Electricity load forecasts are an integral part of many decision-making pro- cesses in the electricity market. However, most literature on electricity load forecasting concentrates on deterministic forecasts, neglecting possibly impor- tant information about uncertainty. A more complete picture...

When agents do not know where to find a match, they search. However, agents could direct their search to agents who strategically choose a certain signal. Introducing cheap talk to a model of sequential search with bargaining, we find that signals will be truthful if there are mild...

The influence of maternal health problems on child’s worrying status is important in practice in terms of the intervention of maternal health problems early for the influence on child’s worrying status. Conventional methods apply symmetric prior distributions such as a normal...

This paper is intended as a guide to building insurance risk (loss) models. A typical model for insurance risk, the so-called collective risk model, treats the aggregate loss as having a compound distribution with two main components: one characterizing the arrival of claims and another...

News carry information of market moves. The gargantuan plethora of opinions, facts and tweets on financial business offers the opportunity to test and analyze the influence of such text sources on future directions of stocks. It also creates though the necessity to distill via statistical...

We analyze the impact of CDS trading on bank syndication activity. Theoretically, the effect of CDS trading is ambiguous: on the one hand, CDS can improve risksharing and hence be a more flexible risk management tool than loan syndication; on the other hand, CDS trading can reduce bank...

We introduce a methodology for measuring default risk connectedness that is based on an out-of-sample variance decomposition of model forecast errors. The out-of-sample nature of the procedure leads to \realized" measures which, in practice, respond more quickly to crisis occurrences than those...

Principal component analysis (PCA) is a widely used dimension reduction tool in the analysis of many kind of high-dimensional data. It is used in signal process- ing, mechanical ingeneering, psychometrics, and other fields under different names. It still bears the same mathematical idea: the...

Large panels of variables are used by policy makers in deciding on policy actions. Therefore it is desirable to include large information sets in models for economic analysis. In this survey methods are reviewed for accounting for the information in large sets of variables in vector...

Long-run restrictions have been used extensively for identifying structural shocks in vector autoregressive (VAR) analysis. Such restrictions are typically just-identifying but can be checked by utilizing changes in volatility. This paper reviews and contrasts the volatility models that have...