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Jennifer S.K. Chan

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. 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.

    Cited by:

    1. James, Nick & Menzies, Max, 2023. "Collective infectivity of the pandemic over time and association with vaccine coverage and economic development," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    2. Nick James & Max Menzies, 2023. "An exploration of the mathematical structure and behavioural biases of 21st century financial crises," Papers 2307.15402, arXiv.org, revised Sep 2023.
    3. James, Nick & Menzies, Max, 2023. "An exploration of the mathematical structure and behavioural biases of 21st century financial crises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    4. Stanis{l}aw Dro.zd.z & Jaros{l}aw Kwapie'n & Marcin Wk{a}torek, 2023. "What is mature and what is still emerging in the cryptocurrency market?," Papers 2305.05751, arXiv.org.
    5. James, Nick & Menzies, Max & Chin, Kevin, 2022. "Economic state classification and portfolio optimisation with application to stagflationary environments," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    6. Nick James & Max Menzies, 2023. "Collective dynamics, diversification and optimal portfolio construction for cryptocurrencies," Papers 2304.08902, arXiv.org, revised Jun 2023.

  2. 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.

    Cited by:

    1. James, Nick & Menzies, Max, 2023. "Collective infectivity of the pandemic over time and association with vaccine coverage and economic development," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    2. Nick James & Max Menzies & Kevin Chin, 2022. "Economic state classification and portfolio optimisation with application to stagflationary environments," Papers 2203.15911, arXiv.org, revised Sep 2022.
    3. James, Nick & Menzies, Max & Gottwald, Georg A., 2022. "On financial market correlation structures and diversification benefits across and within equity sectors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    4. Balcilar, Mehmet & Ozdemir, Huseyin & Agan, Busra, 2022. "Effects of COVID-19 on cryptocurrency and emerging market connectedness: Empirical evidence from quantile, frequency, and lasso networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    5. Stanis{l}aw Dro.zd.z & Jaros{l}aw Kwapie'n & Marcin Wk{a}torek, 2023. "What is mature and what is still emerging in the cryptocurrency market?," Papers 2305.05751, arXiv.org.
    6. Dora Almeida & Andreia Dionísio & Paulo Ferreira & Isabel Vieira, 2023. "Impact of the COVID-19 Pandemic on Cryptocurrency Markets: A DCCA Analysis," FinTech, MDPI, vol. 2(2), pages 1-17, May.
    7. Jin, Lifu & Zheng, Bo & Ma, Jiahao & Zhang, Jiu & Xiong, Long & Jiang, Xiongfei & Li, Jiangcheng, 2022. "Empirical study and model simulation of global stock market dynamics during COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    8. Dmitry V. Boguslavsky & Natalia P. Sharova & Konstantin S. Sharov, 2021. "Cryptocurrency as Epidemiologically Safe Means of Transactions: Diminishing Risk of SARS-CoV-2 Spread," Mathematics, MDPI, vol. 9(24), pages 1-19, December.
    9. Serdar Neslihanoglu, 2021. "Linearity extensions of the market model: a case of the top 10 cryptocurrency prices during the pre-COVID-19 and COVID-19 periods," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-27, December.
    10. James, Nick & Menzies, Max & Chin, Kevin, 2022. "Economic state classification and portfolio optimisation with application to stagflationary environments," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    11. Nick James & Kevin Chin, 2021. "On the systemic nature of global inflation, its association with equity markets and financial portfolio implications," Papers 2111.11022, arXiv.org, revised Jan 2022.
    12. Fung, Kennard & Jeong, Jiin & Pereira, Javier, 2022. "More to cryptos than bitcoin: A GARCH modelling of heterogeneous cryptocurrencies," Finance Research Letters, Elsevier, vol. 47(PA).
    13. Jaros{l}aw Kwapie'n & Marcin Wk{a}torek & Stanis{l}aw Dro.zd.z, 2021. "Cryptocurrency Market Consolidation in 2020--2021," Papers 2112.06552, arXiv.org.
    14. Nuray Tosunoğlu & Hilal Abacı & Gizem Ateş & Neslihan Saygılı Akkaya, 2023. "Artificial neural network analysis of the day of the week anomaly in cryptocurrencies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-24, December.
    15. Nick James, 2021. "Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19," Papers 2101.00576, arXiv.org, revised Feb 2021.
    16. James, Nick & Chin, Kevin, 2022. "On the systemic nature of global inflation, its association with equity markets and financial portfolio implications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    17. Marcin Wątorek & Jarosław Kwapień & Stanisław Drożdż, 2022. "Multifractal Cross-Correlations of Bitcoin and Ether Trading Characteristics in the Post-COVID-19 Time," Future Internet, MDPI, vol. 14(7), pages 1-15, July.
    18. Bejaoui, Azza & Mgadmi, Nidhal & Moussa, Wajdi, 2022. "On the relationship between Bitcoin and other assets during the outbreak of coronavirus: Evidence from fractional cointegration analysis," Resources Policy, Elsevier, vol. 77(C).
    19. Nick James & Max Menzies, 2021. "Efficiency of communities and financial markets during the 2020 pandemic," Papers 2104.02318, arXiv.org, revised Jul 2021.
    20. Nick James & Max Menzies, 2021. "Collective correlations, dynamics, and behavioural inconsistencies of the cryptocurrency market over time," Papers 2107.13926, arXiv.org, revised Dec 2021.
    21. Zhao, Jing & Zhang, Qin, 2021. "The effect of contract methods on the lead time of a two-level photovoltaic supply chain: revenue-sharing vs. cost-sharing," Energy, Elsevier, vol. 231(C).
    22. Abubakr Naeem, Muhammad & Iqbal, Najaf & Lucey, Brian M. & Karim, Sitara, 2022. "Good versus bad information transmission in the cryptocurrency market: Evidence from high-frequency data," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    23. Akhtaruzzaman, Md & Boubaker, Sabri & Umar, Zaghum, 2022. "COVID–19 media coverage and ESG leader indices," Finance Research Letters, Elsevier, vol. 45(C).
    24. James, Nick, 2021. "Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    25. Marcin Wk{a}torek & Jaros{l}aw Kwapie'n & Stanis{l}aw Dro.zd.z, 2023. "Cryptocurrencies Are Becoming Part of the World Global Financial Market," Papers 2303.00495, arXiv.org.
    26. Demiralay, Sercan & Golitsis, Petros, 2021. "On the dynamic equicorrelations in cryptocurrency market," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 524-533.
    27. Marcin Wk{a}torek & Jaros{l}aw Kwapie'n & Stanis{l}aw Dro.zd.z, 2022. "Multifractal cross-correlations of bitcoin and ether trading characteristics in the post-COVID-19 time," Papers 2208.01445, arXiv.org.
    28. Nick James & Max Menzies, 2023. "Collective dynamics, diversification and optimal portfolio construction for cryptocurrencies," Papers 2304.08902, arXiv.org, revised Jun 2023.
    29. Aiman Hairudin & Imtiaz Mohammad Sifat & Azhar Mohamad & Yusniliyana Yusof, 2022. "Cryptocurrencies: A survey on acceptance, governance and market dynamics," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4633-4659, October.

