SoFiE Financial Econometrics Summer School

Date & Time: 
Sunday, February 11, 2018 - 22:55

SoFiE Financial Econometrics Summer School

"Introduction to High Frequency Financial Econometrics and Statistics"

August 13-August 17, 2018

Shanghai, China

Volatility Institute, NYU Shanghai

1555Century Avenue, Pudong, Shanghai, China, 200122

 

Sponsors

To be determined

 

The SoFiE Financial Econometrics Schools are annual week-long research-based courses for Ph.D. students and new faculty in financial econometrics. For the first two years, the Summer School was held at Oxford University’s Oxford-Man Institute and in 2014 it moved to Harvard University. In 2015 and 2016, it was held in Brussels. In 2017, The SoFiE Financial Econometrics Summer School took place at the Kellogg School of Management, Northwestern University.

The editorial board for these annual series is made up of professors as follows:

Torben G. Andersen (Northwestern)

Luc Bauwens (Catholic University of Louvain)

Francis X. Diebold (University of Pennsylvania, past President of SoFiE)

Eric Ghysels (University of North Carolina, Chapel Hill, Secretary and Founding Co-President of SoFiE)

Ravi Jagannathan (Northwestern and President SoFiE)

Per Mykland (University of Chicago and President-Elect SoFiE)

Eric Renault (Brown University and past SoFiE President)

Neil Shephard (Harvard University)

Viktor Todorov (Northwestern)

The SoFiE Financial Econometrics Summer School 2018 is to be held at the Volatility Institute, NYU Shanghai, from Monday August 13 through Friday August 17, 2018.

 

Course Description:

The course is intended for Ph.D. students and researchers in statistics, econometrics and finance with an introduction to methods to analyze high frequency data and estimate parametric and nonparametric financial models using such data. The course assumes some familiarity with stochastic calculus and mathematical finance but is otherwise self-contained.

This course is open to all students and researchers who apply to attend and are admitted. The course will offer a limited number of course participants an opportunity to present their current research and receive feedback from the instructors and other course participants. Students interested in making a presentation (which is entirely optional) should indicate so on their application and submit the research paper that will form the basis of their presentation. Students who are selected to make a presentation will be informed at the same time as they receive their admission decisions.

Students will be provided with a packet of lecture notes when the course starts.

 

Lecturers:

Professor Yacine Ait-Sahalia (Department of Economics - Bendheim Center for Finance, Princeton University)

Yacine Aït-Sahalia is the Otto A. Hack 1903 Professor of Finance and Economics at Princeton University. He served as the inaugural Director of the Bendheim Center for Finance from 1998 until 2014. He was previously an Assistant Professor (1993-96), Associate Professor (1996-98) and Professor of Finance (1998) at the University of Chicago’s Graduate School of Business. He received the University of Chicago GSB Emory Williams Award for Excellence in Teaching in 1995 and was named an outstanding faculty by Business Week’s 1997 Guide to the Best Business Schools. His research concentrates on financial econometrics, investments, fixed income and derivative securities, and has been published in leading academic journals. Professor Aït-Sahalia is a Fellow of the Econometric Society, a Fellow of the Institute of Mathematical Statistics, a Fellow of the American Statistical Association, a Fellow of the Society for Financial Econometrics, an Alfred P. Sloan Foundation Research Fellow, a Fellow of the Guggenheim Foundation and a Research Associate for the National Bureau of Economic Research. He is also the recipient of the 1997 Michael Brennan Award, the 1998 Cornerstone Research Award, the 2001 FAME Research Award and the 2003 Dennis J. Aigner Award. He recently served as the editor of the Review of Financial Studies and currently serves as the co-Managing Editor of the Journal of Econometrics and on the editorial board of a number of academic journals. He received his Ph.D. in Economics from the Massachusetts Institute of Technology in 1993 and his undergraduate degree from Ecole Polytechnique in France.

Professor Per Mykland (Department of Statistics, University of Chicago)

Per Mykland is Robert M. Hutchins Distinguished Professor of Statistics and Finance at the University of Chicago, where he is also Scientific Director of the Stevanovich Center for Financial Mathematics. He has held appointments at Oxford and Princeton. Mykland’s main research interests are statistics and econometrics for time dependent processes, including time series and and continuous processes. He is a leader in the field. Highlights include the development of likelihood and expansion methods for martingales (fair games), especially in the context of estimating equations. The results have wide application, including the construction of new nonparametric likelihoods in time series and survival analysis. His recent focus is high-frequency data, mainly in finance. In one breakthrough, he has shown how to connect the analysis of such data with classical statistical techniques, using contiguity. He has contributed to the theory of estimation under microstructure, including the development of the two-scales and pre-averaging estimators of volatility and other intra-day quantities. He has also developed an approach for integrating statistical and market information in the pricing and hedging of options, with a particular view to hedging against statistical uncertainty. Most recently, he has developed the “observed asymptotic variance”, which sets nonparametric standard errors for estimators based on high frequency data. A long-run research goal is for a unified theory of continuous-time finance and high-frequency data. The former reasons through hypothetical high-frequency data, but now these data are no longer hypothetical but very real. Professor Mykland is Associate Editor for several journals, including the Journal of the American Statistical Association, and Journal of Financial Econometrics. He is a fellow of the Institute of Mathematical Statistics, the American Statistical Association and the Society for Financial Econometrics (SoFiE). He is a member of the Council of the SoFiE and has previously served on the Council of the Institute of Mathematical Statistics. Mykland is currently President of the Society for Financial Econometrics, from 2017 to 2019. He has supervised sixteen PhD students, who are now spread between academia and industry. He received his Ph.D. in Statistics from University of California, Berkeley.

