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. I am interested in theoretical problems arising in stochastic processes, applied probability, statistics, econometrics and finance. My research is partly supported by Japanese Society for the Promotion of Science Grants-in-Aid for Scientific Research (B) (23H00807).

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)

Fields of Interest

Stochastic Processes, Applied Probability, Statistics, Econometrics, Finance.

Employment

Education

Research

Published/accepted papers

  1. 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
  2. Cointegration in high frequency data, with Simon Clinet, Electronic Journal of Statistics, 2021, 15(1), 1263-1327. Download the Python code
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. Classifying patents based on their semantic content, with Antonin Bergeaud and Juste Raimbault. PLoS ONE, 2017, 12(4), e0176310. Download the R code
  9. 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. Submitted to the Advances in Applied Probability.
  2. High-frequency estimation of Ito semimartingale baseline for Hawkes processes, with Olivier Scaillet and Seunghyeon Yu. In revision to be submitted to the Annals of Statistics.
  3. Estimation and Test of Branching Ratio for Hawkes processes, with Seunghyeon Yu.
  4. Disentangling seasonality from a self-exciting process with time-varying baseline, with Olivier Scaillet and Seunghyeon Yu.
  5. What drives latency in high-frequency trading?, with Olivier Scaillet and Vladimir Volkov.
  6. Nonparametric estimation of generalized hazard function for first-hitting times, with Julian Kota Kikuchi. To be submitted to the Journal of the American Statistical Association.
  7. Noncausal Hawkes processes, with Kim Christensen and Aleksei Kolokolov.
  8. Formula of boundary crossing probabilities by the Girsanov theorem. Submitted to the Annals of Applied Probability.
  9. Explicit formula of boundary crossing probabilities for continuous local martingales to constant boundary. Submitted to the Annals of Probability.
  10. Brownian motion conditioned to spend limited time outside a monotone function, with Martin Kolb.

Reviewer services

Invited/contributed talk and seminar

Teaching

2023 Fall semester

Estimating volatility in high frequency data

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.

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.

Older courses

Stochastic Calculus, An Introduction with Application

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.

専攻演習S

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. Each student is expected to choose a dataset, to implement some methods from the book studied in class and to make a report based on the analysis of the results. More information about the project will be provided later in the class.

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. Seunghyeon Yu (Ph.D., graduated 02/2023, now research scholar at Northwestern University)
  2. Julian Kota Kikuchi (B.S., graduated 08/2022, now pre-doctoral program at the Federal Reserve Bank of Richmond)
  3. Renyi Qu (B.S., graduated 08/2022, now Master in Data Science at University of Pennsylvania)
  4. Taro Tsuchiya (B.S., graduated 08/2021, now Ph.D. student in C.S. at Carnegie Mellon University)
  5. Meihuazi Chen (B.S., graduated 08/2020, now Data Scientist at Bloomberg in Hong Kong)
  6. Kentaro Asaba (B.S., graduated 08/2020, now Trader at Societe Generale in Tokyo)