and asymmetric models

Title
Quantile forecasts for financial volatilities based on parametric and asymmetric models
Authors
Choi J.-E.Shin D.W.
Ewha Authors
신동완
SCOPUS Author ID
신동완scopus
Issue Date
2019
Journal Title
Journal of the Korean Statistical Society
ISSN
1226-3192JCR Link
Citation
Journal of the Korean Statistical Society vol. 48, no. 1, pp. 68 - 83
Keywords
EGARCH modelForecast intervalLHAR modelRealized volatilitySkew-t distributionValue at riskVolatility index
Publisher
Korean Statistical Society
Indexed
SCIE; SCOPUS; KCI WOS scopus
Document Type
Article
Abstract
For financial volatilities such as realized volatility and volatility index, a new parametric quantile forecast strategy is proposed, focusing on forecast interval and value at risk (VaR) forecast. This fully addresses asymmetries in 3 parts: mean, volatility and distribution. The asymmetries are addressed by the LHAR (leverage heterogeneous autoregressive) model of McAleer and Medeiros (2008) and Corsi and Reno (2009) for the mean part, by the EGARCH model for the volatility part, and by the skew-t distribution for the error distribution part. The method is applied to the realized volatilities and the volatility indexes of the US S&P 500 index, the US NASDAQ index, the Korea KOSPI index in which significant asymmetries are identified. Considerable out-of-sample forecast improvements of the forecast interval and VaR forecast are demonstrated for the volatilities: forecast intervals of volatilities have better coverages with shorter lengths and VaR forecasts of volatility indexes have better violations if asymmetries are properly addressed rather than ignored. The proposed parametric method reveals considerably better out-of-sample performance than the recently proposed semiparametric quantile regression approach of Zikes and Barunik (2016). © 2018 The Korean Statistical Society
DOI
10.1016/j.jkss.2018.08.005
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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