  3. 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.

    Cited by:

    1. Boratyńska Agata, 2021. "Robust Bayesian insurance premium in a collective risk model with distorted priors under the generalised Bregman loss," Statistics in Transition New Series, Polish Statistical Association, vol. 22(3), pages 123-140, September.
    2. Boratyńska, Agata, 2017. "Robust Bayesian estimation and prediction of reserves in exponential model with quadratic variance function," Insurance: Mathematics and Economics, Elsevier, vol. 76(C), pages 135-140.
    3. 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.
    4. 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.
    5. 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.
    6. Gareth W. Peters & Wilson Ye Chen & Richard H. Gerlach, 2016. "Estimating Quantile Families of Loss Distributions for Non-Life Insurance Modelling via L-Moments," Risks, MDPI, vol. 4(2), pages 1-41, May.
    7. Benjamin Avanzi & Mark Lavender & Greg Taylor & Bernard Wong, 2022. "Detection and treatment of outliers for multivariate robust loss reserving," Papers 2203.03874, arXiv.org, revised Jun 2023.
    8. Gareth W. Peters & Wilson Y. Chen & Richard H. Gerlach, 2016. "Estimating Quantile Families of Loss Distributions for Non-Life Insurance Modelling via L-moments," Papers 1603.01041, arXiv.org.
    9. Chan Jennifer So Kuen & Nitithumbundit Thanakorn & Peiris Shelton & Ng Kok-Haur, 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.
    10. Sánchez-Sánchez, M. & Sordo, M.A. & Suárez-Llorens, A. & Gómez-Déniz, E., 2019. "Deriving Robust Bayesian Premiums Under Bands Of Prior Distributions With Applications," ASTIN Bulletin, Cambridge University Press, vol. 49(1), pages 147-168, January.

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).
    See citations under working paper version above.
  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.

    Cited by:

    1. Tak Kuen Siu, 2023. "Bayesian nonlinear expectation for time series modelling and its application to Bitcoin," Empirical Economics, Springer, vol. 64(1), pages 505-537, January.
    2. Constandina Koki & Stefanos Leonardos & Georgios Piliouras, 2019. "A Peek into the Unobservable: Hidden States and Bayesian Inference for the Bitcoin and Ether Price Series," Papers 1909.10957, arXiv.org, revised Jul 2021.
    3. Nitithumbundit, Thanakorn & Chan, Jennifer S.K., 2022. "Covid-19 impact on Cryptocurrencies market using Multivariate Time Series Models," The Quarterly Review of Economics and Finance, Elsevier, vol. 86(C), pages 365-375.

  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.

    Cited by:

    1. Ling Wang & Mei Choi Chiu & Hoi Ying Wong, 2021. "Time-consistent mean-variance reinsurance-investment problem with long-range dependent mortality rate," Papers 2112.06602, arXiv.org.
    2. Ioannis Chalkiadakis & Hongxuan Yan & Gareth W Peters & Pavel V Shevchenko, 2021. "Infection rate models for COVID-19: Model risk and public health news sentiment exposure adjustments," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-39, June.
    3. Wang, Ling & Chiu, Mei Choi & Wong, Hoi Ying, 2021. "Volterra mortality model: Actuarial valuation and risk management with long-range dependence," Insurance: Mathematics and Economics, Elsevier, vol. 96(C), pages 1-14.
    4. Ling Wang & Mei Choi Chiu & Hoi Ying Wong, 2020. "Volterra mortality model: Actuarial valuation and risk management with long-range dependence," Papers 2009.09572, arXiv.org.

  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).

    Cited by:

    1. Alexandre Aidov & Olesya Lobanova, 2021. "Volatility and Depth in Commodity and FX Futures Markets," JRFM, MDPI, vol. 14(11), pages 1-16, November.
    2. Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021. "Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
    3. Riccardo De Blasis, 2023. "Weighted-indexed semi-Markov model: calibration and application to financial modeling," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-16, December.
    4. Ahmed, Walid M.A., 2021. "Stock market reactions to upside and downside volatility of Bitcoin: A quantile analysis," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    5. Alexander Guzmán & Christian Pinto-Gutiérrez & María-Andrea Trujillo, 2021. "Trading Cryptocurrencies as a Pandemic Pastime: COVID-19 Lockdowns and Bitcoin Volume," Mathematics, MDPI, vol. 9(15), pages 1-15, July.
    6. Assaf, Ata & Mokni, Khaled & Yousaf, Imran & Bhandari, Avishek, 2023. "Long memory in the high frequency cryptocurrency markets using fractal connectivity analysis: The impact of COVID-19," Research in International Business and Finance, Elsevier, vol. 64(C).
    7. Lo, Yuen & Medda, Francesca, 2020. "Uniswap and the rise of the decentralized exchange," MPRA Paper 103925, University Library of Munich, Germany.
    8. Jinxin Cui & Aktham Maghyereh, 2022. "Time–frequency co-movement and risk connectedness among cryptocurrencies: new evidence from the higher-order moments before and during the COVID-19 pandemic," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-56, December.
    9. Filip Hampl & Lucie Gyönyörová, 2021. "Can Fiat‐backed Stablecoins Be Considered Cash or Cash Equivalents Under International Financial Reporting Standards Rules?," Australian Accounting Review, CPA Australia, vol. 31(3), pages 233-255, September.
    10. 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).
    11. Christophe Schinckus & Canh Phuc Nguyen & Felicia Hui Ling Chong, 2023. "Between financial and algorithmic dynamics of cryptocurrencies: An exploratory study," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 3055-3070, July.
    12. Adrian Millea, 2021. "Deep Reinforcement Learning for Trading—A Critical Survey," Data, MDPI, vol. 6(11), pages 1-25, November.
    13. Wang, Yang & Xiuping, Sui & Zhang, Qi, 2021. "Can fintech improve the efficiency of commercial banks? —An analysis based on big data," Research in International Business and Finance, Elsevier, vol. 55(C).
    14. Qihang Xue & Caiquan Bai & Weiwei Xiao, 2022. "Fintech and corporate green technology innovation: Impacts and mechanisms," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(8), pages 3898-3914, December.
    15. Huynh, Nhan & Phan, Hoa, 2023. "Emotions in the crypto market: Do photos really speak?," Finance Research Letters, Elsevier, vol. 55(PB).

