Articles with public access mandates - Liang PengLearn more
Not available anywhere: 15
A unified test for predictability of asset returns regardless of properties of predicting variables
X Liu, B Yang, Z Cai, L Peng
Journal of Econometrics 208 (1), 141-159, 2019
Mandates: National Natural Science Foundation of China
Statistical inference for Lee-Carter mortality model and corresponding forecasts
Q Liu, C Ling, L Peng
North American Actuarial Journal 23 (3), 335-363, 2019
Mandates: National Natural Science Foundation of China
Tail index of an AR (1) model with ARCH (1) errors
NH Chan, D Li, L Peng, R Zhang
Econometric Theory 29 (5), 920-940, 2013
Mandates: Research Grants Council, Hong Kong
Bias-corrected inference for a modified Lee–Carter mortality model
Q Liu, C Ling, D Li, L Peng
ASTIN Bulletin: The Journal of the IAA 49 (2), 433-455, 2019
Mandates: National Natural Science Foundation of China
Uniform test for predictive regression with AR errors
C Li, D Li, L Peng
Journal of Business & Economic Statistics 35 (1), 29-39, 2017
Mandates: National Natural Science Foundation of China
Two-step risk analysis in insurance ratemaking
S Ki Kang, L Peng, A Golub
Scandinavian Actuarial Journal 2021 (6), 532-542, 2021
Mandates: US National Science Foundation
Nonparametric inference for sensitivity of Haezendonck–Goovaerts risk measure
X Wang, Q Liu, Y Hou, L Peng
Scandinavian Actuarial Journal 2018 (8), 661-680, 2018
Mandates: US National Science Foundation
Empirical likelihood test for causality of bivariate AR (1) processes
D Li, NH Chan, L Peng
Econometric Theory 30 (2), 357-371, 2014
Mandates: Research Grants Council, Hong Kong
Unified inference for an AR process regardless of finite or infinite variance GARCH errors
H Huang, X Leng, X Liu, L Peng
Journal of Financial Econometrics 18 (2), 425-470, 2020
Mandates: National Natural Science Foundation of China
Statistical inference for a relative risk measure
Y He, Y Hou, L Peng, J Sheng
Journal of Business & Economic Statistics 37 (2), 301-311, 2019
Mandates: National Natural Science Foundation of China
Haezendonck–Goovaerts risk measure with a heavy tailed loss
Q Liu, L Peng, X Wang
Insurance: Mathematics and Economics 76, 28-47, 2017
Mandates: US National Science Foundation
Empirical likelihood test for the equality of several high-dimensional covariance matrices
G Liao, L Peng, R Zhang
Science China Mathematics, 1-18, 2021
Mandates: National Natural Science Foundation of China
Inference for the Lee-Carter Model with An AR (2) Process
D Li, C Ling, Q Liu, L Peng
Methodology and Computing in Applied Probability, 1-29, 2021
Mandates: US National Science Foundation, National Natural Science Foundation of China
Supplement to “Inference for conditional value-at-risk of a predictive regression.”
Y He, Y Hou, L Peng, H Shen
Mandates: National Natural Science Foundation of China
Jackknife empirical likelihood test for the equality of degrees of freedom in t-copulas
Y Hou, D Li, A Liu, L Peng
Science China Mathematics 63, 789-822, 2020
Mandates: National Natural Science Foundation of China
Available somewhere: 27
Estimating the tail dependence function of an elliptical distribution
C Klüppelberg, G Kuhn, L Peng
Mandates: German Research Foundation
An efficient approach to quantile capital allocation and sensitivity analysis
V Asimit, L Peng, R Wang, A Yu
Mathematical Finance 29 (4), 1131-1156, 2019
Mandates: Natural Sciences and Engineering Research Council of Canada
Testing the predictability of US housing price index returns based on an IVX-AR model
B Yang, W Long, L Peng, Z Cai
Journal of the American Statistical Association 115 (532), 1598-1619, 2020
Mandates: National Natural Science Foundation of China
Uniform interval estimation for an AR (1) process with AR errors
J Hill, D Li, L Peng
Statistica Sinica, 119-136, 2016
Mandates: National Natural Science Foundation of China
Estimation of extreme quantiles for functions of dependent random variables
J Gong, Y Li, L Peng, Q Yao
Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2015
Mandates: UK Engineering and Physical Sciences Research Council
Publication and funding information is determined automatically by a computer program