Ying Chen (陈颖）
Dr. Ying Chen is a financial statistician and data scientist. She develops statistical modelling and machine learning methods customized for nonstationary, high frequency and large dimensional complex data such as cryptocurrency, limit order book, and renewable energy. She also works on forecasting, quantum computing in finance, citation analysis and research metrics, financial text mining and sentiment analysis, and network analysis.
Dr. Chen is Associate Professor in Department of Mathematics, Academic Director of the Digital FinTech PhD program in Asian Institute of Digital Finance, and Joint Appointee in Risk Management Institute (1 July 2019 to 30 June 2023), National University of Singapore. She also holds Joint Appointment in Department of Statistics and Applied Probability (1 January 2019 to 31 December 2021), and Courtesy Appointment in Department of Economics (April 1, 2018 to March 31, 2021) at the National University of Singapore. She is also Faculty member in NUS Graduate School for Integrative Sciences and Engineering since July 2016.
Dr. Chen is Council Member of the International Statistical Institute for the period 2023 – 2027. She is Associate Editor of 4 journals including Statistica Sinica, Statistics and Its Interface, and Digital Finance. She is ISI Elected Member since March 2016. She is Scientific Secretary (August 2017 to July 2019) and member of Executive Committee of the International Association for Statistical Computing (IASC) from July 2017 and Board of Director ordinary member of the Asian Regional Section (ARS) of IASC. She is regular member of the Advisory Board of Institute of Statistical Mathematics, Japan from 1 April 2018 to 31 March 2022.
- Quantitative Finance/FinTech/RegTech: Portfolio Liquidation; NLP and Sentiments; Market Making; Quantum Computing in Finance
- Time Series Analysis: Nonstationary Time Series; Functional Time Series, Networks and Spatial-Temporal Data
- Energy Data Analytics: Modeling and forecasting
- Data Oriented Analytics in Precision Medicine, Patent Valuation, eXplainable AI
- B.Sc. in Economics (1998) Renmin University of China 中国⼈民⼤学, China
- M.A. in Economics and Management Science (2002) Humboldt-Universität zu Berlin, Germany
- M.Sc. in Statistics (2005) Humboldt-Universität zu Berlin and Freie Universität Berlin, Germany
- Ph.D. in Statistics (2007) Summa Cum Laude Humboldt-Universität zu Berlin. Supervisors: Prof. Dr. Wolfgang Härdle (Humboldt-Universität zu Berlin, firstname.lastname@example.org) and Prof. Dr. Vladimir Spokoiny (Weierstraß-Institut für Angewandte Analysis und Stochastik, email@example.com)
- Council Member of the International Statistical Institute for the period 2023 – 2027.
- Scientific Programme Committee Member of the 64th ISI World Statistics Congress (WSC2023) in Ottawa, Canada on 16–20 July 2023.
- Organizing Committee member of Artificial Intelligence for FinTech (AI4FinTech) at Association for the Advancement of Artificial Intelligence (AAAI) 2023 Summer Symposium Series, Singapore on 17-19 July 2023.
- Scientific Secretary (July 2017-June 2019) and Executive Committee Member (July 2017 – June 2023) of the International Association for Statistical Computing (IASC)
- Regular member of the Advisory Board of Institute of Statistical Mathematics, Japan from 1 April 2018 to 31 March 2022
- Advisor of the EU FIN-TECH project, under the EU’s Horizon2020 funding scheme, led by Prof. Paolo Giudici (https://www.fintech-ho2020.eu/)
- Scientific committee member of eXplainable Artificial Intelligence in Healthcare Management (xAIM) project under review by EU
- ISI Elected Member since March 2016 – Board of Director ordinary member of the Asian Regional Section (ARS) of the International Association for Statistical Computing (IASC)
- Associate Editor of Statistica Sinica (August 1, 2017 to July 31, 2023), Statistics and Its Interface, Digital Finance and Computational Statistics
- Iwasaki, H., Chen, Y., Tu J. (2023) Topic Tones of Analyst Reports and Stock Returns: A Deep Learning Approach. International Review of Finance.
- Xu, X., Zhang, Y., Liu, Y., Goto, Y., Taniguchi, M., and Chen, Y. (2023) Long-memory Log-linear Zero-inflated Generalized Poisson Autoregression for Covid-19 Pandemic Modeling. Statistica Sinica. doi.org/10.5705/ss.202022.0148.
