Inflation expectation (IE) is often considered to be an important determinant of actual inflation in modern economic theory, we are interested in investigating the main risk factors that determine its dynamics. We fiirst apply a joint arbitrage-free term structure model across different European...

This paper proposes a dynamic spatial autoregressive quantile model. Using predetermined network information, we study dynamic tail event driven risk using a system of conditional quantile equations. Extending Zhu, Wang, Wang and Härdle (2019), we allow the contemporaneous dependency of nodal...

Estimating spot covariance is an important issue to study, especially with the increasing availability of high-frequency nancial data. We study the estimation of spot covariance using a kernel method for high-frequency data. In particular, we consider rst the kernel weighted version of realized...

Pricing kernels implicit in option prices play a key role in assessing the risk aversion over equity returns. We deal with nonparametric estimation of the pricing kernel (Empirical Pricing Kernel) given by the ratio of the risk-neutral density estimator and the subjective density estimator. The...

It is an undisputed fact that weather risk increases over time due to climate change. However, qualification of this statement with regard to the type of weather risk and geographical location is needed. We investigate the application of novel statistical tools for assessing changes in weather...

Financial risk control has always been challenging and becomes now an even harder problem as joint extreme events occur more frequently. For decision makers and government regulators, it is therefore important to obtain accurate information on the interdependency of risk factors. Given a...

Understanding the dynamics of high dimensional non-normal dependency structure is a challenging task. This research aims at attacking this problem by building up a hidden Markov model (HMM) for Hierarchical Archimedean Copulae (HAC), where the HAC represent a wide class of models for high...

We consider the problem of estimating the conditional quantile of a time series fYtg at time t given covariates Xt, where Xt can ei- ther exogenous variables or lagged variables of Yt . The conditional quantile is estimated by inverting a kernel estimate of the conditional distribution function,...