Welcome to Liang Peng's WebPage


Dr. Liang Peng (CV)

Thomas P Bowles Chair Professor of Actuarial Science
Department of Risk Management and Insurance
Robinson College of Business
Georgia State University

Phone:  (404)413-7489


Email:  lpeng@gsu.edu

Mailing Address:

Office 1134, Downtown campus
35 Broad St. NW
Atlanta, GA 30303

Teaching in Fall 2018:

Education:

  • Ph.D. in mathematical statistics at Erasmus University Rotterdam . Supervisor: Professor Laurens de Haan. Time: Nov. 1, 1993 — June 25, 1998. Thesis title: Second Order Condition and Extreme Value Theory , Tinbergen Institute Research Series 178, Thesis Publisher, Amsterdam, 1998.
  • M.S. in probability at Peking University. Supervisor: Professor Shihong Cheng. Time: Sep. 1, 1990 — July 1, 1993.
  • B.S. in mathematics at Zhejiang University. Time: Sep. 1, 1986 — July 1, 1990.


Experience:


Research Interests:

  • Actuarial science
  • Nonparametric statistics
  • Financial econometrics
  • Extreme value theory in finance and insurance
  • Copula and tail copula in risk management


Technical reports:

Some papers:

  • [105] X. Liu, B. Yang, Z. Cai and L. Peng (2018). A unified test for predictability of asset returns. Journal of Econometrics. To appear.
  • [104] X. Wang, Q. Liu, Y. Hou and L. Peng (2018). Nonparametric inference for sensitivity of Haezendonck-Goovaerts risk measure. Scandinavian Actuarial Journal. To appear.
  • [103] F. Wang, L. Peng, Y. Qi and M. Xu (2018). Maximum penalized likelihood estimation for the endpoint and exponent of a distribution. Statistica Sinica. To appear.
  • [102] Yi He, Yanxi Hou, Liang Peng and Jiliang Sheng (2018). Statistical inference for a relative risk measure. Journal of Business and Economic Statistics. To appear.
  • [101] R. Zhang, C. Li and L. Peng (2018). Inference for the Tail Index of a GARCH(1,1) Model and an AR(1) Model with ARCH(1) Errors. Econometric Reviews. To appear. (Technical report)
  • [100] F. Lin, L. Peng, J. Xie and J. Yang (2018). Stochastic distortion and its transformed copula. Insurance: Mathematics and Economics 79, 148–166.
  • [99] X. Leng and L. Peng (2017). Testing for unit root in Lee-Carter mortality model. ASTIN Bulletin 47, 715–735.
  • [98] Q. Liu, L. Peng and X. Wang (2017). Haezendonck-Goovaerts risk measure with a heavy tailed loss. Insurance: Mathematics and Economics 76, 28–47.
  • [97] C. Li, D. Li and L. Peng (2017). Uniform test for predictive regression with AR errors. Journal of Business and Economic Statistics 35, 29–39.
  • [96] X. Leng and L. Peng (2016). Inference pitfalls in Lee-Carter model for forecasting mortality. Insurance: Mathematics and Economics 70, 58–65.
  • [95] X. Wang and L. Peng (2016). Inference for intermediate Haezendonck-Goovaerts risk measure. Insurance: Mathematics and Economics 68, 231–240.
  • [94] A. Asimit, R. Gerrard, Y. Hou and L. Peng (2016). Tail dependence measure for modeling financial extreme co-movements. Journal of Econometrics 194, 330–348.
  • [93] J. Hill, D. Li and L. Peng (2016). Uniform interval estimation for an AR(1) process with AR(p) errors. Statistica Sinica 26, 119–136.
  • [92] A. Liu, Y. Hou and L. Peng (2015). Interval estimation for a measure of tail dependence. Insurance: Mathematics and Economics 64, 294–305.
  • [91] L. Peng, X. Wang and Y. Zheng (2015). Empirical likelihood inference for Haezendonck-Goovaerts risk measure. European Actuarial Journal 5, 427–445.
  • [90] J. Gong, Y. Li, L. Peng and Q. Yao (2015). Estimation of extreme quantiles for functions of dependent random variables. Journal of the Royal Statistical Society Series B 77, 1001–1024.
