Activity Signature Functions for High-Frequency Data Analysis

By V. Todorov, George Tauchen. 2010

102 citations recorded by the source corpus.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/cc830945c457fce8c3f00bf37176799caaba40ae

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Citations in the corpus

On the jump activity index for semimartingales (2012, unknown): “Incidentally, Todorov and Tauchen (2009) also studied the inference on the activity signature functions for high frequency data.”
Testing for pure-jump processes for high-frequency data (2015, background): “On the other hand, in recent years pure-jump processes have also been accepted as an alternative model for log price processes or even the latent spot volatility process to the classic models mentioned earlier; see, for example, Todorov and Tauchen (2010, 2014) and references therein.”
Testing whether jumps have finite or infinite activity (2011, methodology): “t is present. We compute certain power variations of the increments, suitably truncated and/or sampled at diﬀerent frequencies. Related methodologies are being utilized by other authors. For example, Todorov and Tauchen (2010) use the test statistics of A¨ıt-Sahalia and Jacod (2009b), study its loga”
MODELING HIGH-FREQUENCY FINANCIAL DATA BY PURE JUMP PROCESSES (2012, methodology): “In recent years, pure jump models have been widely used as an alternative model for price process to the classical model, which has a continuous martingale component; see Todorov and Tauchen (2010) and references within.”
Nonparametric tests for pathwise properties of semimartingales (2011, background): “If we have an estimate α̂ of the BG index [3, 25, 26], then we may choose β > 1 2−α̂ (recall that 1 2−α ∈ [1/2,1[).”
Limit Theorems for Power Variations of Pure-Jump Processes with Application to Activity Estimation (2011, methodology): “Using again Lemma 1(b) in Todorov and Tauchen (2010) we have Eni−1 ( ∆−(2+ι)p/βn |∆ni Y (τ)|(2+ι)p ) P−→ E ( |L1|(2+ι)p ) , (6.24) for i = 1, 2, ..., 2 [ t 2∆n ] and 0 ι β/p − 2.”
Is the diurnal pattern sufficient to explain intraday variation in volatility? A nonparametric assessment (2017, unknown): “While the prevailing evidence is slightly mixed, it appears largely affirmative (see, e.g., Aı̈t-Sahalia and Jacod, 2012b; Kolokolov and Renò, 2017; Todorov and Tauchen, 2010).”
Estimation of continuous-time stochastic volatility models with jumps using high-frequency data (2009, result): “Moreover, recent results in Ait-Sahalia and Jacod (2008a) and Todorov and Tauchen (2008) suggest that the jumps in asset prices are more “active” than the compound Poisson jumps typically used in parametric specifications.”
Estimation of Jump Tails (2011, methodology): “…the form of the stochastic volatility, and in particular is robust to the presence of jumps in the volatility, as explored parametrically by Barndorff-Nielsen and Shephard (2001) and more recently using non-parametric procedures by, e.g., Jacod and Todorov (2010) and Todorov and Tauchen (2010).”