Yoann Potiron

Tenured Assistant Professor

Faculty of Business and Commerce at Keio University

YoannPotiron

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 : My office is in Research building 439B (4th floor), but I work from my coworker Simon Clinet's office in South Research Building 20709 (7th floor).

Fields of Interest

Econometrics, Finance, High Frequency Data, Market Microstructure Noise, Limit Order Book.

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. Disentangling Sources of High Frequency Market Microstructure Noise, with Simon Clinet. To appear in Journal of Business & Economic Statistics. 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".
  2. Local Parametric Estimation in High Frequency Data, with Per Aslak Mykland. To appear in Journal of Business & Economic Statistics. Download the R code Previously circulated under the name "Estimating the Integrated Parameter of the Locally Parametric Model in High-Frequency Data".
  3. 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".
  4. 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
  5. 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
  6. Classifying Patents Based on their Semantic Content, with Antonin Bergeaud and Juste Raimbault. PLoS ONE, 2017, 12(4), e0176310. Download the R code
  7. Estimation of Integrated Quadratic Covariation with Endogenous Sampling Times, with Per Aslak Mykland, Journal of Econometrics, 2017, 197, 20-41. Download the R code

Submitted papers

  1. Estimation for high-frequency data under parametric market microstructure noise, with Simon Clinet.
  2. Cointegration in high frequency data, with Simon Clinet.

Working papers/On-going projects

  1. How does activity of high frequency traders differ from human traders?, with Simon Clinet and Vladimir Volkov.

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

Electronic Journal of Statistics, Journal of the American Statistical Association, Journal of Econometrics, Journal of Financial Econometrics, Quantitative Finance, Research Policy, Science China Mathematics, The Annals of Applied Statistics.

Teaching

2019 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 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.

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.

Older courses

Essentials of Regression Analysis Using R (Monday 14:45-16:15 in room 334)

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.

Stochastic Calculus, An Introduction with Application (Thursday 14:45-16:15 in room 351A)

The course starts with a quick introduction to normal distribution and multivariate normal distribution, and then Brownian motion and the Ito integral are defined and discussed carefully. Numerous applications are given to high-frequency financial data estimation problems. This includes providing some basic tools of asymptotic statistics on the way.

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.

Other

You can find the website of my co-author Simon Clinet.