  • [89] R. Wang, L. Peng and J. Yang (2015). CreditRisk^+ model with dependent risk factors. North American Actuarial Journal 19, 24–40.
  • [88] S. Ling, L. Peng and F. Zhu (2015). Inference for a special bilinear time series model. Journal of Time Series Analysis 36, 61–65.
  • [87] L. Peng and R. Wang (2014). Estimating bivariate t-copulas via Kendall’s tau. Variance 8, 43–54.
  • [86] L. Peng (2014). Joint tail of ECOMOR and LCR reinsurance treaties. Insurance: Mathematics and Economics 58, 116–120.
  • [85] L. Peng, Y. Qi and F. Wang (2014). Test for a mean vector with fixed or divergent dimension. Statistical Science 29, 113–127.
  • [84] J. Hill and L. Peng (2014). Unified interval estimation for random coefficient autoregressive models. Journal of Time Series Analysis 35, 282–297.
  • [83] Fukang Zhu, Zongwu Cai and Liang Peng (2014). Predictive regressions for macroeconomic data. Annals of Applied Statistics 8, 577–594.
  • [82] D. Li, N.H. Chan and L. Peng (2014). Empirical likelihood test for causality for bivariate AR(1) processes. Econometric Theory 30, 357–371.
  • [81] R. Zhang, L. Peng and R. Wang (2013). Tests for covariance matrix with fixed or divergent dimension. Annals of Statistics 41, 2075–2096.
  • [80] N.H. Chan, D. Li, L. Peng and R. Zhang (2013). Tail index of an AR(1) model with ARCH(1) errors. Econometric Theory 29, 920–940.
  • [79] R. Wang, L. Peng and J. Yang (2013). Jackknife empirical likelihood for parametric copulas. Scandinavian Actuarial Journal 5, 325–339.
  • [78] R. Wang, L. Peng and Y. Qi (2013). Jackknife empirical likelihood test for the equality of two high dimensional means. Statistica Sinica 23, 667–690.
  • [77] R. Wang, L. Peng and J. Yang (2013). Bounds for the sum of dependent risks and worst Value-at-Risk with monotone marginal densities. Finance and Stochastics 17, 395–417.
  • [76] L. Peng, L. Qian and J. Yang (2013). Weighted estimation of dependence function for an extreme-value distribution. Bernoulli 19, 492–520.
  • [75] S.X. Chen, L. Peng and C. Yu (2013). Parameter estimation and model testing for continuous-time markov processes via conditional characteristic functions. Bernoulli 19, 228–251.
  • [74] Z. Li and L. Peng (2012). Bootstrapping endpoint. Sankhya A 74, 126–140.
  • [73] Huijun Feng and Liang Peng (2012). Jackknife empirical likelihood test for regression models. Journal of Multivariate Analysis 112, 63–75.
  • [72] L. Peng, Y. Qi, R. Wang and J. Yang (2012). Jackknife empirical likelihood methods for risk measures and related quantities. Insurance: Mathematics and Economics 51, 142–150.
  • [71] N.H. Chan, L. Peng and R. Zhang (2012). Interval estimation of the tail index of a Garch(1,1) model. Test 21, 546–565.
  • [70] L. Peng (2012). Approximate jackknife empirical likelihood method for estimating equations. Canadian Journal of Statistics 40, 110–123.
  • [69] N.H. Chan, D. Li and L. Peng (2012). Toward a unified interval estimation of autoregressions. Econometric Theory 28, 705–717.
  • [68] Liang Peng, Yongcheng Qi and Ingrid Van Keilegom (2012). Jackknife empirical likelihood method for copulas. Test 21, 74–92.
  • [67] H. Feng and L. Peng (2012). Jackknife empirical likelihood tests for distribution functions. Journal of Statistical Planning and Inference 142, 1571–1585.
  • [66] R. Zhang, L. Peng and Y. Qi (2012). Jackknife-blockwise empirical likelihood methods under dependence. Journal of Multivariate Analysis 104, 56–72.
  • [65] R. Wang and L. Peng (2011). Jackknife empirical likelihood intervals for Spearman’s rho. North American Actuarial Journal 15, 475–486.
