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CRETA Workshop on Advanced Econometrics 13_普林斯頓大學范劍青教授

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CRETA Workshop on Advanced Econometrics 13_普林斯頓大學范劍青教授

簡介摘要:

CRETA Workshop on Advanced Econometrics 13_Professor Jianqing Fan - 12 January 2012, Chong Guang Lecture Hall, 2F, Bldg. 1, College of Management

 

CRETA is pleased to invite Professor Jianqing Fan from Princeton University as our first visitor in 2012!

Prof. Fan is to give lectures on 2 topics:
I Conditional Sparsity in Portfolio Management and Econometric Inference
II Leverage Effect Puzzle
on CRETA Workshop on Advanced Econometrics 13. The workshop is due to take place on Jan. 12 (Thur) at Chong Guang Lecture Hall, 2F, Bldg. 1, College of Management, NTU (台大管理學院一號館 2 樓重光講堂). All participants are welcomed! Please be sure to register your attendance online by noon, Jan. 10(Tue).

*Date: Jan. 12(Thur), 2012, 14:30 pm – 17:30 pm
*Venue: Chong Guang Lecture Hall, 2F, Bldg. 1, College of Management, NTU
(台大管理學院一號館 2 樓重光講堂)
*Topic:
I Conditional Sparsity in Portfolio Management and Econometric Inference
II Leverage Effect Puzzle

[Lecture Overview]
I Conditional Sparsity in Portfolio Management and Econometric Inference
Covariance matrix plays a central role in finance and economics as well as statistical inferences. Popular regularization methods of directly exploiting sparsity are not directly applicable to many financial problems such as portfolio allocations and risk managements. The methods based on the strict factor models as in Fan, Fan and Lv (2008) assume independent idiosyncratic noises. This assumption, however, is too restrictive. By imposing conditional sparsity, we allow the presence of the cross-sectional correlation even after taking out common factors, and it enables us to combine the merits of both methods. We deal with the situations under which the conditioning factors are both observable and unobservable. We estimate the sparse covariance using the adaptive thresholding technique in Cai and Liu (2011), taking into account the fact that direct observations of the idiosyncratic noises are unavailable. The impact of high dimensionality is then studied theoretically and confirmed by simulation experiments. The results for the case with unobservable factors are also obtained.

II Leverage Effect Puzzle
The leverage effect parameter refers to the correlation between asset returns and their volatility. A natural estimate of such a parameter is to use correlation between the daily returns and the changes of daily volatility estimated by high-frequency data. The puzzle is that such an estimate yields nearly zero correlation for all assets such as SP500 and Dow Jone Industrial Average that we have tested. To solve the puzzle, We develop a model to understand the bias problem. The asymptotic biases involved in high frequency estimation of the leverage effect parameter are derived. They quantify the biases due to discretization errors in estimating spot volatilities, biases due to estimation error, and the biases due to market microstructure noises. They enable us to propose novel bias correction methods for estimating the leverage effect parameter. The proposed methods are convincingly illustrated by simulation studies and several empirical applications.

講者介紹:

Professor Jianqing Fan, is a statistician and financial econometrician. He is the current Frederick L. Moore '18 Professor of Finance, and Professor of Statistics at the Princeton University. He is the winner of 2000 COPSS Presidents' Award. Professor Fan is interested in statistical methods in financial econometrics and risk management, computational biology, biostatistics, high-dimensional statistical learning, data-analytic modeling, longitudinal and functional data analysis, nonlinear time series, wavelets and their applications, among others.

For more information about Professor Fan, please go to his website: http://orfe.princeton.edu/~jqfan/biography.html

議  程:

Jan. 12 (Thur) Chong Guang Lecture Hall, 2F (二樓重光講堂)
14:00-14:30 Registration
14:30-15:45 Lecture 1 (75mins/session)
15:45-16:05 Tea Break
16:05-17:20 Lecture 2 (75mins/session)
*Lectures in English