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 bootstrap and asymptotic expansion. To appear in Probability Theory and Related Fields. 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.)
2. Financial Data Analysis by SDE Modeling with YUIMA (in Japanese). Japanese Journal of Applied Statistics, 54 (2025), pp 195–211.
3. Adaptive deep learning for nonparametric time series regression (with D. Kurisu, R. Fukami). Bernoulli, 31 (2025), no. 1, 240-270. arXiv:2207.02546
4. 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
5. 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
6. 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
7. From p-Wasserstein bounds to moderate deviations (with X. Fang). Electronic Journal of Probability, 28 (2023), pp 1-52. arXiv:2205.13307
8. 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
9. 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
10. High-dimensional central limit theorems for homogeneous sums. Journal of Theoretical Probability, 36 (2023), no. 1, 1-45. arXiv:1902.03809
11. 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
12. 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
13. 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
14. 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
15. 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.)
16. De-biased graphical Lasso for high-frequency data. Entropy, 22 (2020), no. 4, 456. arXiv:1905.01494
17. No arbitrage and lead-lag relationships (with T. Hayashi). Statistics and Probability Letters, 154 (2019), 108530. arXiv:1712.09854
18. 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.
19. 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.
20. Oracle inequalities for sign constrained generalized linear models (with Y. Tanoue). Econometrics and Statistics, 11 (2019), pp 145-157. arXiv:1711.03342
21. 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
22. 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
23. Wavelet-based methods for high-frequency lead-lag analysis (with T. Hayashi). SIAM Journal on Financial Mathematics, 9 (2018), no.4, pp 1208-1248.
24. 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.
25. Time endogeneity and an optimal weight function in pre-averaging covariance estimation. Statistical Inference for Stochastic Processes, 20 (2017), no.1, pp 15–56.
26. Realized volatility and related topics (in Japanese). Journal of Business and Institutions, 15 (2017), pp 15–42.
27. Quadratic covariation estimation of an irregularly observed semimartingale with jumps and noise. Bernoulli, 22 (2016), no.3, pp 1894–1936.
28. Estimation of integrated covariances in the simultaneous presence of nonsynchronicity, microstructure noise and jumps. Econometric theory, 32 (2016), no.3, pp 533–611.
29. Limit theorems for the pre-averaged Hayashi-Yoshida estimator with random sampling. Stochastic Processes and their Applications, 124 (2014), no.8, pp 2699–2753.
30. An estimator for the cumulative co-volatility of asynchronously observed semimartingales with jumps. Scandinavian Journal of Statistics, 41 (2014), no.2, pp 460-481.
31. 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. High-dimensional central limit theorems by Stein's method in the degenerate case (with X. Fang, S.-H. Liu, Y.-K. Zhao). arXiv:2305.17365
3. Spectral norm bounds for high-dimensional realized covariance matrices and application to weak factor models. arXiv:2310.06073
4. Gaussian approximation for high-dimensional U-statistics with size-dependent kernels (with S. Imai). arXiv:2504.10866
5. On lead-lag estimation of non-synchronously observed point processes (with T. Shiotani, T. Hayashi). arXiv:2601.01871
6. A note on connections between the Föllmer process and the denoising diffusion probabilistic model. arXiv:2605.18040
7. 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

• Apr. 15, 2026 : Stochastic Analysis and Statistics 2026, University of Tokyo, Tokyo, Japan.
• Mar. 18, 2026 : The LiU Seminar Series in Statistics and Mathematical Statistics, Linköping University, Linköping, Sweden.
• Feb. 10, 2026 : Statistics seminar, University of Melbourne, Melbourne, Australia.
• Dec. 14, 2025 : 18th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2025), University of London, London, UK (hybrid).
• Oct. 7, 2025 : The 65th ISI World Statistics Congress 2025, World Forum The Hague, The Hague, the Netherlands.
• July 15, 2025 : 44th Conference on Stochastic Processes and their Applications (SPA 2025), Wrocław University of Science and Technology and University of Wrocław, Wrocław, Poland.
• Dec. 14, 2024 : 17th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2024), King’s College London, London, UK (hybrid).
• Dec. 6, 2024 : International Symposium on Theories, Methodologies and Applications for Large Complex Data, Tsukuba International Congress Center, Tsukuba, Japan.
• Aug. 12, 2024 : Bernoulli-ims 11th World Congress in Probability and Statistics, Ruhr University Bochum, Bochum, Germany.
• Aug. 7, 2024 : 2024 Joint Statistical Meetings (JSM 2024), Oregon Convention Center, Portland, USA.
