Chen Huang
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Huang
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Chen
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  • Publication
    Flexible HAR Model for Realized Volatility
    ( 2019-08)
    Audrino, Francesco
    ;
    Huang, Chen
    ;
    Ostap, Okhrin
    The Heterogeneous Autoregressive (HAR) model is commonly used in modeling the dynamics of realized volatility. In this paper, we propose a flexible HAR(1,...,p) specification, employing the adaptive LASSO and its statistical inference theory to see whether the lag structure (1, 5, 22) implied from an economic point of view can be recovered by statistical methods. The model differs from Audrino and Knaus (2016) where the authors apply LASSO on the AR(p) model, which does not necessarily lead to a HAR model. Adaptive LASSO estimation and the subsequent hypothesis testing results fail to show strong evidence that such a fixed lag structure can be recovered by a flexible model. We also apply the group LASSO and related tests to check the validity of the classic HAR, which is rejected in most cases. The results justify our intention to use a flexible lag structure while still keeping the HAR frame. In terms of the out-of-sample forecasting, the proposed flexible specification works comparably to the benchmark HAR(1, 5, 22). Moreover, the time-varying model combinations show that when the market environment is not stable, the fixed lag structure (1, 5, 22) is not particularly accurate and effective.
    Type: journal article
    Journal: Studies in Nonlinear Dynamics and Econometrics
    Volume: 23
    Issue: 3
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/98368
  • Publication
    Multivariate factorizable expectile regression with application to fMRI data
    (Elsevier Science, 2018-05)
    Chao, Shih-Kang
    ;
    Härdle, Wolfgang
    ;
    Huang, Chen
    A multivariate expectile regression model is proposed to analyze the tail events of large cross-sectional and spatial data, where the tail events are linked by a latent factor structure. The computational advantage of the method is demonstrated, and the estimation risk is analyzed for every fixed number of iteration and fixed sample size, when the latent factors are either exactly or approximately sparse. The proposed method is applied on the functional magnetic resonance imaging (fMRI) data taken during an experiment of investment decisions making. It is shown that the negative extreme blood oxygenation level dependent (BOLD) responses may be relevant to the risk preferences.
    Type: journal article
    Journal: Computational Statistics & Data Analysis
    Volume: 121
    URL: https://doi.org/10.1016/j.csda.2017.12.001
    DOI: 10.1016/j.csda.2017.12.001
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/100522
    Scopus© Citations 2
  • Publication
    Spatial Functional Principal Component Analysis with Applications to Brain Image Data
    ( 2018-11-07)
    Li, Yingxing
    ;
    Huang, Chen
    ;
    Härdle, Wolfgang
    This paper considers a fast and effective algorithm for conducting functional principal component analysis with multivariate factors. Compared with the univariate case, our approach could be more powerful in revealing spatial connections or extracting important features in images. To facilitate fast computation, we connect singular value decomposition with penalized smoothing and avoid estimating a covariance operator in very high dimension. Under regularity assumptions, the results indicate that we may enjoy the optimal convergence rate by employing the smoothness assumption inherent to functional objects. We apply our method to the analysis of brain image data. Our extracted factors provide excellent recovery of the risk related regions of interest in the human brain and the estimated loadings are very informative in revealing individual risk attitude.
    Type: forthcoming
    Journal: Journal of Multivariate Analysis
    URL: https://doi.org/10.1016/j.jmva.2018.11.004
    DOI: 10.1016/j.jmva.2018.11.004
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/99878
    Scopus© Citations 7
  • Publication
    How does post-earnings announcement sentiment affect firms' dynamics? New evidence from causal machine learning
    ( 2020-12-22)
    Audrino, Francesco
    ;
    Chassot, Jonathan
    ;
    Huang, Chen
    ;
    Knaus, Michael
    ;
    Lechner, Michael
    ;
    Ortega Lahuerta, Juan-Pablo
    We revisit the role played by sentiment extracted from news articles related to earnings announcements as a driver of firms' return, volatility, and trade volume dynamics. To this end we apply causal machine learning on the earnings announcements of a wide cross-section of US companies. This approach allows us to investigate firms' price and volume reactions to different types of post-earnings announcement sentiment (positive, negative, and mixed sentiments) under various underlying macroeconomic and aggregated investors' moods in a properly defined causal framework. Our empirical results support the presence of (i) investors' overconfidence and mispricing due to biased expectations; (ii) a leverage effect in sentiment where reactions are (on average) larger for negative sentiment; and (iii) investors' underreaction to news. Finally, we show that the difference in the average causal effects of the sentiment's types is larger when the general macroeconomic conditions are worse or the uncertainty in the global financial market is higher.
    Type: working paper
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/111461
  • Publication
    LASSO-Driven Inference in Time and Space
    (arXiv preprint, 2018-10-15)
    Chernozhukov, Victor
    ;
    Härdle, Wolfgang
    ;
    Huang, Chen
    ;
    Wang, Weining
    We consider the estimation and inference in a system of high-dimensional regression equations allowing for temporal and cross-sectional dependency in covariates and error processes, covering rather general forms of weak dependence. A sequence of large-scale regressions with LASSO is applied to reduce the dimensionality, and an overall penalty level is carefully chosen by a block multiplier bootstrap procedure to account for multiplicity of the equations and dependencies in the data. Correspondingly, oracle properties with a jointly selected tuning parameter are derived. We further provide high-quality de-biased simultaneous inference on the many target parameters of the system. We provide bootstrap consistency results of the test procedure, which are based on a general Bahadur representation for the Z-estimators with dependent data. Simulations demonstrate good performance of the proposed inference procedure. Finally, we apply the method to quantify spillover effects of textual sentiment indices in a financial market and to test the connectedness among sectors.
    Type: working paper
    URL: https://arxiv.org/abs/1806.05081
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/99941