  5. Chan Jennifer So Kuen & Nitithumbundit Thanakorn & Peiris Shelton & Ng Kok-Haur, 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.

    Cited by:

    1. Wu, Xinyu & Hou, Xinmeng, 2020. "Forecasting volatility with component conditional autoregressive range model," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).

  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.

    Cited by:

    1. Tiwari, Aviral Kumar & Umar, Zaghum & Alqahtani, Faisal, 2021. "Existence of long memory in crude oil and petroleum products: Generalised Hurst exponent approach," Research in International Business and Finance, Elsevier, vol. 57(C).
    2. Dante Miller & Jong-Min Kim, 2021. "Univariate and Multivariate Machine Learning Forecasting Models on the Price Returns of Cryptocurrencies," JRFM, MDPI, vol. 14(10), pages 1-10, October.
    3. Assaf, Ata & Bhandari, Avishek & Charif, Husni & Demir, Ender, 2022. "Multivariate long memory structure in the cryptocurrency market: The impact of COVID-19," International Review of Financial Analysis, Elsevier, vol. 82(C).
    4. Ata Assaf & Luis Alberiko Gil-Alana & Khaled Mokni, 2022. "True or spurious long memory in the cryptocurrency markets: evidence from a multivariate test and other Whittle estimation methods," Empirical Economics, Springer, vol. 63(3), pages 1543-1570, September.
    5. Julien Chevallier & Dominique Guégan & Stéphane Goutte, 2021. "Is It Possible to Forecast the Price of Bitcoin?," Forecasting, MDPI, vol. 3(2), pages 1-44, May.
    6. Tetsuya Takaishi & Takanori Adachi, 2019. "Market efficiency, liquidity, and multifractality of Bitcoin: A dynamic study," Papers 1902.09253, arXiv.org.
    7. Stefano Martinazzi & Daniele Regoli & Andrea Flori, 2020. "A Tale of Two Layers: The Mutual Relationship between Bitcoin and Lightning Network," Risks, MDPI, vol. 8(4), pages 1-18, December.
    8. Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021. "Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
    9. Leonardo Ieracitano Vieira & Márcio Poletti Laurini, 2023. "Time-varying higher moments in Bitcoin," Digital Finance, Springer, vol. 5(2), pages 231-260, June.
    10. Liebi, Luca J., 2022. "Is there a value premium in cryptoasset markets?," Economic Modelling, Elsevier, vol. 109(C).
    11. Lahmiri, Salim & Bekiros, Stelios, 2021. "The effect of COVID-19 on long memory in returns and volatility of cryptocurrency and stock markets," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    12. Kaya, Orçun & Mostowfi, Mehdi, 2022. "Low-volatility strategies for highly liquid cryptocurrencies," Finance Research Letters, Elsevier, vol. 46(PB).
    13. Walid Mensi & Mobeen Ur Rehman & Muhammad Shafiullah & Khamis Hamed Al-Yahyaee & Ahmet Sensoy, 2021. "High frequency multiscale relationships among major cryptocurrencies: portfolio management implications," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-21, December.
    14. Klender Cortez & Martha del Pilar Rodríguez-García & Samuel Mongrut, 2020. "Exchange Market Liquidity Prediction with the K-Nearest Neighbor Approach: Crypto vs. Fiat Currencies," Mathematics, MDPI, vol. 9(1), pages 1-15, December.
    15. Assaf, Ata & Mokni, Khaled & Yousaf, Imran & Bhandari, Avishek, 2023. "Long memory in the high frequency cryptocurrency markets using fractal connectivity analysis: The impact of COVID-19," Research in International Business and Finance, Elsevier, vol. 64(C).
    16. Bariviera, Aurelio F., 2021. "One model is not enough: Heterogeneity in cryptocurrencies’ multifractal profiles," Finance Research Letters, Elsevier, vol. 39(C).
    17. Lahmiri, Salim & Bekiros, Stelios, 2019. "Decomposing the persistence structure of Islamic and green crypto-currencies with nonlinear stepwise filtering," Chaos, Solitons & Fractals, Elsevier, vol. 127(C), pages 334-341.
    18. Aloui, Chaker & Hamida, Hela ben & Yarovaya, Larisa, 2021. "Are Islamic gold-backed cryptocurrencies different?," Finance Research Letters, Elsevier, vol. 39(C).
    19. Tetsuya Takaishi & Takanori Adachi, 2020. "Market Efficiency, Liquidity, and Multifractality of Bitcoin: A Dynamic Study," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 27(1), pages 145-154, March.
    20. Takaishi, Tetsuya, 2020. "Rough volatility of Bitcoin," Finance Research Letters, Elsevier, vol. 32(C).
    21. Leandro Maciel, 2021. "Cryptocurrencies value‐at‐risk and expected shortfall: Do regime‐switching volatility models improve forecasting?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4840-4855, July.
    22. 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).
    23. Dooruj Rambaccussing & Murat Mazibas, 2020. "True versus Spurious Long Memory in Cryptocurrencies," JRFM, MDPI, vol. 13(9), pages 1-11, August.
    24. Dias, Ishanka K. & Fernando, J.M. Ruwani & Fernando, P. Narada D., 2022. "Does investor sentiment predict bitcoin return and volatility? A quantile regression approach," International Review of Financial Analysis, Elsevier, vol. 84(C).
    25. Lepomäki, Laura & Kanniainen, Juho & Hansen, Henri, 2021. "Retaliation in Bitcoin networks," Economics Letters, Elsevier, vol. 203(C).
    26. Flori, Andrea, 2019. "News and subjective beliefs: A Bayesian approach to Bitcoin investments," Research in International Business and Finance, Elsevier, vol. 50(C), pages 336-356.
    27. Kerolly Kedma Felix do Nascimento & Fábio Sandro dos Santos & Jader Silva Jale & Silvio Fernando Alves Xavier Júnior & Tiago A. E. Ferreira, 2023. "Extracting Rules via Markov Chains for Cryptocurrencies Returns Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 61(3), pages 1095-1114, March.
    28. Jong-Min Kim & Chulhee Jun & Junyoup Lee, 2021. "Forecasting the Volatility of the Cryptocurrency Market by GARCH and Stochastic Volatility," Mathematics, MDPI, vol. 9(14), pages 1-16, July.
    29. Nikolaos A. Kyriazis, 2019. "A Survey on Efficiency and Profitable Trading Opportunities in Cryptocurrency Markets," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    30. Tetsuya Takaishi, 2021. "Time-varying properties of asymmetric volatility and multifractality in Bitcoin," Papers 2102.07425, arXiv.org.
    31. Nitithumbundit, Thanakorn & Chan, Jennifer S.K., 2022. "Covid-19 impact on Cryptocurrencies market using Multivariate Time Series Models," The Quarterly Review of Economics and Finance, Elsevier, vol. 86(C), pages 365-375.
    32. Onur Özdemir, 2022. "Cue the volatility spillover in the cryptocurrency markets during the COVID-19 pandemic: evidence from DCC-GARCH and wavelet analysis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-38, December.
    33. Andrea Flori, 2019. "Cryptocurrencies In Finance: Review And Applications," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(05), pages 1-22, August.

