Simon Clinet

Assistant Professor

Faculty of Economics, Keio University

Simon Clinet

Contact Information

Faculty of Economics, Keio University.
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
E-mail : clinet (at) keio.jp
Phone : +81-3-5427-1506
Office : South building, 20709 (7F)

Fields of Interest

Statistical Inference, Stochastic Processes, Financial Statistics, Financial Econometrics, High Frequency Data, Market Microstructure, Limit Order Books.

Employment

Assistant Professor, Faculty of Economics, Keio University.
September 2017 -

Education

Ph.D., Graduate School of Mathematical Sciences, The University of Tokyo.
Under the supervision of Nakahiro Yoshida.
October 2014 - September 2017.
Statistical inference for point processes and its applications to Limit Order Book.
(点過程に対する統計的推測及びリミットオーダーブックへの応用)
Full CV here .

Published/Accepted papers

  1. Clinet, S. and Potiron, Y. Efficient asymptotic variance reduction when estimating volatility in high frequency data.
    Journal of Econometrics, 2018.
  2. Clinet, S. and Potiron, Y. Statistical inference for the doubly stochastic self-exciting process.
    Bernoulli, 2018. (full version)
  3. Clinet, S. and Yoshida, N. Statistical inference for ergodic point processes and application to Limit Order Book.
    Stochastic Processes and Their Applications, 2017.

Submitted/Working papers

  1. Clinet, S. and Potiron, Y. Testing if the market microstructure noise is a function of the limit order book (submitted, under revision for Journal of Econometrics)
  2. Clinet, S. and Potiron, Y. Estimation for high-frequency data under parametric market microstructure noise (submitted)
  3. Clinet, S. and Potiron, Y. A relation between the efficient, transaction and mid prices: disentangling sources of high frequency market microstructure noise (submitted)
  4. Clinet, S., Potiron, Y. and Yabu, T., Testing for cointegration with multiple breaks

Seminars and conferences

Others

Here is a (partially documented) Python package ivol about efficient estimation of volatility and related quantities for noisy high-frequency data. I recommend to use it with Python 3. It is possible to apply the local method to several estimators as studied in my latest paper with Yoann Potiron (see above). In particular, are implemented :

  1. The Quasi Maximum Likelihood Estimator (QMLE).
  2. The Realized Kernel (RK).
  3. The Pre-Averaging Estimator (PAE).
  4. The Two-Scales Realized Volatility (TSRV).
  5. Estimators of the noise variance, the quarticity, and the heteroskedasticity measure ρ as studied in our paper.

Once you have unzipped the package in a directory accessible to your pythonpath, you can get started by typing import ivol; help(ivol) in a python console. The package is not complete and is probably not free of errors. Use it with caution !