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 2017:

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:

  • Extreme value theory in finance and insurance
  • Nonparametric statistics
  • Financial time series
  • Copula and tail copula in risk management
  • Continuous-time stochastic processes in finance


Some papers:

  • [102] F. Wang, L. Peng, Y. Qi and M. Xu (2017). Maximum penalized likelihood estimation for the endpoint and exponent of a distribution. Statistica Sinica. To appear.
  • [101] Yi He, Yanxi Hou, Liang Peng and Jiliang Sheng (2017). Statistical inference for a relative risk measure. Journal of Business and Economic Statistics. To appear.
  • [100] Rongmao Zhang, Chenxue Li and Liang Peng (2017). 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)
  • [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] Xuan Leng and Liang Peng (2016). Inference pitfalls in Lee-Carter model for forecasting mortality. Insurance: Mathematics and Economics 70, 58–65.
  • [95] Xing Wang and Liang 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] Ngai-Hang Chan, Liang Peng and Dabao 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] Deyuan Li and Liang 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] Lu-Hung Chen, Ming-Yen Cheng and Liang 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] Alex J. Koning and Liang Peng (2008). Goodness-of-fit tests for a heavy tailed distribution. Journal of Statistical Planning and Inference 138, 3960 – 3981.
  • [37] Claudia Kluppelberg, Gabriel Kuhn and Liang Peng (2008). Semi-Parametric Models for the Multivariate Tail Dependence Function – the Asymptotically Dependent Case. Scandinavian Journal of Statistics 35, 701-718.
  • [36] Liang Peng and Yongcheng 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] Dabao Zhang, Martin T. Wells and Liang Peng (2008). Nonparametric estimation of the dependence function for a multivariate extreme value distribution. Journal of Multivariate Analysis 99(4), 577 – 588.
  • [33] Claudia Kluppelberg, Gabriel Kuhn and Liang 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] Liang Peng and Yongcheng 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] Ngai Hang Chan and Liang Peng (2005). Weighted least absolute deviations estimation for an AR(1) process with ARCH(1) errors. Biometrika. 92, 477 – 484.
  • [22] Liang Peng and Yongcheng 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] Shiqing Ling and Liang 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] Ming-Yen Cheng and Liang Peng (2002). Regression modeling for nonparametric estimation of distribution and quantile functions. Statistica Sinica, 12, 1043 – 1060.
  • [12] Peter Hall, Liang Peng and Qiwei Yao (2002). Moving-maximum models for extremes of time series. Journal of Statistical Planning and Inference, 103, 51 – 63.
  • [11] Peter Hall, Liang Peng and Qiwei Yao (2002). Prediction and nonparametric estimation for time series with heavy tails. Journal of Time Series Analysis 23(3), 313 – 331.
  • [10] Peter Hall, Liang Peng and Nader Tajvidi (2002). Effect of extrapolation on coverage accuracy of prediction intervals computed from Pareto-type data. Annals of Statistics 30(3), 875 – 895.
  • [9] Shihong Cheng and Liang Peng (2001). Confidence intervals for the tail index. Bernoulli 7(5), 751 – 760.
  • [8] Peter Hall, Liang Peng and Christian 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] Jon Danielsson, Laurens de Haan, Liang Peng and Capser 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] Jaap Geluk, Liang Peng and Casper 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] Shihong Cheng, Liang Peng and Yongcheng Qi (2000). Ergodic behaviour of extreme values. Journal of Australian Mathematical Society (Series A) 68, 170 — 180.
  • [4] Peter Hall, Liang Peng and Nader Tajvidi (1999). On prediction intervals based on predictive likelihood or bootstrap methods. Biometrika 86, 871 — 880.
  • [3] Laurens de Haan and Liang Peng (1999). Exact rates of convergence to a stable law. Journal of the London Mathematical Society 59(2), 1134 — 1152.
  • [2] Laurens de Haan and Liang Peng (1998). Comparison of tail index estimators. Statistica Neerlandica 52(1), 60 — 70.
  • [1] Laurens de Haan and Liang 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

Statistical Science Web

R Homepage

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