  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.

    Cited by:

    1. Shay Kee Tan & Kok Haur Ng & Jennifer So-Kuen Chan, 2022. "Predicting Returns, Volatilities and Correlations of Stock Indices Using Multivariate Conditional Autoregressive Range and Return Models," Mathematics, MDPI, vol. 11(1), pages 1-24, December.
    2. Chen, Wang & Ma, Feng & Wei, Yu & Liu, Jing, 2020. "Forecasting oil price volatility using high-frequency data: New evidence," International Review of Economics & Finance, Elsevier, vol. 66(C), pages 1-12.
    3. Wu, Xinyu & Yin, Xuebao & Umar, Zaghum & Iqbal, Najaf, 2023. "Volatility forecasting in the Bitcoin market: A new proposed measure based on the VS-ACARR approach," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    4. Wu, Xinyu & Xie, Haibin & Zhang, Huanming, 2022. "Time-varying risk aversion and renminbi exchange rate volatility: Evidence from CARR-MIDAS model," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).

  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.

    Cited by:

    1. Shay Kee Tan & Kok Haur Ng & Jennifer So-Kuen Chan, 2022. "Predicting Returns, Volatilities and Correlations of Stock Indices Using Multivariate Conditional Autoregressive Range and Return Models," Mathematics, MDPI, vol. 11(1), pages 1-24, December.
    2. Zheng, Chengli & Su, Kuangxi & Yao, Yinhong, 2021. "Hedging futures performance with denoising and noise-assisted strategies," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    3. Yaojie Zhang & Mengxi He & Danyan Wen & Yudong Wang, 2022. "Forecasting Bitcoin volatility: A new insight from the threshold regression model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 633-652, April.
    4. Zhang, Li & Wang, Lu & Peng, Lijuan & Luo, Keyu, 2023. "Measuring the response of clean energy stock price volatility to extreme shocks," Renewable Energy, Elsevier, vol. 206(C), pages 1289-1300.
    5. Wu, Xinyu & Hou, Xinmeng, 2020. "Forecasting volatility with component conditional autoregressive range model," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    6. Xiafei Li & Dongxin Li & Xuhui Zhang & Guiwu Wei & Lan Bai & Yu Wei, 2021. "Forecasting regular and extreme gold price volatility: The roles of asymmetry, extreme event, and jump," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1501-1523, December.
    7. Usha Rekha Chinthapalli, 2021. "A Comparative Analysis on Probability of Volatility Clusters on Cryptocurrencies, and FOREX Currencies," JRFM, MDPI, vol. 14(7), pages 1-23, July.

  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.

    Cited by:

    1. Yiing Fei Tan & Kok Haur Ng & You Beng Koh & Shelton Peiris, 2022. "Modelling Trade Durations Using Dynamic Logarithmic Component ACD Model with Extended Generalised Inverse Gaussian Distribution," Mathematics, MDPI, vol. 10(10), pages 1-20, May.
    2. Lin, Edward M.H. & Sun, Edward W. & Yu, Min-Teh, 2020. "Behavioral data-driven analysis with Bayesian method for risk management of financial services," International Journal of Production Economics, Elsevier, vol. 228(C).
    3. Li, Zhicheng & Chen, Xinyun & Xing, Haipeng, 2023. "A multifactor regime-switching model for inter-trade durations in the high-frequency limit order market," Economic Modelling, Elsevier, vol. 118(C).
    4. Yong Shi & Wei Dai & Wen Long & Bo Li, 2021. "Improved ACD-based financial trade durations prediction leveraging LSTM networks and Attention Mechanism," Papers 2101.02736, arXiv.org.

  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.

    Cited by:

    1. Shi, Yue & Punzo, Antonio & Otneim, Håkon & Maruotti, Antonello, 2023. "Hidden semi-Markov models for rainfall-related insurance claims," Discussion Papers 2023/17, Norwegian School of Economics, Department of Business and Management Science.

