Econometrics of High-Dimensional Risk Networks

October 16 and 17, 2015 
Chicago

 

Organizers
     Francis X. Diebold     Eric Ghysels        Per Mykland       Lan Zhang  

The purpose of the workshops is to bring together researchers in the very active and burgeoning field of analyzing large scale data pertaining to risk measures.  This includes but is not limited to studying common factors extracted from risk measures such as realized volatility, estimation of large dimensional covariance matrices, factor analysis of liquidity/default risk measures, networks tools for measuring connectedness, etc.  


Program

Friday, October 16

9:00 am     Registration


10:15 am   Opening remarks


10:30 am   Robert Engle   Stern, NYU

Long Run Risk Management: Scenario Generation for the Term Structure


11:15 am   Bryan Kelly   Chicago Booth School of Business

Firm Volatility in Granular Networks


12:00 pm   Francis X. Diebold   University of Pennsylvania

On Estimation and Visualization in Ultra-High-Dimensional Dynamic Stochastic Econometrics


1:00 pm    Lunch


2:00 pm    Kim Christensen   CREATES, Denmark

Inference from high-frequency: A subsampling approach


2:45 pm    Nikolaus Hautsch   University of Vienna

Efficient Iterative Maximum Likelihood Estimation


3:30 pm    Break


4:00 pm    Zheng Tracy Ke   University of Chicago

Statistical limits and spectral methods for high-dimensional clustering


4:45 pm    George Tauchen   Duke University

Jump Regressions


5:30 pm    Reception


Saturday, October 17

8:30 am     Registration


9:15 am     Xinghua Zheng   HKUST

On the inference about the spectral distribution of high-dimensional covariance matrix based on noisy observations – with applications to integrated covolatility matrix inference in the presence of microstructure noise


10:00 am   Yazhen Wang   University of Wisconsin

Large Volatility Matrix Estimation with Factor-Based Diffusion Model for High-Frequency Financial data


10:45 am   Break


11:15 am   Dacheng Xiu   Chicago Booth School of Business

Using Principal Component Analysis to Estimate a High Dimensional Factor Model with High-Frequency Data


12:00 pm   Eric Ghysels   University of North Carolina

Are Main Street and Wall Street Driven by the same Factors?


1:00 pm     Lunch


2:00 pm    Mark Podolskij   CREATES, Denmark

Testing the maximal rank of the volatility process for continuous diffusions observed with noise


2:45 pm    Bas Werker   Tilburg University

Arbitrage Pricing Theory for Squared Returns


3:30 pm    Yingying Li   HKUST

Solving the High-dimensional Markowitz Optimization Problem: When Sparse Regression Meets Random Matrix Theory


4:15 pm    Closing remarks