Publication Type
Journal Article
Version
publishedVersion
Publication Date
7-2016
Abstract
We develop inference theory for models involving possibly nonlinear transforms of the elements of the spot covariance matrix of a multivariate continuous-time process observed at high frequency. The framework can be used to study the relationship among the elements of the latent spot covariance matrix and processes defined on the basis of it such as systematic and idiosyncratic variances, factor betas and correlations on a fixed interval of time. The estimation is based on matching model-implied moment conditions under the occupation measure induced by the spot covariance process. We prove consistency and asymptotic mixed normality of our estimator of the (random) coefficients in the volatility model and further develop model specification tests. We apply our inference methods to study variance and correlation risks in nine sector portfolios comprising the S&P 500 index. We document sector-specific variance risks in addition to that of the market and time-varying heterogeneous correlation risk among the market-neutral components of the sector portfolio returns.
Keywords
High-frequency data; Occupation measure; Semimartingale; Specification test; Stochastic volatility
Discipline
Economic Theory
Research Areas
Macroeconomics; Economic Theory
Publication
Journal of Econometrics
Volume
193
Issue
1
First Page
17
Last Page
34
ISSN
0304-4076
Identifier
10.1016/j.jeconom.2016.01.004
Publisher
Elsevier: 24 months
Citation
LI, Jia; TODOROV, Viktor; and TAUCHEN, George..
Inference theory on volatility functional dependencies. (2016). Journal of Econometrics. 193, (1), 17-34.
Available at: https://ink.library.smu.edu.sg/soe_research/2571
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.