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Preprint
Data-Driven Fixed-Point Tuning for Truncated Realized Variations
arXiv.org
02 Nov 2023
Abstract
Many methods for estimating integrated volatility and related functionals of
semimartingales in the presence of jumps require specification of tuning
parameters for their use. In much of the available theory, tuning parameters
are assumed to be deterministic, and their values are specified only up to
asymptotic constraints. However, in empirical work and in simulation studies,
they are typically chosen to be random and data-dependent, with explicit
choices in practice relying on heuristics alone. In this paper, we consider
novel data-driven tuning procedures for the truncated realized variations of a
semimartingale with jumps, which are based on a type of stochastic fixed-point
iteration. Being effectively automated, our approach alleviates the need for
delicate decision-making regarding tuning parameters, and can be implemented
using information regarding sampling frequency alone. We show our methods can
lead to asymptotically efficient estimation of integrated volatility and
exhibit superior finite-sample performance compared to popular alternatives in
the literature.
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Details
- Title
- Data-Driven Fixed-Point Tuning for Truncated Realized Variations
- Creators
- B. Cooper BonieceJosé E Figueroa-LópezYuchen Han
- Publication Details
- arXiv.org
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Mathematics
- Other Identifier
- 991021861873604721