  11. Phillip, Andrew & Chan, Jennifer S.K. & Peiris, Shelton, 2018. "A new look at Cryptocurrencies," Economics Letters, Elsevier, vol. 163(C), pages 6-9.

    Cited by:

    1. Saha, Kunal, 2018. "An investigation into the dependence structure of major cryptocurrencies," EconStor Preprints 181878, ZBW - Leibniz Information Centre for Economics.
    2. Nguyen, Linh Hoang & Chevapatrakul, Thanaset & Yao, Kai, 2020. "Investigating tail-risk dependence in the cryptocurrency markets: A LASSO quantile regression approach," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 333-355.
    3. Urquhart, Andrew, 2018. "What causes the attention of Bitcoin?," Economics Letters, Elsevier, vol. 166(C), pages 40-44.
    4. Christie Smith & Aaron Kumar, 2018. "Crypto‐Currencies – An Introduction To Not‐So‐Funny Moneys," Journal of Economic Surveys, Wiley Blackwell, vol. 32(5), pages 1531-1559, December.
    5. Mensi, Walid & Rehman, Mobeen Ur & Al-Yahyaee, Khamis Hamed & Al-Jarrah, Idries Mohammad Wanas & Kang, Sang Hoon, 2019. "Time frequency analysis of the commonalities between Bitcoin and major Cryptocurrencies: Portfolio risk management implications," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 283-294.
    6. Smaniotto, Emanuelle Nava & Neto, Giacomo Balbinotto, 2022. "Speculative trading in Bitcoin: A Brazilian market evidence," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 47-54.
    7. Ante, Lennart, 2023. "How Elon Musk's Twitter activity moves cryptocurrency markets," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    8. De Pace, Pierangelo & Rao, Jayant, 2023. "Comovement and instability in cryptocurrency markets," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 173-200.
    9. Matkovskyy, Roman, 2019. "Centralized and decentralized bitcoin markets: Euro vs USD vs GBP," The Quarterly Review of Economics and Finance, Elsevier, vol. 71(C), pages 270-279.
    10. Corbet, Shaen & Cumming, Douglas J. & Lucey, Brian M. & Peat, Maurice & Vigne, Samuel A., 2020. "The destabilising effects of cryptocurrency cybercriminality," Economics Letters, Elsevier, vol. 191(C).
    11. Ardia, David & Bluteau, Keven & Rüede, Maxime, 2019. "Regime changes in Bitcoin GARCH volatility dynamics," Finance Research Letters, Elsevier, vol. 29(C), pages 266-271.
    12. Yarovaya, Larisa & Matkovskyy, Roman & Jalan, Akanksha, 2022. "The COVID-19 black swan crisis: Reaction and recovery of various financial markets," Research in International Business and Finance, Elsevier, vol. 59(C).
    13. Kazeem Abimbola Sanusi & Zandri Dickason-Koekemoer, 2022. "Cryptocurrency Returns, Cybercrime and Stock Market Volatility: GAS and Regime Switching Approaches," International Journal of Economics and Financial Issues, Econjournals, vol. 12(6), pages 52-64, November.
    14. Cheikh, Nidhaleddine Ben & Zaied, Younes Ben & Chevallier, Julien, 2020. "Asymmetric volatility in cryptocurrency markets: New evidence from smooth transition GARCH models," Finance Research Letters, Elsevier, vol. 35(C).
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    20. Prashant Sharma & Prashant Gupta & Dinesh Kumar Sharma & Gaurav Agarwal, 2022. "Investigating the Efficiency of Bitcoin Futures in Price Discovery," International Journal of Economics and Financial Issues, Econjournals, vol. 12(3), pages 104-109, May.
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    46. Skander Slim & Ibrahim Tabche & Yosra Koubaa & Mohamed Osman & Andreas Karathanasopoulos, 2023. "Forecasting realized volatility of Bitcoin: The informative role of price duration," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1909-1929, November.
    47. Vladimir Puzyrev, 2019. "Deep convolutional autoencoder for cryptocurrency market analysis," Papers 1910.12281, arXiv.org.
    48. Luo, Min & Kontosakos, Vasileios E. & Pantelous, Athanasios A. & Zhou, Jian, 2019. "Cryptocurrencies: Dust in the wind?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1063-1079.
    49. 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.
    50. Charfeddine, Lanouar & Benlagha, Noureddine & Maouchi, Youcef, 2020. "Investigating the dynamic relationship between cryptocurrencies and conventional assets: Implications for financial investors," Economic Modelling, Elsevier, vol. 85(C), pages 198-217.
    51. Fakhfekh, Mohamed & Jeribi, Ahmed, 2020. "Volatility dynamics of crypto-currencies’ returns: Evidence from asymmetric and long memory GARCH models," Research in International Business and Finance, Elsevier, vol. 51(C).
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    56. Pedro Bação & António Portugal Duarte & Hélder Sebastião & Srdjan Redzepagic, 2018. "Information Transmission Between Cryptocurrencies: Does Bitcoin Rule the Cryptocurrency World?," CeBER Working Papers 2018-06, Centre for Business and Economics Research (CeBER), University of Coimbra.
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    60. Farman Ullah Khan & Faridoon Khan & Parvez Ahmed Shaikh, 2023. "Forecasting returns volatility of cryptocurrency by applying various deep learning algorithms," Future Business Journal, Springer, vol. 9(1), pages 1-11, December.
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    62. Leopoldo Catania & Mads Sandholdt, 2019. "Bitcoin at High Frequency," JRFM, MDPI, vol. 12(1), pages 1-20, February.
    63. Walid Mensi & Mobeen Ur Rehman & Muhammad Shafiullah & Khamis Hamed Al-Yahyaee & Ahmet Sensoy, 2021. "High frequency multiscale relationships among major cryptocurrencies: portfolio management implications," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-21, December.
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    67. Baumöhl, Eduard, 2019. "Are cryptocurrencies connected to forex? A quantile cross-spectral approach," Finance Research Letters, Elsevier, vol. 29(C), pages 363-372.
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    78. Thomas E. Koker & Dimitrios Koutmos, 2020. "Cryptocurrency Trading Using Machine Learning," JRFM, MDPI, vol. 13(8), pages 1-7, August.
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    121. Platanakis, Emmanouil & Sutcliffe, Charles & Urquhart, Andrew, 2018. "Optimal vs naïve diversification in cryptocurrencies," Economics Letters, Elsevier, vol. 171(C), pages 93-96.
    122. Li, Jing-Ping & Naqvi, Bushra & Rizvi, Syed Kumail Abbas & Chang, Hsu-Ling, 2021. "Bitcoin: The biggest financial innovation of fourth industrial revolution and a portfolio's efficiency booster," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    123. Neveen Ahmed & Omar Farooq & Nidaa Hamed, 2023. "Relation Between Bitcoin and Its Forks: An Empirical Investigation," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 49(2), pages 249-261, April.
    124. Nikolaos A. Kyriazis, 2019. "A Survey on Efficiency and Profitable Trading Opportunities in Cryptocurrency Markets," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    125. Koutmos, Dimitrios, 2018. "Liquidity uncertainty and Bitcoin’s market microstructure," Economics Letters, Elsevier, vol. 172(C), pages 97-101.
    126. Walther, Thomas & Klein, Tony & Bouri, Elie, 2018. "Exogenous Drivers of Bitcoin and Cryptocurrency Volatility – A Mixed Data Sampling Approach to Forecasting," QBS Working Paper Series 2018/02, Queen's University Belfast, Queen's Business School.
    127. 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).
    128. Saralees Nadarajah & Emmanuel Afuecheta & Stephen Chan, 2021. "Dependence between bitcoin and African currencies," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(4), pages 1203-1218, August.
    129. Dunbar, Kwamie & Owusu-Amoako, Johnson, 2022. "Hedging the extreme risk of cryptocurrency," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    130. Li, Zhenghui & Chen, Liming & Dong, Hao, 2021. "What are bitcoin market reactions to its-related events?," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 1-10.
    131. Tan, Chia-Yen & Koh, You-Beng & Ng, Kok-Haur & Ng, Kooi-Huat, 2021. "Dynamic volatility modelling of Bitcoin using time-varying transition probability Markov-switching GARCH model," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    132. Bowden, James & Gemayel, Roland, 2022. "Sentiment and trading decisions in an ambiguous environment: A study on cryptocurrency traders," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    133. Manahov, Viktor & Urquhart, Andrew, 2021. "The efficiency of Bitcoin: A strongly typed genetic programming approach to smart electronic Bitcoin markets," International Review of Financial Analysis, Elsevier, vol. 73(C).
    134. Narayan, Paresh Kumar & Narayan, Seema & Eki Rahman, R. & Setiawan, Iwan, 2019. "Bitcoin price growth and Indonesia's monetary system," Emerging Markets Review, Elsevier, vol. 38(C), pages 364-376.
    135. Aiman Hairudin & Imtiaz Mohammad Sifat & Azhar Mohamad & Yusniliyana Yusof, 2022. "Cryptocurrencies: A survey on acceptance, governance and market dynamics," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4633-4659, October.
    136. Constandina Koki & Stefanos Leonardos & Georgios Piliouras, 2019. "A Peek into the Unobservable: Hidden States and Bayesian Inference for the Bitcoin and Ether Price Series," Papers 1909.10957, arXiv.org, revised Jul 2021.
    137. Corbet, Shaen & Katsiampa, Paraskevi, 2020. "Asymmetric mean reversion of Bitcoin price returns," International Review of Financial Analysis, Elsevier, vol. 71(C).
    138. Nitithumbundit, Thanakorn & Chan, Jennifer S.K., 2022. "Covid-19 impact on Cryptocurrencies market using Multivariate Time Series Models," The Quarterly Review of Economics and Finance, Elsevier, vol. 86(C), pages 365-375.
    139. Andrea Flori, 2019. "Cryptocurrencies In Finance: Review And Applications," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(05), pages 1-22, August.
    140. Mawuli Segnon & Stelios Bekiros, 2019. "Forecasting Volatility in Cryptocurrency Markets," CQE Working Papers 7919, Center for Quantitative Economics (CQE), University of Muenster.

