Pricing of guaranteed minimum withdrawal benefits in variable annuities under stochastic volatility,stochastic interest rates and stochastic mortality via the componentwise splitting method

This paper values guaranteed minimum withdrawal benefit (GMWB) riders embedded in variable annuities assuming that the underlying fund dynamics evolve under the influence of stochastic interest rates, stochastic volatility, stochastic mortality and equity risk. The valuation problem is formulated as a partial differential equation (PDE) which is solved numerically by employing the operator splitting method. Sensitivity analysis of the fair guarantee fee is performed with respect to various model parameters. We find that (i) the fair insurance fee charged by the product provider is an increasing function of the withdrawal rate; (ii) the GMWB price is higher when stochastic interest rates and volatility are incorporated in the model, compared to the case of static interest rates and volatility; (iii) the GMWB price behaves non-monotonically with changing volatility of variance parameter; (iv) the fair fee increases with increasing volatility of interest rates parameter, and increasing correlation between the underlying fund and the interest rates; (v) the fair fee increases when the speed of mean-reversion of stochastic volatility or the average long-term volatility increases; (vi) the GMWB fee decreases when the speed of mean-reversion of stochastic interest rates or the average long-term interest rates increase. We investigate both static and dynamic (optimal) policyholder's withdrawal behaviours; we present the optimal withdrawal schedule as a function of the withdrawal account and the investment account for varying volatility and interest rates. When incorporating stochastic mortality, we find that its impact on the fair guarantee fee is rather small. Our results demonstrate the importance of correct quantification of risks embedded in GMWBs and provide guidance to product providers on optimal hedging of various risks associated with the contract.     

Best friend or broken tool? Exploring the co-existence of humans and artificial intelligence in the workplace ecosystem

Artificial intelligence (AI) has become an important topic in business literature and strategy talk. Yet, much of this literature is normative and conceptual in nature. How organizational members perceive AI and the job role changes that come with it is, so far, largely unknown territory for both HR scholars and practitioners. We sought to investigate the relationship between humans and AI and conducted an in-depth exploratory study into the co-existence of humans and two early-stage AI-solutions, one for “low-status” automation and another for “high-status”; augmentation. We suggest that different organizational groups may engage in distinctly different sensemaking processes regarding AI, an important insight for successful HRM strategies when AI is being introduced into the workplace. Moreover, contrary to recent conceptual work, our findings indicate that AI-enabled automation and augmentation solutions may not be detached from nor exist in tension with each other. They are deeply embedded in organizational processes and workflows for which people who co-exist with the technologies must take ownership. Our findings, in part, go against discussions on AI “taking over” jobs or deskilling humans. We describe a more nuanced version of reality fluctuating around the various ways different organizational groups encounter different AI-solutions in their daily work. Finally, our study warns against unconditional technological enthusiasm, managerial ignorance of the nature of work that employees undertake in different organizational groups, and a neglect of the time and effort required to successfully implement AI-solutions that affect not only the home organization but also members of the broader ecosystem.     

Bailouts and the modeling of bank distress

In this article, we develop a model for predicting distress events among large banks. We show that a bailout possibility induces different behaviors among small and large banks, and the proposed failure prediction model for large banks is thus considerably different from that for small banks. Major bank-level fundamentals show opposite conjecture directions for large versus small banks. The Tier 1 capital ratio, which is under the scrutiny of regulators and investors, has almost no distress prediction power among large banks. However, banks rescued by governments tend to maintain a lower Tier 1 ratio. The cost of funding in large banks is negatively correlated with the probability of failure, reflecting the fact that lenders internalize the too-big-to-fail (TBTF) policy and demand a lower interest rate from TBTF banks.     

Dispersion in Dispersion: Measuring Establishment-Level Differences in Productivity

We describe new experimental productivity dispersion statistics, Dispersion Statistics on Productivity (DiSP), jointly produced by the Bureau of Labor Statistics (BLS) and the Census Bureau, that complement the official BLS industry-level productivity statistics. The BLS has a long history of producing industry-level productivity statistics, which represent the average establishment-level productivity within industries when appropriately weighted. These statistics cannot, however, tell us about the variation in productivity levels across establishments within those industries. Dispersion in productivity across businesses can provide information about the nature of competition and frictions within sectors and the sources of rising wage inequality across businesses. DiSP data show enormous differences in productivity across establishments within industries in the manufacturing sector. We find substantial variation in dispersion across industries, increasing dispersion from 1997 to 2016, and countercyclical total factor productivity dispersion. We hope DiSP will enable further research into understanding productivity differences across industries and establishments and over time.     

Device-mediated customer behaviour on the internet: A systematic literature review

The proliferation of smartphones, tablets and other digital devices in addition to traditional computers has transformed the Internet into a device-mediated environment. While these devices provide immediate access to similar Internet sources, they differ significantly in their characteristics, such as screen size, operation mode and context of use. As a consequence, behaviours on the Internet along the customer journey vary substantially depending on the device used. To summarize the fast-growing body of research on device-mediated customer behaviour, a systematic, framework-based literature review of 59 articles from the last decade was conducted. Through an examination of the antecedents, decisions and outcomes investigated in the publications, the review presents a conceptual framework that highlights the relation between device characteristics, decision processes and behavioural outcomes. The review further summarizes the theories, contexts and methods employed in the studies and sets an extensive future research agenda. We found that the extant literature lacks comprehensive theories and clear definitions of digital devices in the omnichannel environment. Furthermore, existing findings should be generalized for other contexts (e.g. industries and countries) and validated via the introduction of other research designs and methods. The understanding of device-mediated behaviour and the consequently arising marketing measures remains scarce. Thus, this review advances the comprehension of customer behaviour on the Internet and provides researchers and practitioners with information on the implications for customer experience and omnichannel management.