@article{kalnina_2022, title={Inference for Nonparametric High-Frequency Estimators with an Application to Time Variation in Betas}, volume={41}, ISSN={["1537-2707"]}, url={http://dx.doi.org/10.1080/07350015.2022.2040520}, DOI={10.1080/07350015.2022.2040520}, abstractNote={Abstract We consider the problem of conducting inference on nonparametric high-frequency estimators without knowing their asymptotic variances. We prove that a multivariate subsampling method achieves this goal under general conditions that were not previously available in the literature. By construction, the subsampling method delivers estimates of the variance-covariance matrices that are always positive semidefinite. Our simulation study indicates that the subsampling method is more robust than the plug-in method based on the asymptotic expression for the variance. We use our subsampling method to study the dynamics of financial betas of six stocks on the NYSE. We document significant variation in betas, and find that tick data captures more variation in betas than the data sampled at moderate frequencies such as every 5 or 20 min. To capture this variation we estimate a simple dynamic model for betas. The variance estimation is also important for the correction of the errors-in-variables bias in such models. We find that the bias corrections are substantial, and that betas are more persistent than the naive estimators would lead one to believe.}, number={2}, journal={JOURNAL OF BUSINESS & ECONOMIC STATISTICS}, publisher={Informa UK Limited}, author={Kalnina, Ilze}, year={2022}, month={Mar} }
 @article{aït-sahalia_kalnina_xiu_2020, title={High-frequency factor models and regressions}, volume={216}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85079147511&partnerID=MN8TOARS}, DOI={10.1016/j.jeconom.2020.01.007}, number={1}, journal={Journal of Econometrics}, author={Aït-Sahalia, Y. and Kalnina, I. and Xiu, D.}, year={2020}, pages={86–105} }
 @article{ait-sahalia_kalnina_xiu_2019, title={High-Frequency Factor Models and Regressions}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85113923984&partnerID=MN8TOARS}, DOI={10.2139/ssrn.3319890}, abstractNote={We consider a nonparametric time series regression model. Our framework allows precise estimation of betas without the usual assumption of betas being piecewise constant. This property makes our framework particularly suitable to study individual stocks. We provide an inference framework for all components of the model, including idiosyncratic volatility and idiosyncratic jumps. Our empirical analysis investigates the largest data set in the high-frequency literature. First, we use all traded stocks from NYSE, AMEX, and NASDAQ stock markets for 1996-2017 to construct the five Fama-French factors and the momentum factor at the 5-minute frequency. Second, we document the key empirical properties across all the stocks and the new factors, and apply the nonparametric time series regression model with the new high-frequency Fama-French factors. We find that this factor model is effective in explaining the systematic component of the risk of individual stocks. In addition, we provide evidence that idiosyncratic jumps are related to idiosyncratic events such as earnings disappointments.}, journal={SSRN}, author={Ait-Sahalia, Y. and Kalnina, I. and Xiu, D.}, year={2019} }
 @article{kalnina_xiu_2017, title={Nonparametric Estimation of the Leverage Effect: A Trade-Off Between Robustness and Efficiency}, volume={112}, ISSN={0162-1459 1537-274X}, url={http://dx.doi.org/10.1080/01621459.2016.1141687}, DOI={10.1080/01621459.2016.1141687}, abstractNote={ABSTRACT We consider two new approaches to nonparametric estimation of the leverage effect. The first approach uses stock prices alone. The second approach uses the data on stock prices as well as a certain volatility instrument, such as the Chicago Board Options Exchange (CBOE) volatility index (VIX) or the Black–Scholes implied volatility. The theoretical justification for the instrument-based estimator relies on a certain invariance property, which can be exploited when high-frequency data are available. The price-only estimator is more robust since it is valid under weaker assumptions. However, in the presence of a valid volatility instrument, the price-only estimator is inefficient as the instrument-based estimator has a faster rate of convergence.We consider an empirical application, in which we study the relationship between the leverage effect and the debt-to-equity ratio, credit risk, and illiquidity. Supplementary materials for this article are available online.}, number={517}, journal={Journal of the American Statistical Association}, publisher={Informa UK Limited}, author={Kalnina, Ilze and Xiu, Dacheng}, year={2017}, month={Jan}, pages={384–396} }
 @article{kalnina_2011, title={Subsampling high frequency data}, volume={161}, ISSN={0304-4076}, url={http://dx.doi.org/10.1016/j.jeconom.2010.12.011}, DOI={10.1016/j.jeconom.2010.12.011}, abstractNote={The main contribution of this paper is to propose a novel way of conducting inference for an important general class of estimators that includes many estimators of integrated volatility. A subsampling scheme is introduced that consistently estimates the asymptotic variance for an estimator, thereby facilitating inference and the construction of valid confidence intervals. The new method does not rely on the exact form of the asymptotic variance, which is useful when the latter is of complicated form. The method is applied to the volatility estimator of Aït-Sahalia et al. (2011) in the presence of autocorrelated and heteroscedastic market microstructure noise.}, number={2}, journal={Journal of Econometrics}, publisher={Elsevier BV}, author={Kalnina, Ilze}, year={2011}, month={Apr}, pages={262–283} }
 @article{kalnina_linton_2008, title={Estimating quadratic variation consistently in the presence of endogenous and diurnal measurement error}, volume={147}, ISSN={0304-4076}, url={http://dx.doi.org/10.1016/j.jeconom.2008.09.016}, DOI={10.1016/j.jeconom.2008.09.016}, abstractNote={We propose an econometric model that captures the effects of market microstructure on a latent price process. In particular, we allow for correlation between the measurement error and the return process and we allow the measurement error process to have a diurnal heteroskedasticity. We propose a modification of the TSRV estimator of quadratic variation. We show that this estimator is consistent, with a rate of convergence that depends on the size of the measurement error, but is no worse than n−1/6. We investigate in simulation experiments the finite sample performance of various proposed implementations.}, number={1}, journal={Journal of Econometrics}, publisher={Elsevier BV}, author={Kalnina, Ilze and Linton, Oliver}, year={2008}, month={Nov}, pages={47–59} }