  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.

    Cited by:

    1. Shay Kee Tan & Kok Haur Ng & Jennifer So-Kuen Chan, 2022. "Predicting Returns, Volatilities and Correlations of Stock Indices Using Multivariate Conditional Autoregressive Range and Return Models," Mathematics, MDPI, vol. 11(1), pages 1-24, December.
    2. Wu, Xinyu & Hou, Xinmeng, 2020. "Forecasting volatility with component conditional autoregressive range model," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    3. 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.
    4. Wu, Xinyu & Xie, Haibin & Zhang, Huanming, 2022. "Time-varying risk aversion and renminbi exchange rate volatility: Evidence from CARR-MIDAS model," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).

  13. 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.

    Cited by:

    1. Ioannis Badounas & Georgios Pitselis, 2020. "Loss Reserving Estimation With Correlated Run-Off Triangles in a Quantile Longitudinal Model," Risks, MDPI, vol. 8(1), pages 1-26, February.
    2. Gareth W. Peters & Pavel V. Shevchenko & Bertrand Hassani & Ariane Chapelle, 2016. "Should the advanced measurement approach be replaced with the standardized measurement approach for operational risk?," Papers 1607.02319, arXiv.org, revised Sep 2016.
    3. Liang Yang & Zhengxiao Li & Shengwang Meng, 2020. "Risk Loadings in Classification Ratemaking," Papers 2002.01798, arXiv.org, revised Jan 2022.
    4. Gareth W. Peters, 2018. "General Quantile Time Series Regressions for Applications in Population Demographics," Risks, MDPI, vol. 6(3), pages 1-47, September.
    5. Gareth W. Peters & Pavel V. Shevchenko & Bertrand K. Hassani & Ariane Chapelle, 2016. "Should the advanced measurement approach be replaced with the standardized measurement approach for operational risk?," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01391091, HAL.
    6. Gareth W. Peters & Pavel V. Shevchenko & Bertrand K. Hassani & Ariane Chapelle, 2016. "Should the advanced measurement approach be replaced with the standardized measurement approach for operational risk?," Post-Print halshs-01391091, HAL.
    7. Gareth W. Peters & Pavel V. Shevchenko & Bertrand K. Hassani & Ariane Chapelle, 2016. "Should the advanced measurement approach be replaced with the standardized measurement approach for operational risk?," Documents de travail du Centre d'Economie de la Sorbonne 16065, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.

