The Realized Volatility of FTSE-100 Futures Prices

By Nelson Areal, Stephen J. Taylor. 2002

175 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/6e083fe360eac5f075e120e671a347a4bda5ed2e

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Citations in the corpus

Forecasting Currency Volatility: A Comparison of Implied Volatilities and AR(FI)MA Models (2004, background): “…Similar decay patterns are also found for the logarithm of realized volatility by ABDL (2001) for exchange rate markets and by ABDE (2001) and Areal and Taylor (2002) for stock markets. introduced a flexible class of long memory processes, called autoregressive, fractionally integrated,…”
Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility (2007, methodology): “…empirical observations have similarly motivated the estimation of long-memory type ARFIMA models for realized volatilities in ABDL (2003), Areal and Taylor (2002), Deo, Hurvich, and Lu (2006), Koopman, Jungbacker, and Hol (2005), Martens, van Dijk, and de Pooter (2004), Pong, Shackleton, Taylor,…”
The Volatility of Realized Volatility (2005, methodology): “As already shown by Merton (1980) and extended by Comte and Renault (1998), Andersen and Bollerslev (1998), Andersen et al.”
Realised power variation and stochastic volatility models (2003, background): “…Andersen, Bollerslev, Diebold, and Ebens (2001), Barndorff-Nielsen and Shephard (2002), Andreou and Ghysels (2001), Bai, Russell, and Tiao (2000), Maheu and McCurdy (2002), Areal and Taylor (2002), Galbraith and Zinde-Walsh (2000), Bollerslev and Zhou (2002) and Bollerslev and Forsberg (2002).”
The economic value of volatility timing using “realized” volatility ☆ (2003, unknown): “This sampling frequency balances the benefits derived from large sample asymptotics and the detrimental impact of market microstructure effects (ABDE, 2001; ABDL, 2001a; Areal and Taylor, 2002). To account for serial correlation induced by microstructure effects, ABDE (2001) suggest prewhitening the”
Autoregressive Conditional Heteroscedasticity (ARCH) Models: A Review (2004, background): “O t h e r M e t h o d s o f V o l a t i l i t y M o d e l i n g “Stochastic volatility” models (Barndorff-Nielsen et al. (2001), Chib et al. (1998), Ghysels et al. (1996), Harvey and Shephard (1993), Jacquier et al. (1994), Shephard (1996), Taylor (1994)), “implied volatility” models (Day and Lewis ”
Econometric analysis of realized volatility and its use in estimating stochastic volatility models (2002, methodology): “The quadratic variation estimation of integrated volatility has recently been highlighted, following the initial draft of Barndorff-Nielsen and Shephard (2001a) and the concurrent independent work of Andersen and Bollerslev (1998a), by Andersen, Bollerslev, Diebold and Labys (2001) and Maheu and McC”
Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data—But Which Frequency to Use? (2008, unknown): “in the beginning, see also Bandi et al. (2008). Third, Andersen et al.”
Do Jumps Matter for Volatility Forecasting? Evidence from Energy Markets (2016, result): “…paper also connects with the growing literature that uses intraday data to obtain more accurate volatility forecasts (Andersen and Bollerslev, 1998; Areal and Taylor, 2002; Martens and Zein, 2004; Giot and Laurent, 2007; Corsi and Reno, 2009; Corsi et al., 2010; Chung et al., 2011; Patton and…”
Forecasting Daily Variability of the S&P 100 Stock Index Using Historical, Realised and Implied Volatility Measurements (2005, background): “The descriptive statistics in Table 1 for realised volatility mirror earlier findings for stock market data as reported by Andersen, Bollerslev, Diebold, and Ebens (2001), Areal and Taylor (2002) and Giot and Laurent (2004).”
A Discrete-Time Model for Daily S & P500 Returns and Realized Variations: Jumps and Leverage Effects (2009, background): “The recent empirical analysis in Bollerslev et al. (2005) also points toward the existence of a contemporaneous leverage effect in the form of cross-correlated high-frequency returns and absolute returns.”
Robust estimation of intraweek periodicity in volatility and jump detection (2011, methodology): “, 2002), efficient estimation of daily volatility using high-frequency returns (Areal and Taylor, 2002; Bos, 2008) and, as we show in this paper, for intraday jump detection.”