Measuring and Forecasting S&P 500 Index-Futures Volatility Using High-Frequency Data

By M. Martens. 2002

263 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/e38ee7ab4fd863080868d9c267220733938491a8

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Citations in the corpus

Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data—But Which Frequency to Use? (2008, unknown): “…′t ,c−o + I∑ i=1 rt−1+ih,hr ′t−1+ih,h (1) where rt ,c−o is the (N × 1) vector of close-to-open (overnight) returns from day t − 1 (close) to day t (open).4 Martens (2002) documents that the overnight volatility represents an important fraction of total daily volatility, hence incorporating…”
Measuring and Forecasting Volatility in Chinese Stock Market Using HAR-CJ-M Model (2013, methodology): “According to researches of Martens [20] and Koopman et al.”
Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility (2007, unknown): “…accuracy through the use of realized volatilities are, of course, entirely consistent with the earlier empirical evidence in ABDL (2003), Bollerslev and Wright (2001), and Martens (2002), among others, and further corroborated by the analytical results of Andersen, Bollerslev, and Meddahi (2004).”
Comparison of Volatility Measures: A Risk Management Perspective (2010, unknown): “Gallo (2001), Martens (2002) , Fleming, Kirby & Ostdiek (2003)”
Forecasting Daily Variability of the S&P 100 Stock Index Using Historical, Realised and Implied Volatility Measurements (2005, unknown): “Some of these loss functions are also considered by Andersen, Bollerslev, and Lange (1999), Martens (2002) and Hansen and Lunde (2003b).”
A Reduced Form Framework for Modeling Volatility of Speculative Prices Based on Realized Variation Measures (2007, methodology): “The most common approach for dealing with this issue when modeling and forecasting realized volatilities is to scale the intraday measures and/or model forecasts by a constant to make them unconditionally unbiased for the total daily variation; see, e.g., Martens (2002) , Fleming, Kirby and Ostdiek”
Realized Volatility: A Review (2008, result): “( 1999 ), and Martens ( 2001 , 2002 ), among others.”
Realized volatility forecasting and market microstructure noise (2011, background): “…Bollerslev, Diebold and Labys (2003), Areal and Taylor (2002), Corsi (2009), Deo, Hurvich and Lu (2006), Koopman, Jungbacker and Hol (2005), Martens (2002), Pong, Shackleton, Taylor and Xu (2004), and Thomakos and Wang (2003), among many others, involving estimation of reduced form…”
Forecasting Volatility Using High-Frequency Data (2011, methodology): “GARCHX models of this type have been estimated by Blair et al. (2004), Martens (2001, 2002), Engle (2002), Barndorff-Nielsen and Shephard (2007), Barndorff-Nielsen et al. (2010), amongst others.”
Practical Issues in the Analysis of Univariate GARCH Models (2009, background): “Variables that have been shown to help predict volatility are trading volume, macroeconomic news announcements ([58], [43], [17]), implied volatility from option prices and realized volatility ([82], [11]), overnight returns ([46], [68]), and after hours realized volatility ([21])”