  14. 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.

    Cited by:

    1. Kokonendji, Célestin C. & Puig, Pedro, 2018. "Fisher dispersion index for multivariate count distributions: A review and a new proposal," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 180-193.
    2. Arnold, Richard & Chukova, Stefanka & Hayakawa, Yu & Marshall, Sarah, 2020. "Geometric-Like Processes: An Overview and Some Reliability Applications," Reliability Engineering and System Safety, Elsevier, vol. 201(C).

  15. 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.

    Cited by:

    1. Arnold, Richard & Chukova, Stefanka & Hayakawa, Yu & Marshall, Sarah, 2020. "Geometric-Like Processes: An Overview and Some Reliability Applications," Reliability Engineering and System Safety, Elsevier, vol. 201(C).

  16. 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.

    Cited by:

    1. Avanzi, Benjamin & Taylor, Greg & Vu, Phuong Anh & Wong, Bernard, 2020. "A multivariate evolutionary generalised linear model framework with adaptive estimation for claims reserving," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 50-71.
    2. Boratyńska, Agata, 2017. "Robust Bayesian estimation and prediction of reserves in exponential model with quadratic variance function," Insurance: Mathematics and Economics, Elsevier, vol. 76(C), pages 135-140.
    3. 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.
    4. Ceren Eda Can & Gul Ergun & Refik Soyer, 2022. "Bayesian Analysis of Proportions via a Hidden Markov Model," Methodology and Computing in Applied Probability, Springer, vol. 24(4), pages 3121-3139, December.
    5. Benjamin Avanzi & Gregory Clive Taylor & Phuong Anh Vu & Bernard Wong, 2020. "A multivariate evolutionary generalised linear model framework with adaptive estimation for claims reserving," Papers 2004.06880, arXiv.org.
    6. Gareth W. Peters, 2018. "General Quantile Time Series Regressions for Applications in Population Demographics," Risks, MDPI, vol. 6(3), pages 1-47, September.
    7. Gareth W. Peters & Wilson Ye Chen & Richard H. Gerlach, 2016. "Estimating Quantile Families of Loss Distributions for Non-Life Insurance Modelling via L-Moments," Risks, MDPI, vol. 4(2), pages 1-41, May.
    8. Erengul Dodd & George Streftaris, 2017. "Prediction of settlement delay in critical illness insurance claims by using the generalized beta of the second kind distribution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(2), pages 273-294, February.
    9. Gareth W. Peters & Wilson Y. Chen & Richard H. Gerlach, 2016. "Estimating Quantile Families of Loss Distributions for Non-Life Insurance Modelling via L-moments," Papers 1603.01041, arXiv.org.
    10. Chan Jennifer So Kuen & Nitithumbundit Thanakorn & Peiris Shelton & Ng Kok-Haur, 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.

  17. 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.

    Cited by:

    1. Shay Kee Tan & Kok Haur Ng & Jennifer So-Kuen Chan, 2022. "Predicting Returns, Volatilities and Correlations of Stock Indices Using Multivariate Conditional Autoregressive Range and Return Models," Mathematics, MDPI, vol. 11(1), pages 1-24, December.
    2. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    3. Arnold, Richard & Chukova, Stefanka & Hayakawa, Yu & Marshall, Sarah, 2020. "Geometric-Like Processes: An Overview and Some Reliability Applications," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    4. 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.
    5. Wu, Xinyu & Hou, Xinmeng, 2020. "Forecasting volatility with component conditional autoregressive range model," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    6. 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.
    7. Chan Jennifer So Kuen & Nitithumbundit Thanakorn & Peiris Shelton & Ng Kok-Haur, 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.
    8. 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.

  18. 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.

    Cited by:

    1. Shiow-Lan Gau & Jean Dieu Tapsoba & Shen-Ming Lee, 2014. "Bayesian approach for mixture models with grouped data," Computational Statistics, Springer, vol. 29(5), pages 1025-1043, October.

  19. 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.

    Cited by:

