Yoann Potiron

Associate Professor

Faculty of Business and Commerce at Keio University

YoannPotiron I am from Chambery, France. I hold a B.S. in Applied Mathematics from Ecole Polytechnique in August 2013. I also obtained a M.S. in Statistics in August 2015 and a Ph.D. in Statistics in March 2016, both from University of Chicago. I joined the Faculty of Business and Commerce at Keio University in Tokyo as a Tenure-track Assistant Professor in April 2016, before obtaining my tenure in April 2018 and becoming an Associate Professor in April 2020. My research interests are financial econometrics, mathematical statistics, nonparametric statistics and applied probability. My 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 (possibly working remotedely as an exchange student). I am also looking for Master or B.S. students to work on research assistanship.





General information

Contact Information

Faculty of Business and Commerce, Keio University.
2-15-45 Mita, Minato-ku, Tokyo 108-8345
E-mail: potiron (at) fbc.keio.ac.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. Accepted by the Annals of the Institute of Statistical Mathematics.
  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. Estimation of latency with high frequency data, with Vladimir Volkov. Reject and Resubmit 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 Annals of the Institute of Statistical Mathematics.
  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 generalized hazard function for first-hitting times, with Julian Kota Kikuchi and Chang Yuan Li. Submitted to the Journal of the American Statistical Association.
  4. Explicit formula of boundary crossing probabilities for continuous local martingales to constant boundary. In revision to be submitted to the Journal of the American Statistical Association.
  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.

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 starts with a quick introduction to conditional expectation. Then, normal distribution, multivariate normal distribution and Brownian motion are defined and discussed carefully. Exercises and homework are based on a setup of high-frequency financial data estimation problems. This includes providing some basic tools of asymptotic statistics on the way.

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. Chang Yuan Li
  2. Seunghyeon Yu (Ph.D., graduated 02/2023, now research scholar at Northwestern University)
  3. Julian Kota Kikuchi (B.S., graduated 08/2022, now Ph.D. student in Economics at Institut Polytechnique Paris)
  4. Renyi Qu (B.S., graduated 08/2022, now Master in Data Science at University of Pennsylvania)
  5. Taro Tsuchiya (B.S., graduated 08/2021, now Ph.D. student in C.S. at Carnegie Mellon University)
  6. Meihuazi Chen (B.S., graduated 08/2020, now Data Scientist at Bloomberg in Hong Kong)
  7. Kentaro Asaba (B.S., graduated 08/2020, now Trader at Societe Generale in Tokyo)