A0534
Title: On data-driven tuning for truncated realized variations
Authors: B Cooper Boniece - Drexel University (United States) [presenting]
Jose Figueroa-Lopez - Washington University in Saint Louis (United States)
Yuchen Han - Washington University in Saint Louis (United States)
Abstract: Many methods for estimating volatility in the presence of jumps require the specification of tuning parameters for their use in practice. 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 often relying entirely on heuristics. Data-driven fixed-point procedures are discussed for estimating volatility in the presence of jumps. If time permits, some related work on data-driven thresholding procedures in a high-dimensional setting will be discussed.