• July 17, 2024 : The 7th International Conference on Econometrics and Statistics (EcoSta 2024), Beijing Normal University, Beijing, China (hybrid).
• July 13, 2024 : The 2nd Joint Conference on Statistics and Data Science in China (JCSDS 2024), Yunnan Haigeng Auditorium, Kunming, China.
• June 19, 2024 : Asia-Pacific Seminar in Probability and Statistics (APSPS), online.
• Feb. 7, 2024 : Stochastic Analysis and Statistics 2024, University of Tokyo, Tokyo, Japan (hybrid).
• Dec. 16, 2023 : 16th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2023), HTW Berlin, University of Applied Sciences, Berlin, Germany (hybrid).
• July 27, 2023 : 43rd Conference on Stochastic Processes and their Applications (SPA 2023), University of Lisbon, Lisbon, Portugal.
• July 8, 2023 : The 12th ICSA International Conference, Chinese University of Hong Kong, Hong Kong, China.
• Mar. 31, 2023 : Workshop on Mathematical Statistics in the Information Age, University of Freiburg, Freiburg, Germany.
• Dec. 17, 2022 : 15th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2022), King's College London, London, UK (hybrid).
• Dec. 10, 2022 : 33rd (EC)^2 Conference, ESSEC Executive Education, Paris, France (poster).
• Oct. 13, 2022 : Risk and Statistics, 3rd Tohoku-ISM-UUlm Joint Workshop, Tohoku University, Miyagi, Japan (hybrid).
• June 5, 2022 : The 5th International Conference on Econometrics and Statistics (EcoSta 2022), Ryukoku University, Kyoto, Japan (hybrid).
• Dec. 18, 2021 : 15th International Conference on Computational and Financial Econometrics (CFE 2021), King's College London, London, UK (hybrid).
• Oct. 21, 2021 : Maths & Stats Colloquium Series (Macquarie University), online.
• July 20, 2021 : Bernoulli-IMS 10th World Congress in Probability and Statistics, online.
• June 25, 2021 : The 4th International Conference on Econometrics and Statistics (EcoSta 2021), online.
• Dec. 1, 2020 : The LiU Seminar Series in Statistics and Mathematical Statistics, online.
• Sep. 25, 2020 : CEQURA Conference 2020 on Advances in Financial and Insurance Risk Management, online.
• Dec. 22, 2019 : The 11th ICSA International Conference, Hangzhou Dragon Hotel, Hangzhou, China.
• Dec. 16, 2019 : 12th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2019), University of London, London, UK.
• Aug. 21, 2019 : The 62nd ISI World Statistics Congress 2019, Kuala Lumpur Convention Centre, Kuala Lumpur, Malaysia.
• July 25, 2019 : The 32nd European Meeting of Statisticians, University of Palermo, Palermo, Italy.
• July 17, 2019 : The 3rd KAFE-JAFEE International Conference on Financial Engineering, Busan International Finance Center, Busan, Korea.
• June 27, 2019 : The Third YUIMA Conference, Università di Padova, Brixen-Bressanone, Italy.
• Mar. 25, 2019 : The Second YUIMA Conference, SAPIENZA University of Rome, Rome, Italy.
• Jan. 30, 2019 : ASC2019 : Asymptotic Statistics and Computations, The University of Tokyo, Tokyo, Japan.
• Jan. 29, 2019 : 4th Yuima Users Workshop, The University of Tokyo, Tokyo, Japan.
• Dec. 15, 2018 : 11th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2018), University of Pisa, Pisa, Italy.
• Oct. 4, 2018 : CEQURA Conference 2018 on Advances in Financial and Insurance Risk Management, Katholische Akademie in Bayern, Munich, Germany.
• July 17, 2018 : 10th World Congress of the Bachelier Finance Society (Bachelier 2018), Trinity College Dublin, Dublin, Ireland.
• June 29, 2018 : The 5th Institute of Mathematical Statistics Asia Pacific Rim Meeting (IMS-APRM 2018), National University of Singapole, Singapole.
• June 19, 2018 : The 2nd International Conference on Econometrics and Statistics (EcoSta 2018), City University of Hong Kong, Hong Kong, China.
• Mar. 27, 2018 : Computational Aspects of Simulation and Inference for Stochastic Processes and the YUIMA Project, University of Milan, Milan, Italy.
• Mar. 3, 2018 : 2018 Kagawa International Symposium "Recent Developments in Statistics and Econometrics", Kagawa University, Kagawa, Japan.
• Feb. 5, 2018 : ASC2018 : Asymptotic Statistics and Computations, The University of Tokyo, Tokyo, Japan.

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 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