Yoann Potiron

Associate Professor

Faculty of Business and Commerce at Keio University

YoannPotiron I am from Chambery, France. I hold a B.S. with a major in Applied Mathematics and minor in Computer Science from Universite de Lyon 1 in 2010. Moreover, I obtained a M.S. (Diplome d'Ingenieur) in Probability from Ecole Polytechnique in 2013. Finally, I graduated for my Ph.D. in Statistics from University of Chicago in 2016. I joined the Faculty of Business and Commerce at Keio University in Tokyo as a Tenure-track Assistant Professor in 2016, before obtaining my tenure in 2018 and becoming an Associate Professor in 2020. I am interested in problems arising in statistics, stochastic processes, econometrics and finance.

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 collaborative research and/or 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

Statistics, Stochastic Processes, Econometrics, Finance.

Employment

Education

Ph.D., Department of Statistics, The University of Chicago.
Committee: Per Aslak Mykland, Dacheng Xiu, Dan Nicolae and Greg Lawler.
September 2013 - March 2016

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, 649-669.
  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 codePreviously circulated under the name "A relation between the efficient, transaction and mid prices: Disentangling sources of high frequency market microstructure noise".
  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 Previously circulated under the name "Estimating the Integrated Parameter of the Locally Parametric Model in High-Frequency Data".
  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, 209, 289-337. Download the Python code Previously circulated under the name "Testing if the market microstructure noise is a function of the limit order book".
  6. Efficient Asymptotic Variance Reduction when Estimating Volatility in High Frequency Data, with Simon Clinet, Journal of Econometrics, 2018, 206, 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, 20-41. Download the R code

Papers in revision

  1. None

Submitted papers

  1. Existence in the inverse Shiryaev problem

Working papers/On-going projects

  1. An expression of volatility in the inverse first-passage-time problem, with Renyi Qu
  2. Sovereign risk and low-latency trading, with Vladimir Volkov
  3. Nonparametric Estimation of the Intensity Integrated Volatility for the Doubly Stochastic Poisson Process, with Seunghyeon Yu
  4. Estimating volatility in the inverse first-passage-time problem, with Julian Kota Kikuchi
  5. Supplier switching and the degree of outsourcing, with Yukako Ono

Communication

  1. An Hypernetwork Approach to Measure Technological Innovation, with Juste Raimbault and Antonin Bergeaud, Conference on Complex Systems, 2016, Amsterdam.

Invited lectures

Referee services

Annals of the Institute of Statistical Mathematics, Electronic Journal of Statistics, Journal of the American Statistical Association, Journal of Business & Economic Statistics, Journal of Econometrics, Journal of Financial Econometrics, Journal of Financial and Quantitative Analysis, Metrika, Quantitative Finance, Research Policy, Science China Mathematics, The Annals of Applied Statistics.

Teaching

2021 Spring 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 financial data. The course aims to provide students with techniques and receipts for estimation and assessment of quality of financial 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. Julian Kota Kikuchi (B.S., expected to graduate in 08/2022)
  2. Renyi Qu (B.S., expected to graduate in 08/2022)
  3. Seunghyeon Yu (Ph.D. candidate in finance at the Korea Advanced Institute of Science and Technology, expected to graduate in 02/2022)
  4. Taro Tsuchiya (B.S., graduating in 08/2021, will start Ph.D. in C.S. at Carnegie Mellon University)
  5. Meihuazi Chen (B.S., graduated 08/2020, now Business Analyst at Elements Global Services)
  6. Kentaro Asaba (B.S., graduated 08/2020, now Trader at Societe Generale)