We explore the taxonomy of cryptocurrencies and integrate our analysis with traditional ways of understanding financial assets. We thus classify cryptocurrencies using the time series and distributional properties of returns. Cryptocurrencies appear inherently speculative in nature. The result...

In this paper we develop a novel semi-nonparametric panel copula model with external covariates for the study of wage rank dynamics. We focus on nonlinear dependence between the current and lagged worker's ranks in the wage residuals distribution, conditionally on individual characteristics. We...

In density forecasts, respondents are asked to assign probabilities to pre-specifted ranges of inflation. We show in two large-scale experiments that responses vary when we modify the response scale. Asking an identical question with modifted response scales induces different answers: Shifting,...

We propose to treat survey-based density expectations as compositional data when testing either for heterogeneity in density forecasts across different groups of agents or for changes over time. Monte Carlo simulations show that the proposed test has more power relative to both a bootstrap...

This paper develops power series expansions of a general class of moment functions, including transition densities and option prices, of continuous-time Markov processes, including jump di⁄usions. The proposed expansions extend the ones in Kristensen and Mele (2011) to cover general Markov...

This paper offers a simple yet effective way of estimating the moments of a stock's return distribution. The methodology is based on quantile regression, which is able to effectively summarize a stock's return moments by using a rich set of information about different parts of the stock's return...

Pairs trading, a popular investing strategy, is often carried out using the meanreverting Ornstein-Uhlenbeck equation as a cornerstone. The random variables which determine the underlying stochastic process are customarily assumed to follow a normal distribution, despite the well-known fact that...

We examine the predictive value of expected skewness of oil returns for the realized volatility using monthly data from 1859:11 to 2023:04. We utilize a quantile predictive regression model, which is able to accommodate nonlinearity and structural breaks. In-sample results show that the...