- Chen, Y.,Koch, T., Zakiyeva, N., Liu, K., Xu, Z., Chen, CH., Nakano, J., Honda, K. (2023)Article’s Scientific Prestige: measuring the impact of individual articles in the Web of Science. Journal of Informetrics. Volume 17, Issue 1, doi.org/10.1016/j.joi.2023.
- Lai, W.T., Chen, R.B., Chen, Y., and Koch, T. (2022) Variational Bayesian Inference for Network Autoregression Models. Computational Statistics and Data Analysis, Volume 169, 107406, doi.org/10.1016/j.csda.2021.107406.
- Xu, X., Chen, Y., Zhang, G. and Koch, T. (2022) Modeling functional time series and mixed-type predictors with partially functional autoregression. Journal of Business & Economic Statistics. 10.1080/07350015.2021.2011299.
- Liu, P., Chen, Y. and Teo, C.P. (2021) Limousine Service Management: Capacity Planning with Predictive Analytics and Optimization. INFORMS Journal on Applied Analytics, 245-328, doi.org/10.1287/inte.2021.1079.
- Xu, X., Chen, Y. and Kou, S. (2021) Discussion on “Text Selection”. Journal of Business & Economic Statistics, 39:4, 883-887.10.1080/07350015.2021.1942890.
- Xu, X., Chen, Y., Goude, Y. and Yao, Q. (2021) Day-ahead Probabilistic Forecasting for French Half-hourly Electricity Loads and Quantiles for Curve-to-Curve Regression. Accepted by Applied Energy.
- Chen, Y., Koch, T., Zakiyeva, N., and Zhu, B. (2020) Modeling and Forecasting the Dynamics of the Natural Gas Transmission Network in Germany with the Demand and Supply Balance Constraint. Applied Energy. Volume 278, 115597. https://doi.org/10.1016/j.apenergy.2020.115597
- Xu, X., Chen, Y., Chen, C.W.S and Lin, X. (2020) Adaptive Log-Linear Zero-Inflated Generalized Poisson Autoregressive Model with Applications to Crime Counts Data. Annals of Applied Statistics. Volume 14, 1493-1515. https://doi.org/10.1214/20-AOAS1360
- Chen, Y., Koch, T., Lim, K.G., Xu, X. and Zakiyeva, N. (2020) A review study of functional autoregressive models with application to energy forecasting. WIREs Computational Statistics. https://doi.org/10.1002/wics.1525
- Zhu, Y., Han, X. and Chen, Y. (2020). Bayesian estimation and model selection of threshold spatial Durbin model. Economics Letters. Volume 188, March 2020, 108956, https://doi.org/10.1016/j.econlet.2020.108956
- Chen, Y., Giudici, P., Misheva, B.H., and Trimborn, S. (2020). Lead Behaviour in Bitcoin Markets. Risks. Volume 8(1), 4; https://doi.org/10.3390/risks8010004
- Chen, Y., Koch, T. and Xu, X. (2020). Day-Ahead High-Resolution Forecasting of Natural Gas Demand and Supply in Germany with a Hybrid Model. Applied Energy, Volume 262, 15 March 2020, 114486 https://doi.org/10.1016/j.apenergy.2019.114486
- Lin, L.Ch., Chen, Y., Pan, G. & Spokoiny, V. (2019). Efficient and semi-positive definite pre-averaging realized covariance estimator. Accepted by Statistica Sinica. http://www3.stat.sinica.edu.tw/ss_newpaper/SS-2017-0489_na.pdf, DOI number: 10.5705/ss.202017.0489.
