Yoann Potiron

Associate Professor

Faculty of Business and Commerce at Keio University

YoannPotiron Yoann Potiron comes from Chambery, France. He holds a B.S. in Applied Mathematics from Ecole Polytechnique in August 2013. He also obtained a M.S. in Statistics in August 2015 and a Ph.D. in Statistics in March 2016, both from the University of Chicago. He joined the Faculty of Business and Commerce at Keio University in Tokyo as a Tenure-track Assistant Professor in April 2016, before obtaining his tenure in April 2018 and becoming an Associate Professor in April 2020. His research interests are financial econometrics, mathematical statistics, nonparametric statistics and applied probability. He has reviewed for the leading econometrics journal Journal of Econometrics, and the leading statistics journals Journal of the American Statistical Association and the Annals of Statistics. He has publications in the leading econometrics journal Journal of Econometrics. He also has publications in the very good econometrics journal Journal of Business & Economic Statistics, and in the very good statistics journals Bernoulli, the Annals of the Institute of Statistical Mathematics and the Electronic Journal of Statistics. His research is partly supported by Japanese Society for the Promotion of Science Grants-in-Aid for Scientific Research (B) (23H00807, sole investigator).

I am looking for Ph.D or postdoc students, whether it is working remotedely or as an exchange student. I am also looking for Master or B.S. students to work on research assistanship. Please email-me to contact me if you are interested.





General information

Contact Information

Faculty of Business and Commerce, Keio University.
2-15-45 Mita, Minato-ku, Tokyo 108-8345
E-mail: potiron (at) keio (dot) jp
Phone: +81 (0)3 5418 6571
Office : Research building 439B (4th floor)

Research interests

Financial econometrics, mathematical statistics, nonparametric statistics and applied probability.

Employment

Education

Research

Grants

  1. April 2023 - March 2028 JSPS Grants-in-Aid for Scientific Research (B) "Duration models related problems in econometrics" (23H00807, sole investigator, JPY17,290,000)
  2. April 2020 - March 2023 JSPS Grants-in-Aid for Early-Career Scientists "Econometric methods for high frequency data" (20K13470, sole investigator, JPY4,290,000)
  3. April 2017 - March 2020 JSPS Grants-in-Aid for Young Scientists B "Forecasting and model selection in time-varying parameter models" (17K13718, sole investigator, JPY4,290,000)

Published/accepted papers

  1. Non-explicit formula of boundary crossing probabilities by the Girsanov theorem. To appear in the Annals of the Institute of Statistical Mathematics. Download the supplement
  2. Estimation for high-frequency data under parametric market microstructure noise, with Simon Clinet, Annals of the Institute of Statistical Mathematics, 2021, 73(1), 649-669. Download the supplement
  3. Cointegration in high frequency data, with Simon Clinet, Electronic Journal of Statistics, 2021, 15(1), 1263-1327. Download the Python code
  4. Disentangling Sources of High Frequency Market Microstructure Noise, with Simon Clinet, Journal of Business & Economic Statistics, 2021, 39(1), 18-39. Download the supplement Download the Python code
  5. Local Parametric Estimation in High Frequency Data, with Per Aslak Mykland, Journal of Business & Economic Statistics, 2020, 38(3), 679-692. Download the supplement Download the R code
  6. Testing if the market microstructure noise is fully explained by the informational content of some variables from the limit order book, with Simon Clinet. Journal of Econometrics, 2019(1), 209, 289-337. Download the Python code
  7. Efficient asymptotic variance reduction when estimating volatility in high frequency data, with Simon Clinet, Journal of Econometrics, 2018, 206(1), 103-142. Download the Python/R code
  8. Statistical inference for the doubly stochastic self-exciting process, with Simon Clinet, Bernoulli, 2018, 24(4B), 3469-3493. Download the supplement Download the R code
  9. Classifying patents based on their semantic content, with Antonin Bergeaud and Juste Raimbault. PLoS ONE, 2017, 12(4), e0176310. Download the R code
  10. Estimation of integrated quadratic covariation with endogenous sampling times, with Per Aslak Mykland, Journal of Econometrics, 2017, 197(1), 20-41. Download the R code

Papers in revision

  1. Mutually exciting point processes with latency, with Vladimir Volkov. In minor revision for the Journal of the American Statistical Association.

