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Purpose This study aims to examine Millennials and generational differences in online gambling activity by comparing online gambling behavior across four different generations: Silent Generation, Baby Boomers, Gen Xers and Millennials. Design/methodology/approach The sample comprised tracked gambling data at the individual player level provided by an online casino accepting real money wagers in a major US gambling market. Attributes of gambling behavior were examined and compared across different generations using Kruskal–Wallis test and pairwise comparisons. Findings Generational differences were observed in 13 of the 16 behavioral variables. Millennials spent the least amount of time on gambling and exhibited the lowest scores on the number of days for slot gambling, trip length and trip frequency among all generations. However, their average table gaming volume per play day was greater than those of other generations. Practical implications The results of this study provide a better understanding of the generational differences in online gambling behavior. They also help casino operators and gaming machine manufacturers develop casino games and products that can appeal to different generational groups in the online gambling market. Originality/value Despite the on-going industry discussion about Millennials and their potential influence on the online gambling market, there appears to be a paucity of empirical research on the online gambling behavior of the Millennial generation. This study fills that gap in empirical evidence, addressing generational differences in online gambling.

Stress testing has become an important component of macroprudential regulation yet its goals and implementation are still being debated, reflecting the difficulty of designing such frameworks in the context of enormous model uncertainty. We illustrate methods for responding to possible misspecifications in models used for assessing bank vulnerabilities. We show how 'exponential tilting' allows the incorporation of external judgment, captured in moment conditions, into a forecasting model as a partial correction for misspecification. We also make use of methods from robust control to seek the most relevant dimensions in which a regulator's forecasting model might be misspecified -a search for a 'worst case' model that is a 'twisted' version of the regulator's initial forecasting model. Finally, we show how the two approaches can be blended so that one can search for a worst case model subject to restrictions on its properties, informed by the regulator's judgment. We demonstrate the methods using the New York Fed's CLASS model, a top-down capital stress testing framework that projects the effect of macroeconomic scenarios on U.S. banking firms.$ The authors thank

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