- Chen, Y., Chua, W.S. & Hӓrdle, W.K (2019). Forecasting Limit Order Book Liquidity Supply-Demand Curves with Functional AutoRegressive Dynamics, Quantitative Finance. 19(9):1473-1489. https://dx.doi.org/10.1080/14697688.2019.1622290
- Chen, Y., Marron, J.S. & Zhang,J. (2019). Modeling Seasonality and Serial Dependence of Electricity Price Curves with Warping Functional AutoregressiveDynamics, Annals of Applied Statistics. 13 (3): 1590-1616. http://dx.doi.org/10.1214/18-AOAS1234
- Lim, K.G., Chen, Y. & Yap, N (2019), Intraday Information from S&P 500 Index Futures Options, Journal of Financial Markets 42:29-55. https://dx.doi.org/10.1016/J.FINMAR.2018.10.001
- Chen, Y., Niu, L., Chen, R.B. & He, Q. & (2019). Sparse-Group Independent Component Analysis with Application to Yield Curves Prediction. Computational Statistics and Data Analysis 133:76-89. https://dx.doi.org/10.1016/J.CSDA.2018.08.027
- Guo, J. & Chen, Y. (2019). An L2-norm based ANOVA test for the equality of weakly dependent functional time series. Statistics and its Interface 12(1):167-180. https://dx.doi.org/10.4310/SII.2019.V12.N1.A14
- Chen, Y., Hӓrdle, W.K., He, Q. & Majer, P. (2018). Risk Related Brain Regions Detection and Individual Risk Classification with 3D Image FPCA, Statistics and Risk Modeling 35(3-4):89-110. https://dx.doi.org/10.1515/STRM-2017-0011
- Chen, Y., Han, Q. & Niu, L.. (2018), Forecasting the Term Structure of Option Implied Volatility: The Power of an Adaptive Method, Journal of Empirical Finance 49:157-177. https://dx.doi.org/10.1016/J.JEMPFIN.2018.09.006
- Chen, Y., Chua, W.S. & Koch, T.(2018). Forecasting day-ahead high-resolution natural-gas demand and supply in Germany. Applied Energy, 228, 1091-1110. https://dx.doi.org/10.1016/J.APENERGY.2018.06.137
- Chen, Y., & Li, B. (2017). An Adaptive Functional Autoregressive Forecast Model to Predict Electricity Price Curves. Journal of Business & Economic Statistics, 35(3), 371-388. https://dx.doi.org/10.1080/07350015.2015.1092976
- Xu, M., Li, J., & Chen, Y. (2017). Varying Coefficient Functional Autoregressive Model with Application to the US Treasuries. Journal of Multivariate Analysis, 159, 168-183. https://dx.doi.org/10.1016/J.JMVA.2017.05.003
- Niu, L., Xu, X., & Chen, Y. (2017). An Adaptive Approach to Forecasting Three Key Macroeconomic Variables for Transitional China. Economic Modelling, 66, 201-213. https://dx.doi.org/10.1016/J.ECONMOD.2017.07.001
- Weisman, O., Pelphrey, K. A., Leckman, J. F., Feldman, R., Lu, Y., Chong, A., Chen, Y., Monakhov, M., Chew, S. H. & Ebstein, R. P. (2015). The Association between 2D: 4D Ratio and Cognitive Empathy is Contingent on a Common Polymorphism in the Oxytocin Receptor Gene (OXTR rs53576). Psychoneuroendocrinology, 58, 23-32. https://dx.doi.org/10.1016/J.PSYNEUEN.2015.04.007
- Chen, Y., & Spokoiny, V. (2015). Modeling Nonstationary and Leptokurtic Financial Time Series. Econometric Theory, 31(4), 703-728. https://dx.doi.org/10.1017/S0266466614000528
- Chen, R. B., Chen, Y., & Härdle, W. K. (2014). TVICA—Time Varying Independent Component Analysis and Its Application to Financial Data. Computational Statistics & Data Analysis, 74, 95-109.
- Chen, Y., & Niu, L. (2014). Adaptive Dynamic Nelson-Siegel Term Structure Model with Applications. Journal of Econometrics, 180(1), 98-115.
- Chen, Y., Li, B., & Niu, L. (2013). A Local Vector Autoregressive Framework and its Applications to Multivariate Time Series Monitoring and Forecasting. Statistics and Its Interface, 6(4), 499-509.
- Chen, Y., & Lu, J. (2012). Value at Risk Estimation. In Handbook of Computational Finance, 307-333. Springer, Berlin, Heidelberg.
- Chen, Y., & Li, B. (2011). Forecasting Yield Curves in an Adaptive Framework. Central European Journal of Economic Modelling and Econometrics, 3(4), 237-259.
- Chen, Y., Härdle, W. K., & Pigorsch, U. (2010). Localized Realized Volatility Modelling, Journal of the American Statistical Association, 105(492), 1376-1393.
- Chen, Y., Härdle, W., & Spokoiny, V. (2010). GHICA—Risk Analysis with GH Distributions and Independent Components. Journal of Empirical Finance, 17(2), 255-269.
- Chen, Y., Härdle, W., & Jeong, S. O. (2008). Nonparametric Risk Management with Generalized Hyperbolic Distributions. Journal of the American Statistical Association, 103(483), 910-923.
- Chen, Y., Härdle, W., & Spokoiny, V. (2007). Portfolio Value at Risk Based on Independent Component Analysis. Journal of Computational and Applied Mathematics, 205(1), 594-607.
- Chen, Y., Härdle, W., & Schultz, R. (2005). Prognose mit nichtparametrischen Verfahren. In Prognoserechnung (pp. 113-124).