Submitted/working papers

  1. Approximation convergence in the inverse first-passage time problem, with Leonard Vimont. Submitted to the Annales de l’Institut Henri Poincaré.
  2. High-frequency estimation of Ito semimartingale baseline for Hawkes processes, with Olivier Scaillet, Vladimir Volkov and Seunghyeon Yu. In revision to be submitted to the Annals of Statistics.
  3. Nonparametric estimation of hitting-time variance, with Julian Kota Kikuchi and Chang Yuan Li. In revision to be submitted to the Annals of Statistics.
  4. First passage time and inverse problem for continuous local martingales. In revision to be submitted to the Scandinavian Journal of Statistics.
  5. Estimation and Test of Branching Ratio for Hawkes processes, with Olivier Scaillet, Vladimir Volkov and Seunghyeon Yu.
  6. Disentangling seasonality from a self-exciting process with time-varying baseline, with Olivier Scaillet, Vladimir Volkov and Seunghyeon Yu.
  7. What drives latency in high-frequency trading?, with Olivier Scaillet and Vladimir Volkov.
  8. Noncausal Hawkes processes, with Kim Christensen and Aleksei Kolokolov.
  9. Brownian motion conditioned to spend limited time outside a monotone function, with Martin Kolb and Dominic Schickentanz.
  10. Kaplan-Meier estimation for cumulative functionals of distribution function with censored data, with Chang Yuan Li.
  11. Testing the Ito semimartingale assumption with bipower variation, with Kim Christensen and Ulrich Hounyo.
  12. Estimation of integrated latency with high frequency data, with Deniz Erdemlioglu and Vladimir Volkov.
  13. Nonparametric local estimation of the receiver operating characteristic curve, with Chang Yuan Li.
  14. Estimation by realized copulas with high-frequency data, with Kim Christensen, Zhi Liu and Liu Wenjing.
  15. High-dimensional Hawkes processes with high-frequency data, with Yi Ding.

Reviewer services

Invited/contributed talk and seminar

Teaching

2024 Fall semester

INTRODUCTION TO ECONOMETRICS/ESSENTIALS OF REGRESSION ANALYSIS USING R

The emphasis of this class is on linear models with R. The objective is to learn what methods are available and more importantly, when they should be applied. Many examples are presented to clarify the use of the techniques and to demonstrate what conclusions can be made.

BROWNIAN MOTION（ＧＰＰ）

The course gives the technical tools of stochastic calculus to conduct work in the industry or empirical research in finance with high-frequency data. It starts with an introduction to probability space, continuous random variable, mean and variance. Then, we introduce Brownian motion, which is the main theoretical tool of this course.

SEMINAR/SEMINAR (QA)/SEMINAR (QB)(Type1)(Economy and Industry)/（３ｒｄ　Ｙｅａｒ）

The main objective of this seminar is to develop the skills needed to do work in the industry or research with financial data. The seminar will start with a semester of theoretical foundation in statistics and stochastic processes. In the second semester, the students will learn how to use R, and code the estimators theoretically derived in the first semester. In the second year, each student is expected to choose a project related to the field of financial econometrics, and to make a report and a final presentation to the class. In addition, the student is expected to discuss about the advancement of the project regularly during the semester. The presentation can include theory, numerical simulations and/or data analysis.

Older courses

SPECIAL RESEARCH TOPICS IN BUSINESS AND COMMERCE (S)(Economy and Industry)

The main objective of this course is to develop the skills needed to conduct work in the industry or empirical research in fields operating with time series data using the software R. The course aims to provide students with techniques and receipts for estimation and assessment of quality of economic models with time series data.

ESTIMATING VOLATILITY IN HIGH-FREQUENCY DATA/FINANCIAL ECONOMETRICS（ＧＰＰ）

The main objective of this course is to develop the skills needed to do work in the industry or research with the use of statistics and/or financial data. The course aims to provide students with techniques and receipts for estimation and assessment of quality of statistical models. Each student is expected to choose a project, and to make a report and a final presentation to the class. In addition, the student is expected to discuss about the advancement of the project at least once during the semester. The presentation can include theory, numerical simulations and/or data analysis.

Time series analysis

The main objective of this course is to develop the skills needed to do work in the industry or empirical research in fields operating with time series data. The course aims to provide students with techniques and receipts for estimation and assessment of quality of economic models with time series data. Special attention will be placed on limitations and pitfalls of different methods and their potential fixes. The course will also emphasize recent developments in Time Series Analysis and will present some open questions and areas of ongoing research. We will be using the software R, but students can do their homework using their own software.

Students

  1. Liu Wenjing (Ph.D., expected to graduate in 08/2026)
  2. Leonard Vimont (M.S., expected to graduate in 08/2025)
  3. Chang Yuan Li
  4. Seunghyeon Yu (Ph.D., graduated 02/2023)
  5. Julian Kota Kikuchi (B.S., graduated 08/2022)
  6. Renyi Qu (B.S., graduated 08/2022)
  7. Taro Tsuchiya (B.S., graduated 08/2021)
  8. Meihuazi Chen (B.S., graduated 08/2020)
  9. Kentaro Asaba (B.S., graduated 08/2020)