Yuta Koike

Associate Professor

Graduate School of Mathematical Sciences, University of Tokyo

Contact Information

Graduate School of Mathematical Sciences, University of Tokyo.
3-8-1 Komaba, Meguro-ku, Tokyo 153-8914, Japan
E-mail : kyuta (at) ms.u-tokyo.ac.jp

Fields of Interest

Asymptotic statistics, Financial econometrics, High-dimensional statistics, High frequency data, Mathematical statistics, Statistics for stochastic processes.

Education

• Apr. 2006-Mar. 2010: Department of Mathematics, Tokyo Institute of Technology, Japan [Bachelor (Science), 2010]
• Apr. 2010-Mar. 2012: Master course of Graduate School of Mathematical Sciences, University of Tokyo, Japan [Master (Mathematical Science), 2012]
  Master Thesis: ​ An estimator for the cumulative co-volatility of nonsynchronously observed semimartingales with jumps
• Apr. 2012-Jul. 2014: Doctoral course of Graduate School of Mathematical Sciences, University of Tokyo, Japan (withdrawal for getting a job at The Institute of Statistical Mathematics)
• Apr. 2015: Ph.D. (Mathematical Science; Doctorate by way of Dissertation), Graduate School of Mathematical Sciences, University of Tokyo, Japan
  Dissertation: ​ Covariance estimation from ultra-high-frequency data

Work experiences

• Aug. 2014-Jul. 2015: Project Researcher at Risk Analysis Research Center, The Institute of Statistical Mathematics
• Aug. 2015-Mar. 2016: Project Assistant Professor at Risk Analysis Research Center, The Institute of Statistical Mathematics
• Apr. 2016-Oct. 2017: Assistant Professor at Department of Business Administration, Graduate School of Social Sciences, Tokyo Metropolitan University
• Nov. 2017-present: Associate Professor at Graduate School of Mathematical Sciences, University of Tokyo

Published papers

1. High-dimensional central limit theorems by Stein's method in the degenerate case (with X. Fang, S.-H. Liu, Y.-K. Zhao). To appear in Annals of Applied Probability. arXiv:2305.17365
2. High-dimensional bootstrap and asymptotic expansion. Probability Theory and Related Fields, 195 (2026), pp 1051-1125. arXiv:2404.05006
   (Note: Remark 2.4(c) of this paper claims that if a probability distribution has a Stein kernel, its support is convex support. However, this is incorrect; see this note.)
3. Financial Data Analysis by SDE Modeling with YUIMA (in Japanese). Japanese Journal of Applied Statistics, 54 (2025), pp 195–211.
4. Adaptive deep learning for nonparametric time series regression (with D. Kurisu, R. Fukami). Bernoulli, 31 (2025), no. 1, 240-270. arXiv:2207.02546
5. Sharp high-dimensional central limit theorems for log-concave distributions (with X. Fang). Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, 60 (2024), no. 3, 2129-2156. arXiv:2207.14536
6. Large-dimensional central limit theorem with fourth-moment error bounds on convex sets and balls (with X. Fang). Annals of Applied Probability, 34 (2024), 2065-2106. arXiv:2009.00339
7. Drift estimation for a multi-dimensional diffusion process using deep neural networks (with A. Oga). Stochastic Processes and their Applications, 170 (2024), 104240. arXiv:2112.13332
8. From p-Wasserstein bounds to moderate deviations (with X. Fang). Electronic Journal of Probability, 28 (2023), pp 1-52. arXiv:2205.13307
9. Nearly optimal central limit theorem and bootstrap approximations in high dimensions (with V. Chernozhukov, D. Chetverikov). Annals of Applied Probability, 33 (2023), no. 3, pp 2374-2425. arXiv:2012.09513
10. High-dimensional data bootstrap (with V. Chernozhukov, D. Chetverikov, K. Kato). Annual Review of Statistics and Its Applications, 10 (2023), pp 427-449. arXiv:2205.09691
11. High-dimensional central limit theorems for homogeneous sums. Journal of Theoretical Probability, 36 (2023), no. 1, 1-45. arXiv:1902.03809
12. Improved central limit theorem and bootstrap approximations in high dimensions (with V. Chernozhukov, D. Chetverikov, K. Kato). Annals of Statistics, 50 (2022), no. 5, pp 2562-2586. arXiv:1912.10529
13. New error bounds in multivariate normal approximations via exchangeable pairs with applications to Wishart matrices and fourth moment theorems (with X. Fang). Annals of Applied Probability, 32 (2022), no. 1, pp 602-631. arXiv:2004.02101
14. High-dimensional central limit theorems by Stein’s method (with X. Fang). Annals of Applied Probability, 31 (2021), no. 4, pp 1660-1686. arXiv:2001.10917
15. Inference for time-varying lead-lag relationships from ultra high frequency data. Japanese Journal of Statistics and Data Science, 4 (2021), no. 1, pp 643–696. SSRN
16. Notes on the dimension dependence in high-dimensional central limit theorems for hyperrectangles. Japanese Journal of Statistics and Data Science, 4 (2021), no. 1, pp 257–297. arXiv:1911.00160
    (Note: The proof of Lemma 2.2 of this paper is incorrect. See the arXiv version for the corrected proof, where the constant is doubled.)
17. De-biased graphical Lasso for high-frequency data. Entropy, 22 (2020), no. 4, 456. arXiv:1905.01494
18. No arbitrage and lead-lag relationships (with T. Hayashi). Statistics and Probability Letters, 154 (2019), 108530. arXiv:1712.09854
19. Asymptotic properties of the realized skewness and related statistics (with Z. Liu). Annals of the Institute of Statistical Mathematics, 71 (2019), no. 4, pp 703-741.
20. Covariance estimation and quasi-likelihood analysis (with N. Yoshida). In: J. Chevallier, S. Goutte, D. Guerreiro, S. Saglio and B. Sanhaji, eds., Financial mathematics, volatility and covariance modelling, vol. 2. (2019), chap. 12. Routledge, pp 308-335.
21. Oracle inequalities for sign constrained generalized linear models (with Y. Tanoue). Econometrics and Statistics, 11 (2019), pp 145-157. arXiv:1711.03342
22. Mixed-normal limit theorems for multiple Skorohod integrals in high-dimensions, with application to realized covariance. Electronic Journal of Statistics, 13 (2019), no.1, pp 1443-1522. arXiv:1806.05077
23. Gaussian approximation of maxima of Wiener functionals and its application to high-frequency data. Annals of Statistics, 47 (2019), no.3, pp 1663-1687. arXiv:1709.00353
24. Wavelet-based methods for high-frequency lead-lag analysis (with T. Hayashi). SIAM Journal on Financial Mathematics, 9 (2018), no.4, pp 1208-1248.
25. On the asymptotic structure of Brownian motions with a small lead-lag effect. Journal of the Japan Statistical Society, 47 (2017), no.2, pp 1-31.
26. Time endogeneity and an optimal weight function in pre-averaging covariance estimation. Statistical Inference for Stochastic Processes, 20 (2017), no.1, pp 15–56.
27. Realized volatility and related topics (in Japanese). Journal of Business and Institutions, 15 (2017), pp 15–42.
28. Quadratic covariation estimation of an irregularly observed semimartingale with jumps and noise. Bernoulli, 22 (2016), no.3, pp 1894–1936.
29. Estimation of integrated covariances in the simultaneous presence of nonsynchronicity, microstructure noise and jumps. Econometric theory, 32 (2016), no.3, pp 533–611.
30. Limit theorems for the pre-averaged Hayashi-Yoshida estimator with random sampling. Stochastic Processes and their Applications, 124 (2014), no.8, pp 2699–2753.
31. An estimator for the cumulative co-volatility of asynchronously observed semimartingales with jumps. Scandinavian Journal of Statistics, 41 (2014), no.2, pp 460-481.
32. The YUIMA project: A computational framework for simulation and inference of stochastic differential equations (with A. Brouste, M. Fukasawa, H. Hino, S. Iacus, H. ​Masuda, R. Nomura, T. Ogihara, Y. Shimizu, M. Uchida, N. Yoshida). Journal of Statistical Software, 57 (2014), no.4, pp 1-51.

