When analysing the volatility related to high frequency financial data, mostly non-parametric approaches based on realised or bipower variation are applied. This article instead starts from a continuous time diffusion model and derives a parametric analog at high frequency for it, allowing...

We consider the effect of adaptive model selection and regularization by agents on price volatility and market stability in a simple agent-based model of a financial market. The agents base their trading behavior on forecasts of future returns, which they update adaptively and asynchronously...

Accurately classifying products is essential in international trade. Virtually all countries categorize products into tariff lines using the Harmonized System (HS) nomenclature for both statistical and duty collection purposes. In this paper, we apply and assess several different algorithms to...

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...

Human decision-making differs due to variation in both incentives and available information. This generates substantial challenges for the evaluation of whether and how machine learning predictions can improve decision outcomes. We propose a framework that incorporates machine learning on...

We develop Residual MisPricing (RMP), an index capturing mispricing relative to a linear benchmark asset pricing model, from the structure imposed by no-arbitrage. RMP is fully conditional and depends only on the returns of basic assets. Return data for several economies reveal that RMP is...