A Comparison of Seasonal Adjustment Methods When Forecasting Intraday Volatility
By M. Martens, Yuanchen Chang, Stephen J. Taylor. 2002
128 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/bb7bd07183eb154a0be8b1f0eda7f9da52f3f0d8
How researchers see this paper
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In Economics and Econometrics
- Eric Ghysels
- Xilong Chen
- Paul Embrechts
- Alexandra Dias
- Kris Boudt
- Sabéastien Laurent
- Abdelhakim Aknouche
- Christophe Croux
In Finance
- Alexandra Dias
- Eric Ghysels
- Kris Boudt
- Xilong Chen
- Paul Embrechts
- Christophe Croux
- Sabéastien Laurent
- Abdelhakim Aknouche
Citations in the corpus
- Robust estimation of intraweek periodicity in volatility and jump detection (2011, methodology): “…variation in volatility and the impact of news on intraday volatility (Andersen et al., 2003; Dominguez and Panthaki, 2006), forecasting of intraday variance (Martens et al., 2002) and efficient estimation of daily volatility using high-frequency returns (Areal and Taylor, 2002; Bos, 2008).”
- Modeling exchange rate dependence dynamics at different time horizons (2010, background): “…a seasonal factor corresponding to each sixty-minute interval of the day as the average of the squared returns observed every day 5 See for instance Martens et al. (2002). for that time interval, s 2 i = 1 T T t=1˜x 2 it i = 1, 2, . . . , 24, where T is the number of days in the sample and˜x it is…”
- News - Good or Bad - and its Impact on Volatility Predictions over Multiple Horizons (2011, background): “9See e.g. Andersen and Bollerslev (1997), Andersen and Bollerslev (1998), Bollen and Inder (2002), Dacorogna, Gençay, Müller, Olsen, and Pictet (2001), Martens, Chang, and Taylor (2002), among others.”
- Modeling and Explaining the Dynamics of European Union Allowance Prices at High-Frequency (2012, unknown): “There are several ways to deal with the intraday seasonality (see, e.g., Martens, Chang, and Taylor (2002)).”
- Quasi‐maximum likelihood estimation of periodic GARCH and periodic ARMA‐GARCH processes (2009, background): “…useful and appropriate for modelling many time series encountered in practice, which are characterized by a stochastic conditional variance with periodic dynamics (see also Franses and Paap, 2000, 2004; Ghysels and Osborn, 2001; Taylor, 2004, 2006; Martens et al., 2002, and the references therein).”