Working papers

1. Multi-scale analysis of lead-lag relationships in high-frequency financial markets (with T. Hayashi). arXiv:1708.03992
2. Spectral norm bounds for high-dimensional realized covariance matrices and application to weak factor models. arXiv:2310.06073
3. Gaussian approximation for high-dimensional U-statistics with size-dependent kernels (with S. Imai). arXiv:2504.10866
4. On lead-lag estimation of non-synchronously observed point processes (with T. Shiotani, T. Hayashi). arXiv:2601.01871
5. A note on connections between the Föllmer process and the denoising diffusion probabilistic model. arXiv:2605.18040
6. Wasserstein bounds for denoising diffusion probabilistic models via the Föllmer process. arXiv:2605.18069

Other unpublished manuscripts

1. Central limit theorems for pre-averaging covariance estimators under endogenous sampling times. arXiv:1305.1229
   (This is a preliminary version of "Time endogeneity and an optimal weight function in pre-averaging covariance estimation")
2. Higher order realized power variations of semi-martingales with applications (with Z. Liu). SSRN
   (This is a preliminary version of "Asymptotic properties of the realized skewness and related statistics")

Presentations at seminars/conferences

Teaching

• Introduction to Statistical Data Analysis II (Spring semester at U. of Tokyo)
• Probability and Statistics XC (Spring semester at U. of Tokyo)

• Introduction to Statistical Data Analysis II (Spring semester at U. of Tokyo)
• Probability and Statistics XC (Spring semester at U. of Tokyo)
• Introduction to Statistical Data Analysis I (Autumn semester at U. of Tokyo)
• Econometrics II (Autumn semester at Seijo U.)

• Statistical Analysis (February - April at UTokyo Extension)
• Introduction to Statistical Data Analysis II (Spring semester at U. of Tokyo, twice a week)
• Probability and Statistics XC (Spring semester at U. of Tokyo)
• Economic Mathematics I (Spring semester at Seijo U.)
• Econometrics I (Spring semester at Seijo U.)
• Statistical Analysis (October - December at UTokyo Extension)
• Introduction to Statistical Data Analysis I (Autumn semester at U. of Tokyo)
• Economic Mathematics II (Autumn semester at Seijo U.)
• Econometrics II (Autumn semester at Seijo U.)

