Automatic differentiation (AD) is a popular method of computing exact derivatives in complex sensitivity analyses and optimisation routines that lack closed-form solutions, posing challenges for analytical and numerical alternatives. This paper introduces a vectorised version of AD that builds...

Statistical and computational methods are being increasingly integrated into Decision Support Systems to aid management and help with strategic decisions. Researchers need to fully understand the use of such techniques in order to make predictions when using financial data. This paper therefore...

The capture ratio is a widely-used investment performance measure. We study the statistical problem of estimating the capture ratio based on a finite number of observations of a fund's returns. We derive the asymptotic distribution of the estimator and use it for testing whether one fund has a...

Although harmonised soil profile data are available, no attempt has been made to comprehensively map soil depth to bedrock across Sri Lanka. In this paper we describe the development of a digital soil depth map for Sri Lanka using a soil profile dataset and an extensive array of environmental...

Inflation expectations are central to economics because they affect the effectiveness of fiscal and monetary policy as well as realized inflation. We survey the recent literature with a focus on the inflation expectations of households. We first review standard data sources and discuss their...

Inflation expectations are central to economics because they affect the effectiveness of fiscal and monetary policy as well as realized inflation. We survey the recent literature with a focus on the inflation expectations of households. We first review standard data sources and discuss their...

Many researchers investigate the sharing economy effect using panel regressions that assume a constant coefficient over space and time, but without testing whether this is the case. Using the Airbnb data in Barron et al. (2021), we find that the logarithms of both predictor and response...

The paper presents a decision-making rule, based on statistical learning techniques, to evaluate and monitor the overall quality of the granular dataset referring to the Non-Performing Loans data collection carried out by the Bank of Italy. The datasets submitted by the reporting agents must...

In this work, we develop a multi-fidelity Bayesian experimental design framework to efficiently quantify the extreme-event statistics of an input-to-response (ItR) system with given input probability and expensive function evaluations. The key idea here is to leverage low-fidelity samples whose...

Often, model development starts with missing data treatment. For regulatory internal rating based (IRB) models, missing data raise data quality concerns around system and process whereas the randomness nature of missing is sometimes overlooked, resulting inappropriate choice of imputation...