Letmathe, S., Feng, Y., &; Uhde, A. (n.d.). Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall. Journal of Risk, 25(2).
LibreCat: /record/35992

Letmathe, S., Feng, Y., &; Uhde, A. (n.d.). Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall. Journal of Risk.https://doi.org/10.21314/JOR.2022.044 
LibreCat: /record/29317
DOI: 10.21314/JOR.2022.044

Peitz, C., Feng, Y., Gilroy, B. M., &; Stöckmann, N. (2020). The Shanghai-Hong Kong Stock Connect: An Application of the Semi-CGARCH and Semi-EGARCH. Asian Economic and Financial Review, 10(4), 427–438.
LibreCat: /record/16873

Schäfer, B., &; Feng, Y. (2018). Further Development of the Double Conditional Smoothing for Nonparametric Surfaces Under a Lattice Spatial Model. In Book of Abstracts (p. 7). Paderborn, Germany.
LibreCat: /record/4665

Feng, Y., &; Letmathe, S. (2018). The Non-Gaussian ESEMIFAR Model. Presented at the European Conference on Data Analysis, Paderborn, Germany.
LibreCat: /record/4667

Forstinger, S., Feng, Y., &; Peitz, C. (2018). Forecasting Non-Negative Financial Processes Using Different Parametric and Semi-Parametric ACD-Type Models. In Book of Abstracts (p. 17). Paderborn, Germany.
LibreCat: /record/4668

Zhang, X., &; Feng, Y. (2018). A Box-Cox Semiparametric Multiplicative Error Model. In Book of Abstracts (p. 19). Paderborn, Germany.
LibreCat: /record/4669

Zhang, X., Feng, Y., &; Peitz, C. (2017). A general class of SemiGARCH models based on the Box-Cox transformation.
LibreCat: /record/4633

Feng, Y., &; Gries, T. (2017). Data-driven local polynomial for the trend and its derivatives in economic time series.
LibreCat: /record/4671

Feng, Y., Forstinger, S., &; Peitz, C. (2015). On the iterative plug-in algorithm for estimating diurnal patterns of financial trade durations. Journal of Statistical Computation and Simulation, 86(12), 2291–2307.https://doi.org/10.1080/00949655.2015.1107908 
LibreCat: /record/4592
DOI: 10.1080/00949655.2015.1107908

Feng, Y., &; Zhou, C. (2015). Forecasting financial market activity using a semiparametric fractionally integrated Log-ACD. International Journal of Forecasting, 31(2), 349–363.https://doi.org/10.1016/j.ijforecast.2014.09.001 
LibreCat: /record/4593
DOI: 10.1016/j.ijforecast.2014.09.001

Beran, J., Feng, Y., &; Hebbel, H. (Eds.). (2015). Empirical Economic and Financial Research - Theory, Methods and Practice. Berlin: Springer.
LibreCat: /record/4649

Beran, J., Feng, Y., &; Hebbel, H. (2015). Introduction. In Empirical Economic and Financial Research (pp. 1–6). Cham: Springer International Publishing.https://doi.org/10.1007/978-3-319-03122-4_1 
LibreCat: /record/4650
DOI: 10.1007/978-3-319-03122-4_1

Feng, Y., &; Zhou, C. (2015). An iterative plug-in algorithm for realized kernels.
LibreCat: /record/4656

Beran, J., Feng, Y., &; Ghosh, S. (2014). Modelling long-range dependence and trends in duration series: an approach based on EFARIMA and ESEMIFAR models. Statistical Papers, 56(2), 431–451.https://doi.org/10.1007/s00362-014-0590-x 
LibreCat: /record/4599
DOI: 10.1007/s00362-014-0590-x

Beran, J., Feng, Y., &; Ghosh, S. (2014). On EFARIMA and ESEMIFAR Models. In Empirical Economic and Financial Research (pp. 239–253). Cham: Springer International Publishing.https://doi.org/10.1007/978-3-319-03122-4_15 
LibreCat: /record/4602
DOI: 10.1007/978-3-319-03122-4_15

Peitz, C., &; Feng, Y. (2014). Double Conditional Smoothing of High-Frequency Volatility Surface Under a Spatial Model. In Empirical Economic and Financial Research (pp. 341–356). Cham: Springer International Publishing.https://doi.org/10.1007/978-3-319-03122-4_21 
LibreCat: /record/4603
DOI: 10.1007/978-3-319-03122-4_21

Feng, Y. (2014). Data-driven estimation of diurnal patterns of durations between trades on financial markets. Statistics &; Probability Letters, 92, 109–113.https://doi.org/10.1016/j.spl.2014.05.011 
LibreCat: /record/4605
DOI: 10.1016/j.spl.2014.05.011

Zhou, C., &; Feng, Y. (2014). Data-driven estimation of realized kernels under dependent microstructure noise and further analysis using the Semi-FI-Log-ACD. Presented at the Conference on Computational and Financial Econometrics, University of Pisa, Italy.
LibreCat: /record/4664

Feng, Y., Guo, Z., &; Peitz, C. (2013). A Tree-form Constant Market Share Model for Growth Causes in International Trade Based on Multi-level Classification. Journal of Industry, Competition and Trade, 14(2), 207–228.https://doi.org/10.1007/s10842-013-0156-y 
LibreCat: /record/4596
DOI: 10.1007/s10842-013-0156-y