The Financial Risk Meter (FRM) is an established mechanism that, based on conditional Value at Risk (VaR) ideas, yields insight into the dynamics of network risk. Originally, the FRM has been composed via Lasso based quantile regression, but we here extend it by incorporating the idea of...

This paper considers estimation and inference for varying-coefficient models with nonstationary regressors. We propose a nonparametric estimation method using penalized splines, which achieves the same optimal convergence rate as kernel-based methods, but enjoys computation advantages. Utilizing...

This paper considers smooth principle component analysis for high dimensional data with very large dimensional observations p and moderate number of individuals N. Our setting is similar to traditional PCA, but we assume the factors are smooth and design a new approach to estimate them. By...

This paper considers estimation and inference for varying-coefficient models with nonstationary regressors. We propose a nonparametric estimation method using penalized splines, which achieves the same optimal convergence rate as kernel-based methods, but enjoys computation advantages. Utilizing...

This paper investigates the effect of introducing index futures trading on the spot price volatility in the Chinese stock market. We employ a recently developed panel data policy evaluation approach (Hsiao et al. 2011) to construct counterfactuals of the spot market volatility, based mainly on...

The Financial Risk Meter (FRM) is an established mechanism that, based on conditional Value at Risk (VaR) ideas, yields insight into the dynamics of network risk. Originally, the FRM has been composed via Lasso based quantile regression, but we here extend it by incorporating the idea of...