PUBLICATIONS
1."Bootstrap inference for pre-averaged realized volatility based on non-overlapping returns," with Sílvia Gonçalves and Nour Meddahi, 2014, Journal of Financial Econometrics, 12(4), 679-707.
2."Validity of Edgeworth expansions for realized volatility estimators," with Bezirgen Veliyev, 2016, Econometrics Journal, 19(1), 1-32. This paper receives the Denis Sargan Econometrics Prize for 2016, the Royal Economic Society.
3."Bootstrapping pre-averaged realized volatility under market microstructure noise," with Sílvia Gonçalves and Nour Meddahi, 2017, Econometric Theory, 33 (4), 791-838.
4."Bootstrapping integrated covariance matrix estimators in noisy jump-diffusion models with non-synchronous trading," 2017, Journal of Econometrics, 197(1), 130-152.
5."A local stable bootstrap for power variations of pure-jump semimartingales and activity index estimation," with Rasmus T. Varneskov, 2017, Journal of Econometrics, 198(1), 10-28.
6."Is the diurnal pattern sufficient to explain the intraday variation in volatility? A nonparametric assessment," with Kim Christensen, and Mark Podolskij, 2018, Journal of Econometrics, 205(2), 336-362.
7."Bootstrapping high-frequency jump tests," with Prosper Dovonon, Sílvia Gonçalves, and Nour Meddahi, 2019, Journal of American Statistical Association, 114, 793-803. Online Appendix.
8."A local Gaussian bootstrap method for realized volatility and realized beta," 2019, Econometric Theory, 35, 360-416.
9."The local fractional bootstrap," with Mikkel Bennedsen, Asger Lunde, and Mikko Pakkanen, 2019, Scandinavian Journal of Statistics, 46, 329-359. PDF.
10."Inference for local distributions at high sampling frequencies: a bootstrap approach," with Rasmus T. Varneskov, 2020, Journal of Econometrics, 215(1), 1-34. (Lead Article).
11."Estimating the variance of a combined forecast: bootstrap-based approach," with Kajal Lahiri, 2023, Journal of Econometrics, 232(2), 445-468.
12."A wild bootstrap for dependent data," 2023, Econometric Theory, 39(2), 264-289 .
13." Are Some Forecasters Really Better Than Others? A Further Note," with Kajal Lahiri, 2023, Journal of Money, Credit and Banking, 55, 577-593.
14."Bootstrapping two-stage quasi-maximum likelihood estimators of time series model", with Silvia Goncalves, Andrew Patton and Kevin Sheppard, PDF. MATLAB Toolbox, 2023, Journal of Business and Economics Statistics, 41(3), 683-694.
15."Bootstrapping Laplace Transforms of Volatility," with Rasmus T. Varneskov, and Zhi Liu., 2023, Quantitative Economics, 14(3), 1059-1103.
16."Heterogeneity in Carbon Intensity Patterns: A Subsampling Approach," with Johnson Kakeu and Li Lu, Energy Economics, Forthcoming.
17."Serial Dependence Robust Bootstrap Test for Cross-Sectional Correlation in Large Panel Models," with Chihwa Kao and Min Seong Kim, accepted at The Econometrics Journal.
18."A modified wild bootstrap procedure for Laplace transform of volatility," with Zhi Liu and Rasmus T. Varneskov, 2024, accepted at Economics Letters.
19."Forecasting economic time series in presence of weak factors: multiple supervised learning-based approach," with Zhendong Li, 2025, accepted at International Journal of Forecasting.
20."Wild Bootstrap Inference with Multiway Clustering and Serially Correlated Time Effects," with Jiahao Lin, 2025, accepted at Journal of Business and Economics Statistics.
21."Misspecification-robust bootstrap t-test for irrelevant factor in linear stochastic discount factor models," with Antoine Djogbenou, 2025, accepted at Journal of Econometrics.
WORKING PAPERS
1."A nonparametric test for diurnal variation in spot correlation processes," with Kim Christensen and Zhi Liu, 2024, Quantitative Economics, Revise and Resubmit.
