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Jennifer S.K. Chan

Personal Details

First Name:Jennifer
Middle Name:S.K.
Last Name:Chan
Suffix:
RePEc Short-ID:pch754
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Research output

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Jump to: Working papers Articles

Working papers

  1. Steven Y. K. Wong & Jennifer Chan & Lamiae Azizi & Richard Y. D. Xu, 2020. "Time-varying neural network for stock return prediction," Papers 2003.02515, arXiv.org, revised Jan 2021.
  2. Nick James & Max Menzies & Jennifer Chan, 2020. "Semi-metric portfolio optimization: a new algorithm reducing simultaneous asset shocks," Papers 2001.09404, arXiv.org, revised Mar 2023.
  3. Nick James & Max Menzies & Jennifer Chan, 2019. "Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-19," Papers 1912.06193, arXiv.org, revised Nov 2020.
  4. Alice X. D. Dong & Jennifer S. K. Chan & Gareth W. Peters, 2014. "Risk Margin Quantile Function Via Parametric and Non-Parametric Bayesian Quantile Regression," Papers 1402.2492, arXiv.org.
  5. Jennifer Chan & Boris Choy & Udi Makov, 2007. "Robust Bayesian Analysis of Loss Reserves Data Using the Generalized-t Distribution," Research Paper Series 196, Quantitative Finance Research Centre, University of Technology, Sydney.