  • [64] Minqiang Li and Liang Peng (2011). Empirical likelihood test via estimating equations. Journal of Statistical Planning and Inference 141, 2428–2439.
  • [63] Z. Li, Y. Gong and L. Peng (2011). Empirical likelihood intervals for conditional Value-at-Risk in heteroscedastic regression models. Scandinavian Journal of Statistics 38, 781–787.
  • [62] Liang Peng (2011). Empirical likelihood methods for Gini index. Australian and New Zealand Journal of Statistics 53, 131–139.
  • [61] M. Li, L. Peng and Y. Qi (2011). Reduce computation in profile empirical likelihood method. Canadian Journal of Statistics 39, 370–384. Report with detailed proofs
  • [60] D. Li, L. Peng and Y. Qi (2011). Empirical likelihood confidence intervals for the endpoint of a distribution function. Test 20, 353–366.
  • [59] S. Haug, C. Kluppelberg and L. Peng (2011). Statistical models and methods for dependence in insurance data. Journal of Korean Statistical Society 40, 125–139.
  • [58] N.H. Chan, L. Peng and D. Zhang (2011). Empirical likelihood based confidence intervals for conditional variance in heteroscedastic regression models. Econometric Theory 27, 154–177.
  • [57] L. Peng and Y. Qi (2010). Smoothed jackknife empirical likelihood method for tail copulas. Test 19, 514–536.
  • [56] D. Li, L. Peng and J. Yang (2010). Bias reduction for high quantiles. Journal of Statistical Planning and Inference 140, 2433–2441.
  • [55] Yun Gong, Liang Peng and Yongcheng Qi (2010). Smoothed jackknife empirical likelihood method for ROC curve. Journal of Multivariate Analysis 101, 1520–1531.
  • [54] V. Asimit, D. Li and L. Peng (2010). Pitfalls in using Weibull tailed distributions. Journal of Statistical Planning and Inference 140, 2018–2024.
  • [53] Y. Gong, Z. Li and L. Peng (2010). Empirical likelihood intervals for conditional Value-at-Risk in ARCH/GARCH models. Journal of Time Series Analysis 31, 65–75.
  • [52] N.H. Chan, T. Lee and L. Peng (2010). On nonparametric local inference for density estimation. Computational Statistics and Data Analysis 54, 509–515.
  • [51] H. Liang and L. Peng (2010). Asymptotic normality and Berry-Esseen results for conditional density estimator with censored and dependent data. Journal of Multivariate Analysis 101, 1043–1054.
  • [50] Y. Gong and L. Peng (2010). Coverage accuracy for a mean without third moment. Journal of Statistical Planning and Inference 104, 1082–1088.
  • [49] Liang Peng (2010). A practical way for estimating tail dependence functions. Statistica Sinica 20, 365–378.
  • [48] N.H. Chan, S.X. Chen, L. Peng and C.L. Yu (2009). Empirical likelihood methods based on characteristic functions with applications to Levy processes. Journal of the American Statistical Association 104, 1621–1630.
  • [47] S.X. Chen, L. Peng and Y. Qin (2009). Effects of data dimension on empirical likelihood. Biometrika 96, 711–722.
  • [46] L. Peng and Y. Qi (2009). Maximum likelihood estimation of extreme value index for irregular cases. Journal of Statistical Planning and Inference 139, 3361–3376.
  • [45] L. Peng (2009). A practical way for analyzing heavy tailed data. Canadian Journal of Statistics 37, 235–248.
  • [44] L. Peng and J. Yang (2009). Jackknife method for intermediate quantiles. Journal of Statistical Planning and Inference 139, 2372 – 2381.
  • [43] D. Li and L. Peng (2009). Does Bias Reduction with External Estimator of Second Order Parameter Work for Endpoint? Journal of Statistical Planning and Inference 139, 1937 – 1952.
