The study of poverty and its quantification is a critical yet unresolved problem in social science. This work seeks to use a new composite indicator to assess poverty as a multidimensional concept. However, subjective decisions, such as various weighting systems on the indicator's creation, may...

The analysis and measurement of poverty is a crucial and unsolved issue in the field of social science. This work aims to measure poverty as a multidimensional notion using a new composite indicator. However, subjective choices as different weighting schemes on the indicator's construction could...

The paper presents a comparison of the two languages Python and R related to the classification tools and demonstrates the differences in their syntax and graphical output. It indicates the functionality of R and Python packages {dendextend} and scipy.cluster as effective tools for the...

This article employs two alternative approaches based on input-output tables to re-evaluate key Australian industries and their interconnections. The first approach utilizes traditional linkages, while the second approach employs machine learning methods. Data come from the OECD database for...

The analysis and measurement of poverty is a crucial and unsolved issue in the field of social science. This work aims to measure poverty as a multidimensional notion using a new composite indicator. However, subjective choices as different weighting schemes on the indicator's construction could...

In this paper, we propose two practicable approaches for consistently modelling the realworld and risk-neutral measures within cross-asset Monte-Carlo frameworks. We go on to explore the necessity of supporting the real-world measure and consider its calibration with the aid of an explicit...

This paper tests whether the economic interpretation of risk contributions, as measure by marginal change in volatility, is true when accounting for fat tails in the asset return distributions (Qian, 2006). This is relevant to portfolio managers that have an expectation that risk and loss...

A new algorithm for calibrating agent-based models is proposed, which employs a popular gradient boosting framework. Machine learning techniques are not used to develop a surrogate model, but rather assist in narrowing down the parameter space during the search for optimal parameters. Our...

This paper sets forth a novel approach to calculate the levelized cost of electricity (LCOE) using a probabilistic model that accounts for endogenous input parameters. The approach is applied to the example of a nuclear and gas power project. Monte Carlo simulation results show that a...

This paper will outline the functionality available in the CovRegpy package for actuarial practitioners, wealth managers, fund managers, and portfolio analysts written in Python 3.7. The major contributions of CovRegpy can be found in the CovRegpy_DCC.py, CovRegpy_IFF.py, CovRegpy_RCR.py,...