Buy‐now‐pay‐later (BNPL) in Australia is a rapidly growing payment innovation. Regulators and consumer groups have expressed concerns at the financial risks posed by BNPL. As BNPL is not regulated under consumer credit law, financial regulator and consumer groups have recommended that BNPL users adopt a range of responsible financial behaviours for their financial wellbeing. This study, using a survey of BNPL users and structural equation modelling, shows a link between most of these recommended financially responsible behaviours and financial wellbeing and that the financial behaviours of younger users (aged under 25) place them at greater risk of reduced financial wellbeing.

Using a comprehensive range of metrics, this article determines how relative market and credit risk change among European sectors during extreme market fluctuations. Differences are found between conditional and nonconditional outcomes, and sectors which were most risky prior to the Global Financial Crisis (GFC) are found to be different to the riskiest sectors during the GFC. These findings are consistent across the metrics used. The insights into extreme sectoral risk are important to investors in portfolio selection and to banks in setting sectoral concentration limits.

The three main Value at Risk (VaR) methodologies are historical, parametric and Monte Carlo Simulation. Cheung & Powell (2012), using a step-by-step teaching study, showed how a nonparametric historical VaR model could be constructed using Excel, thus benefitting teachers and researchers by providing them with a readily useable teaching study and an inexpensive and flexible VaR modelling option. This article extends that work by demonstrating how parametric and Monte Carlo Simulation VaR models can also be constructed in Excel, thus providing a total Excel modelling package encompassing all three VaR methods.

This paper features an analysis of the effectiveness of a range of portfolio diversification strategies, with a focus on down-side risk metrics, as a portfolio diversification strategy in a European market context. We apply these measures to a set of daily arithmetically-compounded returns, in U.S. dollar terms, on a set of ten market indices representing the major European markets for a nine-year period from the beginning of 2005 to the end of 2013. The sample period, which incorporates the periods of both the Global Financial Crisis (GFC) and the subsequent European Debt Crisis (EDC), is a challenging one for the application of portfolio investment strategies. The analysis is undertaken via the examination of multiple investment strategies and a variety of hold-out periods and backtests. We commence by using four two-year estimation periods and a subsequent one-year investment hold out period, to analyse a naive 1/N diversification strategy and to contrast its effectiveness with Markowitz mean variance analysis with positive weights. Markowitz optimisation is then compared to various down-side investment optimisation strategies. We begin by comparing Markowitz with CVaR, and then proceed to evaluate the relative effectiveness of Markowitz with various draw-down strategies, utilising a series of backtests. Our results suggest that none of the more sophisticated optimisation strategies appear to dominate naive diversification.