  • [42] L. Chen, M.Y. Cheng and L. Peng (2009). Conditional variance estimation in heteroscedastic regression model. Journal of Statistical Planning and Inference 139, 236 – 245.
  • [41] J. Chen, L. Peng and Y. Zhao (2009). Empirical likelihood based confidence intervals for copulas. Journal of Multivariate Analysis 100, 137 – 151.
  • [40] N.H. Chan, J. Chen, X. Chen, Y. Fan and L. Peng (2009). Statistical inference for multivariate residual copula of GARCH models. Statistica Sinica 19(1), 53 – 70.
  • [39] L. Peng (2008). Estimating the probability of a rare event via elliptical copulas. North American Actuarial Journal 12(2), 116–128.
  • [38] A.J. Koning and L. Peng (2008). Goodness-of-fit tests for a heavy tailed distribution. Journal of Statistical Planning and Inference 138, 3960 – 3981.
  • [37] C. Kluppelberg, G. Kuhn and L. Peng (2008). Semi-Parametric Models for the Multivariate Tail Dependence Function – the Asymptotically Dependent Case. Scandinavian Journal of Statistics 35, 701-718.
  • [36] L. Peng and Y. Qi (2008). Bootstrap Approximation of Tail Dependence Function. Journal of Multivariate Analysis 99, 1807 – 1824.
  • [35] L. de Haan, C. Neves and L. Peng (2008). Parametric tail copula estimation and model testing. Journal of Multivariate Analysis 99, 1260 – 1275.
  • [34] D. Zhang, M.T. Wells and L. Peng (2008). Nonparametric estimation of the dependence function for a multivariate extreme value distribution. Journal of Multivariate Analysis 99(4), 577 – 588.
  • [33] C. Kluppelberg, G. Kuhn and L. Peng (2007). Estimating the tail dependence of an elliptical distribution. Bernoulli 13(1), 229 – 251.
  • [32] M. Cheng, L. Peng and J.S. Wu (2007). Reducing variance in univariate smoothing. Annals of Statistics 35(2), 522 – 542.
  • [31] L. Peng and Y. Qi (2007). Partial derivatives and confidence intervals of bivariate tail dependence functions. Journal of Statistical Planning and Inference 137, 2089 – 2101.
  • [30] M. Cheng and L. Peng (2007). Variance reduction in multivariate likelihood models. Journal of American Statistical Association 102(477), 293 – 304.
  • [29] Ngai Hang Chan, Shijie Deng, Liang Peng and Zhendong Xia (2007). Interval estimation for the conditional Value-at-Risk based on GARCH models with heavy tailed innovations. Journal of Econometrics 137(2), 556 – 576.
  • [28] L. Peng and Y. Qi (2006). Confidence regions for high quantiles of a heavy tailed distribution. Annals of Statistics 34(4), 1964 – 1986.
  • [27] G.T. Zhou and L. Peng (2006). Optimality condition for selected mapping in OFDM. IEEE Transactions on Signal Processing 54(8), 3159 – 3165.
  • [26] M. Cheng and L. Peng (2006). A simple and efficient improvement of multivariate local linear regression. Journal of Multivariate Analysis 97(7), 1501 – 1524.
  • [25] N.H. Chan, L. Peng and Y. Qi (2006). Quantile inference for near-integrated autoregressive time series with infinite variance. Statistica Sinica 16(1), 15 – 28.
  • [24] L. Peng and Y. Qi (2006). A new calibration method of constructing empirical likelihood-based confidence intervals for the tail index. Australian & New Zealand Journal of Statistics 48(1), 59 – 66.
  • [23] N.H. Chan and L. Peng (2005). Weighted least absolute deviations estimation for an AR(1) process with ARCH(1) errors. Biometrika. 92, 477 – 484.
  • [22] L. Peng and Y. Qi (2004). Estimating the first and second order parameters of a heavy tailed distribution. Australian & New Zealand Journal of Statistics. 46(2), 305 – 312.
  • [21] S. Ling and L. Peng (2004). Hill’s estimator for the tail index of an ARMA model. Journal of Statistical Planning and Inference. 123(2), 279 – 293.
