Feng Li

Central University of Finance and Economics (CUFE)

39 South College Road

Haidian District

Beijing, Beijing 100081

China

http://feng.li/

  • Central University of Finance and Economics (CUFE)

SCHOLARLY PAPERS

3

DOWNLOADS

285

CITATIONS

2

Ideas:
“  I am working on projects that are related with financial risks and statistical models. Research papers and computer code are available at https://feng.li/  ”

  • Central University of Finance and Economics (CUFE)

Scholarly Papers (3)

Actions:
Email selected abstracts
1.

Credit Risk Clustering in a Business Group: Which Matters More, Systematic or Idiosyncratic Risk?

Number of pages: 24 Posted: 04 Jun 2018
Feng Li and Zhuojing He
Central University of Finance and Economics (CUFE) and Central University of Finance and Economics (CUFE)
Downloads 178 (161,855)

Abstract:

Loading...

Business Groups, Credit Risk Clustering, Covariate-Dependent Copulas, MCMC

2.

Flexible Modeling of Conditional Distributions Using Smooth Mixtures of Asymmetric Student T Densities

Riksbank Research Paper Series No. 64, Sveriges Riksbank Working Paper Series No. 233
Number of pages: 24 Posted: 04 Mar 2010 Last Revised: 08 May 2010
Feng Li, Mattias Villani and Robert Kohn
Central University of Finance and Economics (CUFE), Sveriges Riksbank - Research Division and University of New South Wales - School of Economics and School of Banking and Finance
Downloads 70 (316,217)
Citation 2

Abstract:

Loading...

Bayesian Inference, Markov Chain Monte Carlo, Mixture of Experts, Variable Selection, Volatility Modeling

3.

Modeling Conditional Densities Using Finite Smooth Mixtures

Riksbank Research Paper Series No. 76, Sveriges Riksbank Working Paper Series No. 245
Number of pages: 22 Posted: 27 Jan 2011
Robert Kohn, Feng Li and Mattias Villani
University of New South Wales - School of Economics and School of Banking and Finance, Central University of Finance and Economics (CUFE) and Sveriges Riksbank - Research Division
Downloads 37 (419,460)

Abstract:

Loading...

Bayesian inference, Markov chain Monte Carlo, Mixture of Experts, Variable selection