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On forecasting daily stock volatility: the role of intraday information and market conditions

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On forecasting daily stock volatility: the role of intraday information and market conditions. / Fuertes, Ana-Maria; Izzeldin, Marwan; Kalotychou, Elena.

In: International Journal of Forecasting, Vol. 25, No. 2, 06.01.2009, p. 259-281.

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Fuertes, A-M, Izzeldin, M & Kalotychou, E 2009, 'On forecasting daily stock volatility: the role of intraday information and market conditions', International Journal of Forecasting, vol. 25, no. 2, pp. 259-281. https://doi.org/10.1016/j.ijforecast.2009.01.006

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Fuertes, Ana-Maria ; Izzeldin, Marwan ; Kalotychou, Elena. / On forecasting daily stock volatility: the role of intraday information and market conditions. In: International Journal of Forecasting. 2009 ; Vol. 25, No. 2. pp. 259-281.

Bibtex

@article{1b0def85b65545b9bf8eed9b72d9c1d4,
title = "On forecasting daily stock volatility: the role of intraday information and market conditions",
abstract = "Several recent studies advocate the use of nonparametric estimators of daily price vari- ability that exploit intraday information. This paper compares four such estimators, realised volatility, realised range, realised power variation and realised bipower variation, by examining their in-sample distributional properties and out-of-sample forecast ranking when the object of interest is the conventional conditional variance. The analysis is based on a 7-year sample of transaction prices for 14 NYSE stocks. The forecast race is conducted in a GARCH framework and relies on several loss functions. The realized range fares relatively well in the in-sample .t analysis, for instance, regarding the extent to which it brings normality in returns. However, overall the realised power variation provides the most accurate 1-day-ahead forecasts. Fore- cast combination of all four intraday measures produces the smallest forecast errors in about half of the sampled stocks. A market conditions analysis reveals that the additional use of intraday data on day t .. 1 to forecast volatility on day t is most advantageous when day t is a low volume or an up-market day. The results have implications for value-at-risk analysis.",
keywords = "Conditional variance, Realised Volatility, Nonparametric estimators, Intraday prices, Superior predictive ability",
author = "Ana-Maria Fuertes and Marwan Izzeldin and Elena Kalotychou",
year = "2009",
month = jan,
day = "6",
doi = "10.1016/j.ijforecast.2009.01.006",
language = "English",
volume = "25",
pages = "259--281",
journal = "International Journal of Forecasting",
issn = "0169-2070",
publisher = "Elsevier Science B.V.",
number = "2",

}

RIS

TY - JOUR

T1 - On forecasting daily stock volatility: the role of intraday information and market conditions

AU - Fuertes, Ana-Maria

AU - Izzeldin, Marwan

AU - Kalotychou, Elena

PY - 2009/1/6

Y1 - 2009/1/6

N2 - Several recent studies advocate the use of nonparametric estimators of daily price vari- ability that exploit intraday information. This paper compares four such estimators, realised volatility, realised range, realised power variation and realised bipower variation, by examining their in-sample distributional properties and out-of-sample forecast ranking when the object of interest is the conventional conditional variance. The analysis is based on a 7-year sample of transaction prices for 14 NYSE stocks. The forecast race is conducted in a GARCH framework and relies on several loss functions. The realized range fares relatively well in the in-sample .t analysis, for instance, regarding the extent to which it brings normality in returns. However, overall the realised power variation provides the most accurate 1-day-ahead forecasts. Fore- cast combination of all four intraday measures produces the smallest forecast errors in about half of the sampled stocks. A market conditions analysis reveals that the additional use of intraday data on day t .. 1 to forecast volatility on day t is most advantageous when day t is a low volume or an up-market day. The results have implications for value-at-risk analysis.

AB - Several recent studies advocate the use of nonparametric estimators of daily price vari- ability that exploit intraday information. This paper compares four such estimators, realised volatility, realised range, realised power variation and realised bipower variation, by examining their in-sample distributional properties and out-of-sample forecast ranking when the object of interest is the conventional conditional variance. The analysis is based on a 7-year sample of transaction prices for 14 NYSE stocks. The forecast race is conducted in a GARCH framework and relies on several loss functions. The realized range fares relatively well in the in-sample .t analysis, for instance, regarding the extent to which it brings normality in returns. However, overall the realised power variation provides the most accurate 1-day-ahead forecasts. Fore- cast combination of all four intraday measures produces the smallest forecast errors in about half of the sampled stocks. A market conditions analysis reveals that the additional use of intraday data on day t .. 1 to forecast volatility on day t is most advantageous when day t is a low volume or an up-market day. The results have implications for value-at-risk analysis.

KW - Conditional variance

KW - Realised Volatility

KW - Nonparametric estimators

KW - Intraday prices

KW - Superior predictive ability

U2 - 10.1016/j.ijforecast.2009.01.006

DO - 10.1016/j.ijforecast.2009.01.006

M3 - Journal article

VL - 25

SP - 259

EP - 281

JO - International Journal of Forecasting

JF - International Journal of Forecasting

SN - 0169-2070

IS - 2

ER -