Estimating Daily Volatility in Financial Markets Utilizing Intraday Data

By B. Bollen, B. Inder. 2002

154 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/3bef1254c0b2d5b6922c1a806ca811bd3146b9d7

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Citations in the corpus

MIDAS Regressions: Further Results and New Directions (2007, methodology): “Filtering, as an approach to microstructure noise correction, was applied in Ebens (1999), Andersen et al. (2001), Maheu and McCurdy (2002), and Bollen and Inder (2002).”
A Discrete-Time Model for Daily S & P500 Returns and Realized Variations: Jumps and Leverage Effects (2009, unknown): “…type procedures to explicitly account for the serial correlation induced by the market microstructure noise as in, e.g., Andersen et al. (2001a), Bollen and Inder (2002), Corsi et al. (2001), Hansen and Lunde (2006), and Zhou (1996), along with sub-sampling schemes designed to adjust for the…”
Realised quantile-based estimation of the integrated variance (2010, methodology): “, 2000; Bandi and Russell, 2008), pre-filtering (Andersen et al., 2001; Bollen and Inder, 2002; Hansen et al., 2008), model-based corrections (e.”
Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility (2003, unknown): “7 A number of recent studies have investigated the practical choice of ) together with different filtering procedures in order to best mitigate the impact of market microstructure effects in the construction of realized volatility measures (e.g., ABDL, 2000; Aït-Sahalia and Mykland, 2003; Andreou an”
Properties of Range-Based Volatility Estimators (2012, unknown): “This is in line with empirical finding of Bollen and Inder (2002).”
Designing Realised Kernels to Measure the Ex-Post Variation of Equity Prices in the Presence of Noise (2008, methodology): “The moving average filter of Andersen, Bollerslev, Diebold, and Ebens (2001) and Hansen, Large, and Lunde (2008) and the autoregressive filter of Bollen and Inder (2002), are estimators that use pre-whitening techniques — see also Bandi and Russell (2008).”
Can Internet search queries help to predict stock market volatility (2016, unknown): “In particular, following Andersen, Bollerslev, Christoﬀersen and Diebold (2006) as well as Bollen and Inder (2002) we estimate autoregressive models with diﬀerent lag length and augment these with lagged search queries log - SQ t − 1 : We consider the lag lengths one and three.”
No-Arbitrage Semi-Martingale Restrictions for Continuous-Time Volatility Models subject to Leverage Effects, Jumps and i.i.d. Noise: Theory and Testable Distributional Implications (2007, unknown): “…issues are the subject of intense scrutiny within a rapidly expanding literature, see e.g., Aı̈t-Sahalia et al. (2005), ABDL (2000a,b, 2003a,b), Bandi and Russell (2004a,b), Bollen and Inder (2002), Corsi et al. (2001), Hansen and Lunde (2006), Oomen (2005), Zhang et al. (2005), and Zhou (1996).”
Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility (2007, unknown): “C display the separate measurements of the jump components (again in standard deviation form) based on the truncated estimator in equation 10 To mitigate the impact of market microstructure frictions in the construction of unbiased and efficient realized volatility measurements, a number of recent s”
Microstructure Noise, Realized Variance, and Optimal Sampling (2008, background): “(18) Since the noise moments depend on the sampling frequency in general, the components of the MSE, with the exception of Q and V , ought to be evaluated for every M .”
VOLATILITY AND CORRELATION FORECASTING (2006, background): “3 were first discussed by Bollerslev and Wooldridge (1992), while Ling and McAleer (2003) provide a more recent account of some of the subsequent important theoretical developments. The use of a fat tailed multivariate student t-distribution in the estimation of multivariate GARCH models was first c”
An analysis of stock market efficiency: Developed vs Islamic stock markets using MF-DFA (2014, background): “The quantity μ ([t1, t2]) is actually a measure of the volatility on the time interval [t1t2] [34].”
Regular and Modified Kernel-Based Estimators of Integrated Variance: The Case with Independent Noise (2004, methodology): “The moving average filter used by Andersen, Bollerslev, Diebold & Ebens (2001) and the autoregressive filter used by Bollen & Inder (2002), are estimators that use pre-whiteningtechniques, and Bandi & Russell (2004) analyze optimal sampling of pre-whiten series.”
Realized Volatility: A Review (2008, methodology): “For example, in Bollen and Inder (2002), an autoregressive (AR) filter was used, while a moving average (MA) filter was considered in Ebens (1999), Maheu and McCurdy (2002), and ABDE (2001). More recently, Hansen, Large and Lunde (2006), hereafter HLL (2006), showed that the MA(1) structure consider”
Properties of Bias-Corrected Realized Variance Under Alternative Sampling Schemes (2005, methodology): “…2004; Oomen, 2003, 2005), pre-filtering the price data in order to purge out serial correlation (Andersen, Bollerslev, Diebold, and Labys, 2003; Bollen and Inder, 2002), bias correction based on a model for the noise (Corsi, Zumbach, Müller and Dacorogna, 2001), and sub-sampling Zhang (2004);…”
Finite-size effect and the components of multifractality in financial volatility (2009, unknown): “This simple definition is widely adopted in the Econophysics community [66–68].”
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.”