Bitcoin price and its marginal cost of production: support for a fundamental value

This study back-tests a marginal cost of production model proposed to value the digital currency Bitcoin. Results from both conventional regression and vector autoregression (VAR) models show that the marginal cost of production plays an important role in explaining Bitcoin prices, challenging recent allegations that Bitcoins are essentially worthless. Even with markets pricing Bitcoin in the thousands of dollars each, the valuation model seems robust. The data show that a price bubble that began in the Fall of 2017 resolved itself in early 2018, converging with the marginal cost model. This suggests that while bubbles may appear in the Bitcoin market, prices will tend to this bound and not collapse to zero.     

State‐Dependent Forward Guidance and the Problem of Inconsistent Announcements

Forward guidance can be provided as an unconditional promise, i.e. commitment to a specific low policy rate. Alternatively, the promise may include an escape clause, i.e. a condition defining the state of the economy under which the central bank would not keep such a low rate and, instead, it would revert to setting policy under discretion. The escape clause can be expressed as a threshold in terms of a specific variable. The present paper shows that, when such a threshold is expressed in terms of an endogenous variable (e.g. output, inflation), there are cases where it becomes impossible for the central bank to act in a way that is consistent with its promise. Consistency imposes limits on the policy rate that can be set since reverting immediately to the optimal discretionary rate can be incompatible with exceeding the threshold.     

Investigating the diversifying or hedging nexus of cannabis cryptocurrencies with major digital currencies

Decisions in Economics and Finance - This paper sets out to explore the nexus between cryptocurrencies connected to cannabis production and the three highest capitalization digital currencies....     

A new take on voice: the influence of BlackRock’s ‘Dear CEO’ letters

Review of Accounting Studies - Abstract We examine whether broad-based public engagement by institutional investors influences the behavior of portfolio firms. We investigate this question in the...     

Time series copulas for heteroskedastic data

We propose parametric copulas that capture serial dependence in stationary heteroskedastic time series. We suggest copulas for first‐order Markov series, and then extend them to higher orders and multivariate series. We derive the copula of a volatility proxy, based on which we propose new measures of volatility dependence, including co‐movement and spillover in multivariate series. In general, these depend upon the marginal distributions of the series. Using exchange rate returns, we show that the resulting copula models can capture their marginal distributions more accurately than univariate and multivariate generalized autoregressive conditional heteroskedasticity models, and produce more accurate value‐at‐risk forecasts.     

Reciprocal value sharing in manufacturer-retailer relationships: the case of new product introductions

Prior literature examined reciprocity in the context of value creation. However, research has yet to examine whether reciprocity exists in value sharing. To address this gap, the authors examine retailer’s reciprocal value sharing with its manufacturer in relation to new product introductions. The authors test, via a survey of retail managers, whether reciprocal value sharing is influenced by an interaction of manufacturer’s prior new product success with innovativeness of a manufacturer’s products and the frequency of new product introduction. The results indicate that a retailer’s reciprocal value sharing is greater when the manufacturer historically launched successful new products, and that this effect is decreased with the innovativeness of a manufacturer’s products but increased with the frequency of new product introduction.     

Gamified interactions: whether,when, and how games facilitate self–brand connections

Firms increasingly use games to interact with their customers. Yet, surprisingly little is known about whether, when, and how such “gamified” interactions engage consumers with a firm’s brand, thereby facilitating self–brand connections. Building on flow theory, we show that gamified interactions that are highly interactive and optimally challenging facilitate self–brand connections, because such games lead to emotional and cognitive brand engagement. A field study and three experiments across various product domains and game designs support our theory. We also identify conditions under which consumers do not become engaged with a brand, namely when firms restrict their decisional control either to voluntarily participate in the game (i.e., compulsory play) or to spend as much time as desired playing the game (i.e., time pressure). Our findings advance existing knowledge about the use of games in marketing and provide important implications for how marketers can harness their potential to build self–brand connections.     

Policy capturing in small‐group decision making: Using linear programming to assign factor weights for the NCAA Selection Committee's ranking decisions in the 2016 through 2018 men's basketball tournaments

We present a general approach for applying policy capturing to small‐group decision making and demonstrate the approach with the NCAA Selection Committee's rankings of the 68 teams in the three most recent Division I Men's Basketball Tournaments. We develop a linear programming model that minimizes the extent to which the evaluation of a lower‐ranked team exceeds that of a team ranked immediately above it. The result is a set of linear weights that approximates a group decision when the decision itself is demonstrably internally inconsistent in that a group ranks some teams higher than others despite a lower implied evaluation.