Abstract: The association between log-price increments of exchange-traded equities, as measured by their spot correlation estimated from high-frequency data, exhibits a pronounced upward-sloping and almost piecewise linear relationship at the intraday horizon. There is notably lower---on average less positive---correlation in the morning than in the afternoon. We develop a nonparametric testing procedure to detect such deterministic variation in a correlation process. The test statistic has a known distribution under the null hypothesis, whereas it diverges under the alternative. It is robust against stochastic correlation. We run a Monte Carlo simulation to discover the finite sample properties of the test statistic, which are close to the large sample predictions, even for small sample sizes and realistic levels of diurnal variation. In an application, we implement the test on a monthly basis for a high-frequency dataset covering the stock market over an extended period. The test leads to rejection of the null most of the time. This suggests diurnal variation in the correlation process is a nontrival effect in practice.
2."Can mutual fund "stars" really pick stocks? New evidence from a wild bootstrap analysis," with Jiahao Lin, 2025
Abstract: This paper introduces a novel approach called wild bootstrapping for analyzing mutual fund performance. Our proposed method preserves various characteristics of mutual fund databases, including entry/exit points for each fund (i.e., missing data) and cross-sectional information. We show that our proposed bootstrap tests have a near-optimal size and exhibit greater power compared to widely used standard bootstrap methods for evaluating mutual fund performance. Additionally, we present a novel approach to picking mutual funds that do not underperform others. Our empirical results indicate that a measurable fraction of funds outperform the market. Furthermore, we extend our methods to assess mutual fund market timing abilities.
3."Two-Stage Model Averaging for Impulse Responses: Local Projection- and VAR-based Approaches" with Seojin Jung, 2025.
Abstract: This paper proposes a model averaging-based estimators of impulse response functions, aiming to effectively combine estimators from a wide range of possible approaches for the effects of the same economic shock. Our proposal allows estimating the weighted combination not only from different models but also from different estimation methods, i.e., using Local Projection (LP)-based estimators and/or vector autoregression (VAR)-based estimators of impulse response functions. Our proposal also allows computing the averaged impulse response functions estimators by using different statistical paradigms (i.e., Bayesian or frequentist). Our simulation study and empirical applications illustrate a typical bias-variance trade-off: estimators of LP-based approaches exhibit lower bias, whereas estimators of VAR-based approaches exhibit lower variance. Additionally, they demonstrate how the weights assigned to different approaches leverage their benefits and balance the properties of LPs and VARs.
4."Jackknife standard errors for two-way clustering with serially correlated time effects," with Jiahao Lin, 2025.
Abstract: Chiang, Hansen, and Sasaki (2024) and Chen and Vogelsang (2024) developed cluster-robust variance estimators (CRVEs) for handling arbitrary serial dependence in linear regressions with two-way clustered panel data. However, conventional CRVEs often perform poorly in finite samples. We propose improved jackknife CRVEs to enhance inference accuracy. Through extensive simulations, we show that the novel jackknife CRVEs deliver remarkably precise inferences. This strong performance holds even in the presence of two-way fixed effects. Notably, one of our new approaches significantly mitigates issues of undefined standard errors when CRVEs are not positive definite, ensuring robust and consistent inference across scenarios.
5."Model Averaging in Semiparametric Estimation of Quantile Treatment Effects," with Sergio Firpo, Antonio Galvao, and Li Lu, 2025.
Abstract: This paper proposes model-averaging methods to estimate quantile treatment effects (QTE and QTT) under treatment selection based on observables. To address propensity score misspecification, we develop two estimators: one averaging QTE/QTT across models, and another that averages propensity scores before estimation. Unconfoundedness is required for at least one covariate set or their union. We introduce a data-driven covariate selection criterion and derive asymptotic properties for inference. A novel ``unconfoundedness signature plot'' is introduced and helps to assess covariate relevance. Simulations show strong finite-sample performance, and we illustrate the approach by estimating the effect of inherited control on firm performance.
6." Projection-Based Wild Bootstrap under General Two-way Cluster Dependence with Serial Dependence," with Jiahao Lin, 2025.
Abstract: This paper introduces a projection-based wild bootstrap (PWB) method for inference in linear regression models with two-way clustered data. We examine all possible scenarios for the asymptotic distribution of the estimator—Gaussian and non-Gaussian—classifying them into five distinct cases. In one scenario, no procedure can achieve uniform consistency under a fully unspecified DGP; to the best of our knowledge, our method is the first to cover the remaining four. We identify and apply two diagnostic factors to distinguish between these scenarios. In addition, our procedure accommodates arbitrary serial dependence. Simulation results demonstrate the accuracy and flexibility of the proposed method, making it a robust tool for empirical work involving complex clustering structures.