Articles

  1. James, Nick & Menzies, Max & Chan, Jennifer, 2021. "Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
  2. Phillip, Andrew & Chan, Jennifer & Peiris, Shelton, 2020. "On generalized bivariate student-t Gegenbauer long memory stochastic volatility models with leverage: Bayesian forecasting of cryptocurrencies with a focus on Bitcoin," Econometrics and Statistics, Elsevier, vol. 16(C), pages 69-90.
  3. Yan, Hongxuan & Peters, Gareth W. & Chan, Jennifer S.K., 2020. "Multivariate Long-Memory Cohort Mortality Models," ASTIN Bulletin, Cambridge University Press, vol. 50(1), pages 223-263, January.
  4. Tan, Shay-Kee & Chan, Jennifer So-Kuen & Ng, Kok-Haur, 2020. "On the speculative nature of cryptocurrencies: A study on Garman and Klass volatility measure," Finance Research Letters, Elsevier, vol. 32(C).
  5. Chan Jennifer So Kuen & Ng Kok-Haur & Nitithumbundit Thanakorn & Peiris Shelton, 2019. "Efficient estimation of financial risk by regressing the quantiles of parametric distributions: An application to CARR models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(2), pages 1-22, April.
  6. Phillip, Andrew & Chan, Jennifer & Peiris, Shelton, 2019. "On long memory effects in the volatility measure of Cryptocurrencies," Finance Research Letters, Elsevier, vol. 28(C), pages 95-100.
  7. Chan, Jennifer So-Kuen & Ng, Kok-Haur & Ragell, Rachel, 2019. "Bayesian return forecasts using realised range and asymmetric CARR model with various distribution assumptions," International Review of Economics & Finance, Elsevier, vol. 61(C), pages 188-212.
  8. Tan, Shay-Kee & Ng, Kok-Haur & Chan, Jennifer So-Kuen & Mohamed, Ibrahim, 2019. "Quantile range-based volatility measure for modelling and forecasting volatility using high frequency data," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 537-551.
  9. R. P. Yatigammana & J. S. K. Chan & R. H. Gerlach, 2019. "Forecasting trade durations via ACD models with mixture distributions," Quantitative Finance, Taylor & Francis Journals, vol. 19(12), pages 2051-2067, December.
  10. Chan, J.S.K. & Choy, S.T.B. & Makov, U.E. & Landsman, Z., 2018. "Modelling Insurance Losses Using Contaminated Generalised Beta Type-Ii Distribution," ASTIN Bulletin, Cambridge University Press, vol. 48(2), pages 871-904, May.
  11. Phillip, Andrew & Chan, Jennifer S.K. & Peiris, Shelton, 2018. "A new look at Cryptocurrencies," Economics Letters, Elsevier, vol. 163(C), pages 6-9.
  12. Ng, Kok Haur & Peiris, Shelton & Chan, Jennifer So-kuen & Allen, David & Ng, Kooi Huat, 2017. "Efficient modelling and forecasting with range based volatility models and its application," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 448-460.
  13. S.T. Boris Choy & Jennifer S.K. Chan & Udi E. Makov, 2016. "Robust Bayesian analysis of loss reserving data using scale mixtures distributions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(3), pages 396-411, March.
  14. Dong, Alice X.D. & Chan, Jennifer S.K. & Peters, Gareth W., 2015. "Risk Margin Quantile Function Via Parametric And Non-Parametric Bayesian Approaches," ASTIN Bulletin, Cambridge University Press, vol. 45(3), pages 503-550, September.
  15. Chan, Jennifer So Kuen & Wan, Wai Yin, 2014. "Multivariate generalized Poisson geometric process model with scale mixtures of normal distributions," Journal of Multivariate Analysis, Elsevier, vol. 127(C), pages 72-87.
  16. J.S.K. Chan & W.Y. Wan & P.L.H. Yu, 2014. "A Poisson geometric process approach for predicting drop-out and committed first-time blood donors," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(7), pages 1486-1503, July.
  17. Dong, A.X.D. & Chan, J.S.K., 2013. "Bayesian analysis of loss reserving using dynamic models with generalized beta distribution," Insurance: Mathematics and Economics, Elsevier, vol. 53(2), pages 355-365.
  18. Chan, J.S.K. & Lam, C.P.Y. & Yu, P.L.H. & Choy, S.T.B. & Chen, C.W.S., 2012. "A Bayesian conditional autoregressive geometric process model for range data," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3006-3019.
  19. Chen, Cathy W.S. & Chan, Jennifer S.K. & So, Mike K.P. & Lee, Kevin K.M., 2011. "Classification in segmented regression problems," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2276-2287, July.
  20. Wang, Joanna J.J. & Chan, Jennifer S.K. & Choy, S.T. Boris, 2011. "Stochastic volatility models with leverage and heavy-tailed distributions: A Bayesian approach using scale mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 852-862, January.
  21. Jennifer Chan & Wai Wan, 2011. "Bayesian approach to analysing longitudinal bivariate binary data with informative dropout," Computational Statistics, Springer, vol. 26(1), pages 121-144, March.
  22. Wan, Wai-Yin & Chan, Jennifer So-Kuen, 2011. "Bayesian analysis of robust Poisson geometric process model using heavy-tailed distributions," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 687-702, January.
  23. Jennifer Chan & Doris Leung, 2010. "Binary geometric process model for the modeling of longitudinal binary data with trend," Computational Statistics, Springer, vol. 25(3), pages 505-536, September.
  24. Chan, Jennifer S.K. & Leung, Doris Y.P. & Boris Choy, S.T. & Wan, Wai Y., 2009. "Nonignorable dropout models for longitudinal binary data with random effects: An application of Monte Carlo approximation through the Gibbs output," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4530-4545, October.
  25. Chan, Jennifer S.K. & Boris Choy, S.T. & Makov, Udi E., 2008. "Robust Bayesian Analysis of Loss Reserves Data Using the Generalized-t Distribution," ASTIN Bulletin, Cambridge University Press, vol. 38(1), pages 207-230, May.
  26. Jennifer S. K. Chan & S. T. Boris Choy & Anna B. W. Lee, 2007. "Bayesian analysis of constant elasticity of variance models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 23(1), pages 83-96, January.
  27. Yu, Philip L.H. & Chan, Jennifer S.K. & Fung, Wing K., 2006. "Statistical Exploration from SARS," The American Statistician, American Statistical Association, vol. 60, pages 81-91, February.
  28. Chan, Jennifer S.K. & Kuk, Anthony Y.C. & Yam, Carrie H.K., 2005. "Monte Carlo approximation through Gibbs output in generalized linear mixed models," Journal of Multivariate Analysis, Elsevier, vol. 94(2), pages 300-312, June.
  29. Chan, Jennifer S. K. & Lam, Yeh & Leung, Doris Y. P., 2004. "Statistical inference for geometric processes with gamma distributions," Computational Statistics & Data Analysis, Elsevier, vol. 47(3), pages 565-581, October.
  30. Lam Yeh & So Kuen Chan, 1998. "Statistical inference for geometric processes with lognormal distribution," Computational Statistics & Data Analysis, Elsevier, vol. 27(1), pages 99-112, March.

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 5 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (3) 2007-06-30 2014-02-15 2020-03-23
  2. NEP-RMG: Risk Management (3) 2007-06-30 2014-02-15 2020-02-10
  3. NEP-FMK: Financial Markets (2) 2020-01-13 2020-03-23
  4. NEP-BIG: Big Data (1) 2020-03-23
  5. NEP-CMP: Computational Economics (1) 2020-03-23
  6. NEP-ETS: Econometric Time Series (1) 2007-06-30
  7. NEP-FOR: Forecasting (1) 2007-06-30
  8. NEP-PAY: Payment Systems and Financial Technology (1) 2020-01-13
  9. NEP-SOG: Sociology of Economics (1) 2014-02-15

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