A test for the rank of the volatility process: the random perturbation approach

In this paper we present a test for the maximal rank of the matrix-valued volatility process in the continuous Ito semimartingale framework. Our idea is based upon a random perturbation of the original high frequency observations of an Ito semimartingale, which opens the way for rank testing. We develop the complete limit theory for the test statistic and apply it to various null and alternative hypotheses. Finally, we demonstrate a homoscedasticity test for the rank process.Comment: 30 page

Estimation of Volatility Functionals in the Simultaneous Presence of Microstructure Noise and Jumps

We propose a new concept of modulated bipower variation for diffusion models with microstructure noise. We show that this method provides simple estimates for such important quantities as integrated volatility or integrated quarticity. Under mild conditions the consistency of modulated bipower variation is proven. Under further assumptions we prove stable convergence of our estimates with the optimal rate n^(-1/4). Moreover, we construct estimates which are robust to finite activity jumps. --Bipower Variation,Central Limit Theorem,Finite Activity Jumps,High-Frequency Data,Integrated Volatility,Microstructure Noise

Pre-averaging based estimation of quadratic variation in the presence of noise and jumps : theory, implementation, and empirical evidence

This paper provides theory as well as empirical results for pre-averaging estimators of the daily quadratic variation of asset prices. We derive jump robust inference for pre-averaging estimators, corresponding feasible central limit theorems and an explicit test on serial dependence in microstructure noise. Using transaction data of different stocks traded at the NYSE, we analyze the estimators’ sensitivity to the choice of the pre-averaging bandwidth and suggest an optimal interval length. Moreover, we investigate the dependence of pre-averaging based inference on the sampling scheme, the sampling frequency, microstructure noise properties as well as the occurrence of jumps. As a result of a detailed empirical study we provide guidance for optimal implementation of pre-averaging estimators and discuss potential pitfalls in practice. Quadratic Variation , MarketMicrostructure Noise , Pre-averaging , Sampling Schemes , Jump

Pre-Averaging Based Estimation of Quadratic Variation in the Presence of Noise and Jumps: Theory, Implementation, and Empirical Evidence

This paper provides theory as well as empirical results for pre-averaging estimators of the daily quadratic variation of asset prices. We derive jump robust inference for pre-averaging estimators, corresponding feasible central limit theorems and an explicit test on serial dependence in microstructure noise. Using transaction data of different stocks traded at the NYSE, we analyze the estimators’ sensitivity to the choice of the pre-averaging bandwidth and suggest an optimal interval length. Moreover, we investigate the dependence of pre-averaging based inference on the sampling scheme, the sampling frequency, microstructure noise properties as well as the occurrence of jumps. As a result of a detailed empirical study we provide guidance for optimal implementation of pre-averaging estimators and discuss potential pitfalls in practice.Quadratic Variation, Market Microstructure Noise, Pre-averaging, Sampling Schemes, Jumps

Testing the parametric form of the volatility in continuous time diffusion models: an empirical process approach

In this paper we present two new tests for the parametric form of the variance function in difusion processes dXt = b(t;Xt)+ó(t;Xt)dWt: Our approach is based on two stochastic processes of the integrated volatility. We prove weak convergence of these processes to centered processes whose conditional distributions given the process (Xt)t2[0;1] are Gaussian. In the special case of testing for a constant volatility the limiting process is the standard Brownian bridge in both cases. As a consequence an asymptotic distribution free test (for the problem of testing for homoscedasticity) and bootstrap tests (for the problem of testing for a general parametric form) can easily be implemented. It is demonstrated that the new tests are more powerful with respect to Pitman alternatives than the currently available procedures for this problem. The asymptotic advantages of the new approach are also observed for realistic sample sizes in a simulation study, where the finite sample properties of a Kolmogorov-Smirnov test are investigated. --Specification tests,integrated volatility,bootstrap,heteroscedasticity,stable convergence,Brownian Bridge