  • [20] Liang Peng and Qiwei Yao (2004). Nonparametric regression under dependent errors with infinite variance. Annals of the Institute of Statistical Mathematics 56(1), 73 – 86.
  • [19] Liang Peng (2004). Bias-corrected estimators for monotone and concave frontier functions. Journal of Statistical Planning and Inference. 119(2), 263 – 275.
  • [18] Liang Peng (2004). Empirical likelihood based confidence interval for the mean of a heavy tailed distribution. Annals of Statistics 32(3), 1192 – 1214.
  • [17] Liang Peng and Xiaohua Zhou (2004). Local linear smoothing of receiver operator characteristic (ROC) curves. Journal of Statistical Planning and Inference 118, 129 – 143.
  • [16] L. Peng and Q. Yao (2003). Least absolute deviations estimation for ARCH and GARCH models. Biometrika 90(4). 967 – 975.
  • [15] G. Claeskens, B. Jing, L. Peng and W. Zhou (2003). Empirical likelihood confidence regions for comparison distributions and ROC curves. The Canadian Journal of Statistics. 31(2), 173 – 190.
  • [14] A. Ferreira, L. de Haan and L. Peng (2003). On optimising the estimation of high quantiles of a probability distribution. Statistics 37(5), 403-434.
  • [13] M.Y. Cheng and L. Peng (2002). Regression modeling for nonparametric estimation of distribution and quantile functions. Statistica Sinica, 12, 1043 – 1060.
  • [12] P. Hall, L. Peng and Q. Yao (2002). Moving-maximum models for extremes of time series. Journal of Statistical Planning and Inference, 103, 51 – 63.
  • [11] P. Hall, L. Peng and Q. Yao (2002). Prediction and nonparametric estimation for time series with heavy tails. Journal of Time Series Analysis 23(3), 313 – 331.
  • [10] P. Hall, L. Peng and N. Tajvidi (2002). Effect of extrapolation on coverage accuracy of prediction intervals computed from Pareto-type data. Annals of Statistics 30(3), 875 – 895.
  • [9] S. Cheng and L. Peng (2001). Confidence intervals for the tail index. Bernoulli 7(5), 751 – 760.
  • [8] P. Hall, L. Peng and C. Rau (2001). Local-likelihood tracking of fault lines and boundaries in spatial problems. Journal of Royal Statistical Society Series B 63(3), 569 – 582.
  • [7] J. Danielsson, L. de Haan, L. Peng and C.G. de Vries (2001). Using a bootstrap method to choose the sample fraction in tail index estimation. Journal of Multivariate Analysis 76, 226 – 248.
  • [6] J. Geluk, L. Peng and C.G. de Vries (2000). Convolutions of heavy tailed random variables and applications to portfolio diversification and MA(1) time series. Advances in Applied Probability 32(4), 1011 – 1026.
  • [5] S. Cheng, L. Peng and Y. Qi (2000). Ergodic behaviour of extreme values. Journal of Australian Mathematical Society (Series A) 68, 170 — 180.
  • [4] P. Hall, L. Peng and N. Tajvidi (1999). On prediction intervals based on predictive likelihood or bootstrap methods. Biometrika 86, 871 — 880.
  • [3] L. de Haan and L. Peng (1999). Exact rates of convergence to a stable law. Journal of the London Mathematical Society 59(2), 1134 — 1152.
  • [2] L. de Haan and L. Peng (1998). Comparison of tail index estimators. Statistica Neerlandica 52(1), 60 — 70.
  • [1] L. de Haan and L. Peng (1997). Rates of convergence for bivariate extremes. Journal of Multivariate Analysis 61(2), 195 — 230.

Links:

Thomas Bowles Symposium 2017: Predictive Analytics and Risk Analytics

Thomas Bowles Symposium 2016: Systemic Risk.Talk1Talk2Talk3Talk4

Society of Actuaries

Casualty Actuarial Society

American Risk and Insurance Association

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