The first three factors resulting from a principal components analysis of term structure data are in the literature typically interpreted as driving the level, slope and curvature of the term structure. Using slight generalisations of theorems from total positivity, we present sufficient...

We analyse a sample of funds and other securities each assigned a total rating score by an unknown expert entity. The scores are based on a number of risk and complexity factors, each assigned a category (factor score) of Low, Medium, or High by the expert entity. A principal component analysis...

Random subspace methods are a novel approach to obtain accurate forecasts in high-dimensional regression settings. We provide a theoretical justification of the use of random subspace methods and show their usefulness when forecasting monthly macroeconomic variables. We focus on two approaches....

There has been increased interest in the use of "big data" when it comes to forecasting macroeconomic time series such as private consumption or unemployment. However, applications on forecasting GDP are rather rare. In this paper we incorporate Google search data into a Bridge Equation Model, a...

This paper proposes two-step static hedging strategies for European basket options by using only plain-vanilla options on a subset of underlying assets. The basic idea is stimulated from a static super-hedging strategy dependent on the whole basket. However, it would be too complicated to handle...

Existing indicators of entrepreneurial activity (such as the Global Entrepreneurship Monitor, Entrepreneurship Barometer, FORA's Entrepreneurship Index, OECD and Economic Commission's sets of indicators, among others) and several variables that have been considered good proxies for...

This master thesis discusses selected topics of Functional Data Analysis (FDA). FDA deals with the random variables (and process) with realizations in the (smooth) functional space. The first part of this thesis introduces the basic assumptions, notation and ideas of FDA, here we will mainly...