Volatility measurement with pockets of extreme return persistence

Andersen, Torben G.; Li, Yingying; Todorov, Viktor; Zhou, Bo

doi:10.1016/j.jeconom.2020.11.005

Cited by 21 publications

(2 citation statements)

References 53 publications

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“…Therefore, we believe that mixed semimartingales with H > 1 2 have the potential to serve as a semiparametric model of return persistence, which has been documented in high-frequency data in a series of recent work (see e.g. [4,5,15,28]). We shall leave it to future work to examine this direction of research.…”

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confidence: 89%

Rate-optimal estimation of mixed semimartingales

Chong¹,

Delerue²,

Mies³

2022

Preprint

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Consider the sum Y = B + B(H) of a Brownian motion B and an independent fractional Brownian motion B(H) with Hurst parameter H ∈ (0, 1). Surprisingly, even though B(H) is not a semimartingale, Cheridito proved in [Bernoulli 7 (2001) that Y is a semimartingale if H > 3/4. Moreover, Y is locally equivalent to B in this case, so H cannot be consistently estimated from local observations of Y . This paper pivots on a second surprise in this model: if B and B(H) become correlated, then Y will never be a semimartingale, and H can be identified, regardless of its value. This and other results will follow from a detailed statistical analysis of a more general class of processes called mixed semimartingales, which are semiparametric extensions of Y with stochastic volatility in both the martingale and the fractional component. In particular, we derive consistent estimators and feasible central limit theorems for all parameters and processes that can be identified from high-frequency observations. We further show that our estimators achieve optimal rates in a minimax sense. The estimation of mixed semimartingales with correlation is motivated by applications to high-frequency financial data contaminated by rough noise.

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confidence: 89%

Rate-optimal estimation of mixed semimartingales

Chong¹,

Delerue²,

Mies³

2022

Preprint

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“…22 The differencing method has been used in high‐frequency econometrics recently; see, for example, Todorov (2013), Hansen and Lunde (2014), Andersen, Li, Todorov, and Zhou (2020). …”

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confidence: 99%

A ReMeDI for Microstructure Noise

Linton

2022

ECTA

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We introduce the Realized moMents of Disjoint Increments (ReMeDI) paradigm to measure microstructure noise (the deviation of the observed asset prices from the fundamental values caused by market imperfections). We propose consistent estimators of arbitrary moments of the microstructure noise process based on high‐frequency data, where the noise process could be serially dependent, endogenous, and nonstationary. We characterize the limit distributions of the proposed estimators and construct confidence intervals under infill asymptotics. Our simulation and empirical studies show that the ReMeDI approach is very effective to measure the scale and the serial dependence of microstructure noise. Moreover, the estimators are quite robust to model specifications, sample sizes, and data frequencies.

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The volatility connectedness of US industries: The role of investor sentiment

Anghel,

Caraiani

2024

Economics Letters

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Volatility measurement with pockets of extreme return persistence

Cited by 21 publications

References 53 publications

Rate-optimal estimation of mixed semimartingales

Rate-optimal estimation of mixed semimartingales

A ReMeDI for Microstructure Noise

The volatility connectedness of US industries: The role of investor sentiment

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