Structural changes often occur in economics and finance due to changes in preferences, technologies, institutional reforms, policies, crises and other factors. It is important to distinguish whether a structural change is abrupt or evolutionary, because the implications on econometric modelling...

Modelling and forecasting symbolic data, especially interval-valued time series (ITS) data, has received considerable attention in statistics and related fields. The core of available methods on ITS analysis is based on various applications of conventional linear modelling. However, few works...

Modelling and forecasting symbolic data, especially interval-valued time series (ITS) data, has received considerable attention in statistics and related fields. The core of available methods on ITS analysis is based on various applications of conventional linear modelling. However, few works...

In this paper, we provide a model-free test for asymmetric correlations which suggest stocks tend to have greater correlations with the market when the market goes down than when it goes up. We also provide such tests for asymmetric betas and covariances. In addition, we evaluate the economic...

This paper proposes a new self-normalization method to construct confidence intervals for quantities of stationary time series. Unlike the self-normalization approach in Shao (2010) and Lobato (2001), which utilize the variance of a partial as the self-normalizer, we propose the use of its...

This paper proposes an adjusted-range based self-normalized test for change in correlation. Unlike the self-normalization approach proposed by Lobato (2001) and Shao (2010), which relies on the variance of the partial sum process as a self-normalizer, and used by Choi and Shin (2020) to...

In this paper, we introduce adjusted-range based Kolmogorov-Smirnov (KS) type statisticsto test for structural breaks in the mean of a process and also in a more general setting. We propose a normalization based on the adjusted-range of a partial sum, which is stochastically proportional to the...

We develop a novel machine learning method to estimate large dimensional time-varying GMM models via our newly designed ridge fusion regularization scheme. Our method is a one-step procedure and allows for abrupt, smooth and dual type time variation with a fast rate of convergence. It...

chapter 1 Introduction -- chapter 2 Methodology to detect extreme risk spillover -- chapter 3 VaR estimation -- chapter 4 Extreme risk spillover between Chinese stock markets and international stock markets -- chapter 5 Information spillover effects between Chinese futures market and spot market...

This paper proposes an adjusted-range based self-normalization method to construct confidence intervals for censored dependent data, which helps to circumvent the long-run variance estimation and tuning parameter selection problems. Simulation studies confirm the validity of this new approach