 

Guest Speakers:

To be determined

 

Course Schedule:

  • Lecture 1: Introductory example of high frequency data and estimation. Introduction to stochastic calculus.
  • Lecture 2: Maximum-likelihood estimation of parametric models in finance using expansion methods.
  • Lecture 3: Estimation of volatility in the absence of microstructure. Asymptotic approximations for high frequency estimators. Use of measure change, and localization.
  • Lecture 4: Examples of maximum-likelihood estimation: term structure models, stochastic volatility models.
  • Lecture 5: Microstructure, multiple and high dimension, asynchronous observation. Application to Regression, ANOVA, and Principal Component Analysis.
  • Lecture 6: Expansion methods for implied volatility models.
  • Lecture 7: Leverage effect. Observed standard error. Edge effects. Contiguity

 

Reading List:

Aït-Sahalia, Y. (1999): “Transition Densities for Interest Rate and Other Nonlinear Diffusions,” The Journal of Finance, 54, 1361–1395.

——— (2002): “Maximum-Likelihood Estimation of Discretely-Sampled Diffusions: A Closed-Form Approximation Approach,” Econometrica, 70, 223–262.

——— (2008): “Closed-Form Likelihood Expansions for Multivariate Diffusions,” Annals of Statistics, 36, 906–937.

——— (2009): “Estimating and Testing Continuous-Time Models in Finance: The Role of Transition Densities,” Annual Review of Financial Economics, 1, 341–359.

Aït-Sahalia, Y. and J. Jacod (2014): High Frequency Financial Econometrics, Princeton University Press.

Aït-Sahalia, Y. and R. Kimmel (2007): “Maximum Likelihood Estimation of Stochastic Volatility Models,” Journal of Financial Economics, 83, 413–452.

——— (2010): “Estimating Affine Multifactor Term Structure Models Using Closed-Form Likelihood Expansions,” Journal of Financial Economics, 98, 113–144.

Aït-Sahalia, Y. and D. Xiu (2018): “Principal Component Analysis of High Frequency Data,” Journal of the American Statistical Association, forthcoming.

   Chen, D., P. A. Mykland, and L. Zhang (2018): “The Five Trolls under the Bridge: Principal Component Analysis with Asynchronous and Noisy High Frequency Data,” Tech. rep., University of Illinois at Chicago and University of Chicago.

Mykland, P. A. and L. Zhang (2009): “Inference for continuous semimartingales observed at high frequency,” Econometrica, 77, 1403–1445.

——— (2012): “The Econometrics of High Frequency Data,” in Statistical Methods for Stochastic Differential Equations, ed. by M. Kessler, A. Lindner, and M. Sørensen, New York: Chapman and Hall/CRC Press, 109–190.

——— (2017): “Assessment of Uncertainty in High Frequency Data: The Observed Asymptotic Variance,” Econometrica, 85, 197231.

Mykland, P. A., L. Zhang, and D. Chen (2017): “The Algebra of Two Scales Estimation, and the S-TSRV: High Frequency Estimation that is Robust to Sampling Times,” Tech. rep., University of Illinois at Chicago and University of Chicago.

Wang, C. D. and P. A. Mykland (2014): “The Estimation of Leverage Effect with High Frequency Data,” Journal of the American Statistical Association, 109, 197–215.

Zhang, L. (2011): “Estimating Covariation: Epps Effect and Microstructure Noise,” Journal of Econometrics, 160, 33–47.

   Zhang, L., P. A. Mykland, and Y. Aït-Sahalia (2005): “A Tale of Two Time Scales: Determining Integrated Volatility with Noisy High-Frequency Data,” Journal of the American Statistical Association, 100, 1394–1411. 

 

Applications: 

Applicants should register and submit electronical materials through the following registration website:

  https://research.shanghai.nyu.edu/vins/sofie_summer_school_registration.

The applications should include a full CV and motivation letter (half-page length) explaining why attending this course would be helpful to the applicant’s research work. All materials should be in pdf version. The application deadline is 13 May 2018. Decisions will be emailed out by 03 June 2018. 

Paper Presentations: 

Applicants are encouraged to present some of their thesis work during the morning session of the last day (Friday). For this, they should preferably append a paper to their application. They can submit an extensive abstract if the paper is not yet finished. The paper topics need not be closely linked to the course but obviously must be in the field of financial econometrics. Papers will be selected by the organizing committee on the basis of their quality.

 

Fees:

$250 for Ph. D. students and faculty members attending this course.
$500 for Ph.D. level colleagues from other institutions.

Confirmed admission of a selected applicants will be conditional on the fee payment in due time (details will be provided in the admission email).

All accepted participants will be expected to be members of the Society for Financial Econometrics or join before their place is confirmed. See http://sofie.stern.nyu.edu/membership on how to join the society (where a student membership option is available).



Travel Accommodation Costs: 

Attendees will be required to pay their own travel and accommodation. No assistance will be offered in this respect. During the teaching schedule (Monday-Friday) at NYU Shanghai, lunch, coffee and tea will be provided free of charge. Evening meals will not be organized and will be at the expense of the participants.

 

Event Types: 
Lectures