Intraday periodicity and volatility persistence in financial markets

By Torben G. Andersen, Tim Bollerslev. 1997

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Citations in the corpus

Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction (2019, background): “Given the strong evidence for the intraday periodicity of returns volatility (Andersen & Bollerslev, 1997), we also include the time-of-day as an additional exogenous input.”
Semi-Parametric Comparison of Stochastic Volatility Models using Realized Measures (2006, methodology): “This is in line with Bollerslev and Zhou (2002), who assume a zero drift term and justify this with the fact that there is very little predictive variation in the mean of high frequency returns, as supported by the empirical findings of Andersen and Bollerslev (1997).”
The Realized Volatility of FTSE-100 Futures Prices (2002, unknown): “Andersen and Bollerslev (1997) use regression methodology to produce smooth variance multipliers.”
Granger Causality in Risk and Detection of Extreme Risk Spillover Between Financial Markets (2009, methodology): “Exchange rate changes are calculated in the same way as in Anderson and Bollerslev (1997) and Diebold et al. (1999). We rst calculate the average log bid and log ask prices to get a log price, then calculate changes as the di¤erences between log prices at consecutive time points.”
Volatility Transmission Models: A Survey (2006, unknown): “However, this information wealth introduces new modelling difficulties, such as regular components or seasonality (see Andersen and Bollerslev (1997)).”
Moving Average-Based Estimators of Integrated Variance (2008, background): “This kind of shape is well documented in practice, see, e.g., Andersen and Bollerslev (1997, 1998).”
Variational sums and power variation: a unifying approach to model selection and estimation in semimartingale models (2003, background): “…Labys (2001), Andersen, Bollerslev, ever, empirically it seemed to be more attractive to use absolute values of the returns than squares, see e.g. Andersen and Bollerslev (1997) (1998), Taylor (1986, Ch.2), Cao and Tsay (1992), Ding, Granger, and Engle (1993), West and Cho (1995), Granger and…”
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): “Meanwhile, as shown by Andersen and Bollerslev (1997b), a failure to properly account for this strong intraday periodicity in volatility may seriously distort any inference regarding the underlying dynamic dependencies in the returns.”
Robust estimation of intraweek periodicity in volatility and jump detection (2011, methodology): “As in Andersen and Bollerslev (1997), one could also consider the calendar effects jointly with macroeconomic news effects. Then r1,i, . . . , rni,i are the returns observed on the same time of the day and day of the week as ri, on a day with the same macroeconomic news releases as for ri. The non-p”
Risk and return: Long-run relations, fractional cointegration, and return predictability $ (2013, methodology): “See, e.g., Harris (1986) and Andersen and Bollerslev (1997b).”
Is the diurnal pattern sufficient to explain intraday variation in volatility? A nonparametric assessment (2017, unknown): “It extends previous work of, e.g., Andersen and Bollerslev (1997); Andersen, Dobrev, and Schaumburg (2012); Boudt, Croux, and Laurent (2011); Taylor and Xu (1997); Todorov and Tauchen (2012) to the noisy setting (see also Hecq, Laurent, and Palm, 2012).”
On the estimation of integrated covariance matrices of high dimensional diffusion processes (2010, background): “For example, it is commonly observed that the stock intraday volatility tends to be U-shaped [see, e.g., Admati and Pfleiderer (1988), Andersen and Bollerslev (1997)] or exhibits some other patterns [see, e.g., Andersen and Bollerslev (1998)].”
Volatility is rough (2014, methodology): “As mentioned earlier, the long memory property of the volatility process has been widely accepted as a stylized fact since the seminal analyses of Ding et al. (1993), Andersen and Bollerslev (1997) and Andersen et al. (2001b).”
Estimating the Quadratic Covariation Matrix from Noisy Observations: Local Method of Moments and Efficiency (2013, methodology): “The seasonal factor ϕl(t) is specified in terms of intraday volatility functions estimated for S&P 500 equity data by the procedure in Andersen and Bollerslev [3]. ϕ1(t) and ϕ2(t) are based on cross-sectional averages of the 50 most and 50 least liquid stocks, respectively, which yields a pronounced”
Realized Volatility: A Review (2008, methodology): “More recently, Andersen and Bollerslev (1998) showed that ex post daily foreign exchange volatility is best measured by aggregating 288 squared five-minute returns.”