2,397 research outputs found
Impacts of extreme weather events on mortgage risks and their evolution under climate change:A case study on Florida
International audienceWe develop an additive Cox proportional hazard model with time-varying covariates, including spatio-temporal characteristics of weather events, to study the impact of weather extremes (heavy rains and tropical cyclones) on the probability of mortgage default and prepayment. We compare the survival model with a flexible logistic model and an extreme gradient boosting algorithm. We estimate the models on a portfolio of mortgages in Florida, consisting of 69,046 loans and 3,707,831 loan-month observations with localization data at the five-digit ZIP code level. We find a statistically significant and non-linear impact of tropical cyclone intensity on default as well as a significant impact of heavy rains in areas with large exposure to flood risks. These findings confirm existing results in the literature and also provide estimates of the impact of the extreme event characteristics on mortgage risk, e.g. the impact of tropical cyclones on default more than doubles in magnitude when moving from a hurricane of category two to a hurricane of category three or more. We build on the identified effect of exposure to flood risk (in interaction with heavy rainfall) on mortgage default to perform a scenario analysis of the future impacts of climate change using the First Street flood model, which provides projections of exposure to floods in 2050 under RCP 4.5. We find a systematic increase in risk under climate change that can vary based on the scenario of extreme events considered. Climate-adjusted credit risk allows risk managers to better evaluate the impact of climate-related risks on mortgage portfolios
Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain
The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio
Environmental variables and machine learning models to predict cetacean abundance in the Central-eastern Mediterranean Sea
: Although the Mediterranean Sea is a crucial hotspot in marine biodiversity, it has been threatened by numerous anthropogenic pressures. As flagship species, Cetaceans are exposed to those anthropogenic impacts and global changes. Assessing their conservation status becomes strategic to set effective management plans. The aim of this paper is to understand the habitat requirements of cetaceans, exploiting the advantages of a machine-learning framework. To this end, 28 physical and biogeochemical variables were identified as environmental predictors related to the abundance of three odontocete species in the Northern Ionian Sea (Central-eastern Mediterranean Sea). In fact, habitat models were built using sighting data collected for striped dolphins Stenella coeruleoalba, common bottlenose dolphins Tursiops truncatus, and Risso's dolphins Grampus griseus between July 2009 and October 2021. Random Forest was a suitable machine learning algorithm for the cetacean abundance estimation. Nitrate, phytoplankton carbon biomass, temperature, and salinity were the most common influential predictors, followed by latitude, 3D-chlorophyll and density. The habitat models proposed here were validated using sighting data acquired during 2022 in the study area, confirming the good performance of the strategy. This study provides valuable information to support management decisions and conservation measures in the EU marine spatial planning context
Forecasting: theory and practice
Forecasting has always been in the forefront of decision making and planning.
The uncertainty that surrounds the future is both exciting and challenging,
with individuals and organisations seeking to minimise risks and maximise
utilities. The lack of a free-lunch theorem implies the need for a diverse set
of forecasting methods to tackle an array of applications. This unique article
provides a non-systematic review of the theory and the practice of forecasting.
We offer a wide range of theoretical, state-of-the-art models, methods,
principles, and approaches to prepare, produce, organise, and evaluate
forecasts. We then demonstrate how such theoretical concepts are applied in a
variety of real-life contexts, including operations, economics, finance,
energy, environment, and social good. We do not claim that this review is an
exhaustive list of methods and applications. The list was compiled based on the
expertise and interests of the authors. However, we wish that our encyclopedic
presentation will offer a point of reference for the rich work that has been
undertaken over the last decades, with some key insights for the future of the
forecasting theory and practice
Co-morbidity of childhood anaemia and malaria with a district-level spatial effect.
Doctoral Degree. University of KwaZulu-Natal, Durban.Anaemia and malaria are the leading causes of sub-Saharan African childhood morbidity
and mortality. This thesis aimed to explore the risk factors as well as the
complex relationship between anaemia and malaria in young children across the districts
or counties of four contiguous sub-Saharan African countries, namely Kenya,
Malawi, Tanzania and Uganda. Nationally representative data from the Demographic
and Health Surveys conducted in all four countries was used. The observed
prevalence of anaemia and malaria was 52.5% and 19.7%, respectively, with
a 15.1% prevalence of co-infection. Machine learning based exploratory classification
methods were used to gain insight into the relationships and patterns among
the explanatory variables and the two responses. The administrative districts are
the level at which public health decisions are made within each of the countries.
Accordingly, the best linear unbiased predictor (BLUP) ranking and selection approach
was adopted to investigate the district-level spatial effects, while controlling
for child-level, household-level and environmental factors. Further to the geoadditive
model, a generalised additive mixed model with a spatial effect based on the geographical
coordinates of the sampled clusters within the districts was applied. The
relationship between the two diseases was further explored using joint modelling
approaches: a bivariate copula geoadditive model and shared component model.
The child’s age, mother’s education level, household wealth index and cluster altitude
were found to be significantly associated with both the anaemia and malaria
status of the child. The results of this study can help policy makers target the correct
set of interventions or prevent the use of incorrect interventions for anaemia and
malaria control and prevention. This aids in the targeted allocation of limited district
health system resources within each of these countries.Author's Keywords: Adjusted odds ratios; Bayesian inference; Best linear unbiased predictor;
Classification methods; Conditional autoregression; Copula model; Geoadditive
model; Joint modelling; Spatial autocorrelation; Spline smoothing; Structured
spatial effect; Unstructured spatial effect.
Author's Publications listed on page 132-136 of thesis
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