• Statistical Analysis (February - April at UTokyo Extension)
• Introduction to Statistical Data Analysis II (Spring semester at U. of Tokyo, twice a week)
• Probability and Statistics XC (Spring semester at U. of Tokyo)
• Economic Mathematics I (Spring semester at Seijo U.)
• Econometrics I (Spring semester at Seijo U.)
• Introduction to Statistical Data Analysis I (Autumn semester at U. of Tokyo)
• Economic Mathematics II (Autumn semester at Seijo U.)
• Econometrics II (Autumn semester at Seijo U.)

• Statistical Analysis (February - April at UTokyo Extension)
• Introduction to Statistical Data Analysis II (Spring semester at U. of Tokyo, twice a week)
• Probability and Statistics XC (Spring semester at U. of Tokyo)
• Economic Mathematics I (Spring semester at Seijo U.)
• Econometrics I (Spring semester at Seijo U.)
• High-dimensional Statistics for Stochastic Processes (Sep. 20-24 at Osaka U.) Lecture note (in Japanese, updated on May 1, 2025)
• Statistical Analysis (November - December at UTokyo Extension)
• Introduction to Statistical Data Analysis I (Autumn semester at U. of Tokyo, twice a week)
• Economic Mathematics II (Autumn semester at Seijo U.)
• Econometrics II (Autumn semester at Seijo U.)

• Introduction to Statistical Data Analysis II (Spring semester at U. of Tokyo, twice a week)
• Probability and Statistics XC (Spring semester at U. of Tokyo)
• Statistical Analysis (May - July at UTokyo Extension)
• Statistical Analysis (July - September at UTokyo Extension)
• Introduction to Statistical Data Analysis I (Autumn semester at U. of Tokyo, twice a week)

• Introduction to Statistical Data Analysis II (Spring semester at U. of Tokyo, twice a week)
• Statistics (June - July at UTokyo Extension)
• Statistics (August - October at UTokyo Extension)
• Statistics (October - December at UTokyo Extension)
• Introduction to Normal Approximation by Stein's Method (October - December at Tokyo Metropolitan U.)
• Introduction to Statistical Data Analysis I (Autumn semester at U. of Tokyo, twice a week)

• Introduction to Statistical Data Analysis II (Spring semester at U. of Tokyo, twice a week)
• Probability Theory I (Spring semester at U. of Tokyo) Lecture note (in Japanese, updated on January 16, 2025)
• Introduction to Programming (Spring 1st half semester at Tokyo Metropolitan U.)
• Statistics (August - October at UTokyo Extension)
• Numerical Methods in Finance (Autumn 1st half semester at Tokyo Metropolitan U.)
• Statistics (October - December at UTokyo Extension)
• Introduction to Statistical Data Analysis I (Autumn semester at U. of Tokyo, twice a week)

• Introduction to Statistical Data Analysis II (Spring semester at U. of Tokyo, twice a week)
• Financial Data Science Exercises (Spring semester at Tokyo Metropolitan U.)
• Introduction to Statistical Data Analysis I (Autumn semester at U. of Tokyo, twice a week)
• Financial Time Series Analysis Exercises (Autumn semester at Tokyo Metropolitan U.)
• Introduction to Statistics (Autumn semester at Seijo U., twice a week)

• Financial Data Science Exercises (Spring semester at Tokyo Metropolitan U.)
• Advanced Stochastic Analysis Exercises (Spring semester at Tokyo Metropolitan U.)
• Introduction to Economic Mathematics I (Spring semester at Aoyama Gakuin U.)
• Introduction to Statistical Data Analysis II (Spring semester at U. of Tokyo)
• Financial Time Series Analysis Exercises (Autumn semester at Tokyo Metropolitan U.)
• Stochastic Analysis Exercises (Autumn semester at Tokyo Metropolitan U.)
• Introduction to Statistical Data Analysis I (Autumn semester at U. of Tokyo, twice a week)
• Introduction to Economic Mathematics II (Autumn semester at Aoyama Gakuin U.)

• Financial Data Science Exercises (Spring semester at Tokyo Metropolitan U.)
• Introduction to Economic Mathematics I (Spring semester at Aoyama Gakuin U.)
• Introduction to Statistics I (Spring semester at Aoyama Gakuin U.)
• Financial Time Series Analysis Exercises (Autumn semester at Tokyo Metropolitan U.)
• Stochastic Analysis Exercises (Autumn semester at Tokyo Metropolitan U.)
• Introduction to Economic Mathematics II (Autumn semester at Aoyama Gakuin U.)
• Introduction to Statistics II (Autumn semester at Aoyama Gakuin U.)

Editorial board

• Asia-Pacific Financial Markets (September 2025-)

• Asia-Pacific Financial Markets (August 2019-August 2025)
• Japanese Journal of Statistics and Data Science (April 2023-)
• Bernoulli (January 2025-)

Others