    1. Tsiotas, Georgios, 2012. "On generalised asymmetric stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 151-172, January.
    2. Jerzy P. Rydlewski & Ma{l}gorzata Snarska, 2012. "On Geometric Ergodicity of Skewed - SVCHARME models," Papers 1209.1544, arXiv.org.
    3. Gregor Kastner & Sylvia Fruhwirth-Schnatter, 2017. "Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models," Papers 1706.05280, arXiv.org.
    4. Joshua C C Chan & Cody Y L Hsiao, 2013. "Estimation of Stochastic Volatility Models with Heavy Tails and Serial Dependence," CAMA Working Papers 2013-74, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    5. Mao, Xiuping & Czellar, Veronika & Ruiz, Esther & Veiga, Helena, 2020. "Asymmetric stochastic volatility models: Properties and particle filter-based simulated maximum likelihood estimation," Econometrics and Statistics, Elsevier, vol. 13(C), pages 84-105.
    6. Shinichiro Shirota & Takayuki Hizu & Yasuhiro Omori, 2013. "Realized Stochastic Volatility with Leverage and Long Memory," CIRJE F-Series CIRJE-F-880, CIRJE, Faculty of Economics, University of Tokyo.
    7. Nakajima Jouchi, 2013. "Stochastic volatility model with regime-switching skewness in heavy-tailed errors for exchange rate returns," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(5), pages 499-520, December.
    8. Wang, Joanna J.J., 2012. "On asymmetric generalised t stochastic volatility models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(11), pages 2079-2095.
    9. Aknouche, Abdelhakim & Dimitrakopoulos, Stefanos & Touche, Nassim, 2019. "Integer-valued stochastic volatility," MPRA Paper 91962, University Library of Munich, Germany, revised 04 Feb 2019.
    10. Sujay Mukhoti & Pritam Ranjan, 2016. "Mean-correction and Higher Order Moments for a Stochastic Volatility Model with Correlated Errors," Papers 1605.02418, arXiv.org.
    11. Mao, Xiuping & Ruiz Ortega, Esther & Lopes Moreira Da Veiga, María Helena, 2013. "One for all : nesting asymmetric stochastic volatility models," DES - Working Papers. Statistics and Econometrics. WS ws131110, Universidad Carlos III de Madrid. Departamento de Estadística.
    12. Yanhui Xi & Hui Peng & Yemei Qin, 2016. "Modeling Financial Time Series Based on a Market Microstructure Model with Leverage Effect," Discrete Dynamics in Nature and Society, Hindawi, vol. 2016, pages 1-15, February.
    13. Yong Li & Tao Zeng & Jun Yu, 2012. "Robust Deviance Information Criterion for Latent Variable Models," Working Papers 30-2012, Singapore Management University, School of Economics.
    14. 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.
    15. Ying Wang & Sai Tsang Boris Choy & Hoi Ying Wong, 2016. "Bayesian Option Pricing Framework with Stochastic Volatility for FX Data," Risks, MDPI, vol. 4(4), pages 1-12, December.
    16. Mao, Xiuping & Ruiz Ortega, Esther & Lopes Moreira Da Veiga, María Helena, 2014. "Score driven asymmetric stochastic volatility models," DES - Working Papers. Statistics and Econometrics. WS ws142618, Universidad Carlos III de Madrid. Departamento de Estadística.
    17. Stojanović, Vladica S. & Popović, Biljana Č. & Milovanović, Gradimir V., 2016. "The Split-SV model," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 560-581.
    18. Patricia Lengua Lafosse & Cristian Bayes & Gabriel Rodríguez, 2015. "A Stochastic Volatility Model with GH Skew Student’s t-Distribution: Application to Latin-American Stock Returns," Documentos de Trabajo / Working Papers 2015-405, Departamento de Economía - Pontificia Universidad Católica del Perú.
    19. Tang, Qihe & Tong, Zhiwei & Yang, Yang, 2021. "Large portfolio losses in a turbulent market," European Journal of Operational Research, Elsevier, vol. 292(2), pages 755-769.
    20. Lengua Lafosse, Patricia & Rodríguez, Gabriel, 2018. "An empirical application of a stochastic volatility model with GH skew Student's t-distribution to the volatility of Latin-American stock returns," The Quarterly Review of Economics and Finance, Elsevier, vol. 69(C), pages 155-173.

  20. 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.

    Cited by:

    1. Aknouche, Abdelhakim & Scotto, Manuel, 2022. "A multiplicative thinning-based integer-valued GARCH model," MPRA Paper 112475, University Library of Munich, Germany.
    2. Arnold, Richard & Chukova, Stefanka & Hayakawa, Yu & Marshall, Sarah, 2020. "Geometric-Like Processes: An Overview and Some Reliability Applications," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    3. 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.
    4. 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.

  21. 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.

    Cited by:

    1. 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.
    2. Arnold, Richard & Chukova, Stefanka & Hayakawa, Yu & Marshall, Sarah, 2020. "Geometric-Like Processes: An Overview and Some Reliability Applications," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    3. 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.

  22. 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.
    See citations under working paper version above.
  23. 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.

    Cited by:

    1. Jie Hua & Guohua Wang & Maolin Huang & Shuyang Hua & Shuanghe Yang, 2020. "A Visual Approach for the SARS (Severe Acute Respiratory Syndrome) Outbreak Data Analysis," IJERPH, MDPI, vol. 17(11), pages 1-16, June.

  24. 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.

    Cited by:

    1. 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.
    2. 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.

  25. 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.

    Cited by:

    1. Aydogdu, Halil & Kara, Mahmut, 2012. "Nonparametric estimation in [alpha]-series processes," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 190-201, January.
    2. Arnold, Richard & Chukova, Stefanka & Hayakawa, Yu & Marshall, Sarah, 2020. "Geometric-Like Processes: An Overview and Some Reliability Applications," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    3. 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.
    4. 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.
    5. 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.
    6. Chen, Jianwei & Li, Kim-Hung & Lam, Yeh, 2010. "Bayesian computation for geometric process in maintenance problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(4), pages 771-781.
    7. 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.

  26. 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.

    Cited by:

    1. Chen, Jinyuan & Li, Zehui, 2008. "An extended extreme shock maintenance model for a deteriorating system," Reliability Engineering and System Safety, Elsevier, vol. 93(8), pages 1123-1129.
    2. 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.
    3. Aydogdu, Halil & Kara, Mahmut, 2012. "Nonparametric estimation in [alpha]-series processes," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 190-201, January.
    4. Arnold, Richard & Chukova, Stefanka & Hayakawa, Yu & Marshall, Sarah, 2020. "Geometric-Like Processes: An Overview and Some Reliability Applications," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    5. 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.
    6. Tang, Ya-yong & Lam, Yeh, 2006. "A [delta]-shock maintenance model for a deteriorating system," European Journal of Operational Research, Elsevier, vol. 168(2), pages 541-556, January.
    7. Lam, Yeh & Zhang, Yuan Lin & Liu, Qun, 2006. "A geometric process model for M/M/1 queueing system with a repairable service station," European Journal of Operational Research, Elsevier, vol. 168(1), pages 100-121, January.
    8. 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.
    9. Lam, Yeh & Zhang, Yuan Lin & Zheng, Yao Hui, 2002. "A geometric process equivalent model for a multistate degenerative system," European Journal of Operational Research, Elsevier, vol. 142(1), pages 21-29, October.
    10. 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.
    11. Chen, Jianwei & Li, Kim-Hung & Lam, Yeh, 2010. "Bayesian computation for geometric process in maintenance problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(4), pages 771-781.
    12. 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.
    13. Lam, Yeh, 2007. "A geometric process maintenance model with preventive repair," European Journal of Operational Research, Elsevier, vol. 182(2), pages 806-819, October.
    14. 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.

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