Schriftenreihen und Sammelbände
http://edoc.hu-berlin.de/18452/5
2017-11-24T03:12:53ZDynamic credit default swaps curves in a network topology
http://edoc.hu-berlin.de/18452/19254
Dynamic credit default swaps curves in a network topology
Xu, Xiu; Chen, Cathy Yi-Hsuan; Härdle, Wolfgang K.
Systemically important banks are connected and have dynamic dependencies of their default probabilities. An extraction of default factors from cross-sectional credit default swaps (CDS) curves allows to analyze the shape and the dynamics of the default probabilities. Extending the Dynamic Nelson Siegel (DNS) model, we propose a network DNS model to analyze the interconnectedness of default factors in a dynamic fashion, and forecast the CDS curves. The extracted level factors representing long-term default risk demonstrate 85.5% total connectedness, while the slope and the curvature factors document 79.72% and 62.94% total connectedness for the short-term and middle-term default risk, respectively. The issues of default spillover and systemic risk should be weighted for the market participants with longer credit exposures, and for regulators with a mission to stabilize financial markets. The US banks contribute more to the long-run default spillover before 2012, whereas the European banks are major default transmitters during and after the European debt crisis either in the long-run or short-run. The outperformance of the network DNS model indicates that the prediction on CDS curve requires network information.
2016-12-29T00:00:00ZMultivariate Factorisable Sparse Asymmetric Least Squares Regression
http://edoc.hu-berlin.de/18452/19253
Multivariate Factorisable Sparse Asymmetric Least Squares Regression
Chao, Shih-Kang; Härdle, Wolfgang K.; Huang, Chen
More and more data are observed in form of curves. Numerous applications in finance, neuroeconomics, demographics and also weather and climate analysis make it necessary to extract common patterns and prompt joint modelling of individual curve variation. Focus of such joint variation analysis has been on fluctuations around a mean curve, a statistical task that can be solved via functional PCA. In a variety of questions concerning the above applications one is more interested in the tail asking therefore for tail event curves (TEC) studies. With increasing dimension of curves and complexity of the covariates though one faces numerical problems and has to look into sparsity related issues. Here the idea of FActorisable Sparse Tail Event Curves (FASTEC) via multivariate asymmetric least squares regression (expectile regression) in a high-dimensional framework is proposed. Expectile regression captures the tail moments globally and the smooth loss function improves the convergence rate in the iterative estimation algorithm compared with quantile regression. The necessary penalization is done via the nuclear norm. Finite sample oracle properties of the estimator associated with asymmetric squared error loss and nuclear norm regularizer are studied formally in this paper. As an empirical illustration, the FASTEC technique is applied on fMRI data to see if individual’s risk perception can be recovered by brain activities. Results show that factor loadings over different tail levels can be employed to predict individual’s risk attitudes.
2016-12-29T00:00:00ZFactorisable Multi-Task Quantile Regression
http://edoc.hu-berlin.de/18452/19252
Factorisable Multi-Task Quantile Regression
Chao, Shih-Kang; Härdle, Wolfgang K.; Yuan, Ming
For many applications, analyzing multiple response variables jointly is desirable because of their dependency, and valuable information about the distribution can be retrieved by estimating quantiles. In this paper, we propose a multi-task quantile regression method that exploits the potential factor structure of multivariate conditional quantiles through nuclear norm regularization. We jointly study the theoretical properties and computational aspects of the estimating procedure. In particular, we develop an efficient iterative proximal gradient algorithm for the non-smooth and non-strictly convex optimization problem incurred in our estimating procedure, and derive oracle bounds for the estimation error in a realistic situation where the sample size and number of iterative steps are both finite. The finite iteration analysis is particular useful when the matrix to be estimated is big and the computational cost is high. Merits of the proposed methodology are demonstrated through a Monte Carlo experiment and applications to climatological and financial study. Specifically, our method provides an objective foundation for spatial extreme clustering, and gives a refreshing look on the global financial systemic risk. Supplementary materials for this article are available online.
2016-12-29T00:00:00ZHow Does Rising House Price Influence Stock Market Participation in China?
http://edoc.hu-berlin.de/18452/19251
How Does Rising House Price Influence Stock Market Participation in China?
Chen, Xiaoyu; Ji, Xiaohao
This is an empirical study on the effect of house price on stock-market participation and its depths based on unique China Household Finance Survey (CHFS) data in 2011 and 2013 including 36213 sample households. We mainly found that, with an increase of one thousand RMB per square meter in macro house price, the probability to participate in the stock market will increase by 5.4% before controlling for wealth effect and 2.84% afterwards, indicating the existence of wealth effect. The participation depths of the stock-total asset ratio is expected to decrease by 0.23% and absolute stock asset is observed to decrease by 5.8 thousand RMB in response to one thousand RMB increase of per square meter house price. The effect of house price on participation decision is also related to housing area, and the negative effect of house price on stock market participation depths gets more intense with the increase of the stock-total asset ratio.
2016-12-22T00:00:00Z