Understanding the topological structure of real world networks is of huge interest in a variety of fields. One of the way to investigate this structure is to find the groups of densely connected nodes called communities. This paper presents a new non-parametric method of community detection in...

Deep learning has substantially advanced the state-of-the-art in computer vision, natural language processing and other elds. The paper examines the potential of contemporary recurrent deep learning architectures for nancial time series forecasting. Considering the foreign exchange market as...

Cryptocurrencies are becoming an attractive asset class and are the focus of recent quantitative research. The joint dynamics of the cryptocurrency market yields information on network risk. Utilizing the adaptive LASSO approach, we build a dynamic network of cryptocurrencies and model the...

Housing typically takes up a major proportion of households' expenditure, and thus it certainly plays a critical role in shaping the pattern of income inequality and social mobility. Whether high housing price-to-rent ratio will am- plify inequality and inhibit social class upgrading is still a...

In this paper, we develop an multi period overlapping generation framework to investigate agents' consumption and saving decisions, inequality and welfare among elderly. We assume that agents are heterogeneous in the non-asset income and the medical expenditure. In order to explicitly analyze...

In this paper, we build an overlapping generation model to examine the reason why developed countries with similar background have implemented different social health insurance systems. We propose two hypotheses to explain this phenomenon: (i) the different participation rates of the poor in the...

We consider a new procedure for detecting structural breaks in mean for high- dimensional time series. We target breaks happening at unknown time points and locations. In particular, at a fixed time point our method is concerned with either the biggest break in one location or aggregating...

The paper presents a systematic theory for asymptotic inferences based on autocovariances of stationary processes. We consider nonparametric tests for se rial correlations using the maximum and the quadratic deviations of sample autocovariances. For these cases, with proper centering and...

We propose an approach to calibrate the conditional value-at-risk (CoVaR) of financial institutions based on neural network quantile regression. Building on the estimation results we model systemic risk spillover effects across banks by considering the marginal effects of the quantile regression...

A daily systemic risk measure is proposed accounting for links and mutual dependencies between financial institutions utilising tail event information. FRM (Financial Risk Meter) is based on Lasso quantile regression designed to capture tail event co-movements. The FRM focus lies on...