ANOVA for diffusions and Itô processes
arXiv:math/0611274 · doi:10.1214/009053606000000452
Abstract
Itô processes are the most common form of continuous semimartingales, and include diffusion processes. This paper is concerned with the nonparametric regression relationship between two such Itô processes. We are interested in the quadratic variation (integrated volatility) of the residual in this regression, over a unit of time (such as a day). A main conceptual finding is that this quadratic variation can be estimated almost as if the residual process were observed, the difference being that there is also a bias which is of the same asymptotic order as the mixed normal error term. The proposed methodology, ``ANOVA for diffusions and Itô processes,'' can be used to measure the statistical quality of a parametric model and, nonparametrically, the appropriateness of a one-regressor model in general. On the other hand, it also helps quantify and characterize the trading (hedging) error in the case of financial applications.
Published at http://dx.doi.org/10.1214/009053606000000452 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
Cited by in corpus (11)
- A Fourier transform method for nonparametric estimation of multivariate volatility
- Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data
- Are volatility estimators robust with respect to modeling assumptions?
- Weak convergence of error processes in discretizations of stochastic integrals and Besov spaces
- Parametric and nonparametric models and methods in financial econometrics
- Second-order asymptotic expansion for a non-synchronous covariation estimator
- Estimation of integrated quadratic covariation with endogenous sampling times
- Efficient estimation for diffusions sampled at high frequency over a fixed time interval
- Model-Free Approaches to Discern Non-Stationary Microstructure Noise and Time-Varying Liquidity in High-Frequency Data
- Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data
- Cross-sectional Dependence in Idiosyncratic Volatility