
Montserrat GuillenUniversity of Barcelona | UB · Department of Econometrics, Statistics and Spanish Economy
Montserrat Guillen
PhD Econ, MSc Maths, MA Data A
About
362
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Introduction
Past President of the European Group of Risk and Insurance Economists
Research Director of Riskcenter at UB
Additional affiliations
January 2008 - present
January 2008 - present
January 2006 - present
Publications
Publications (362)
Telematics devices have transformed driver risk assessment, allowing insurers to tailor premiums based on detailed evaluations of driving habits. However, integrating Advanced Driver Assistance Systems (ADAS) and contextualized geolocation data for predictive improvements remains underexplored due to the recent emergence of these technologies. This...
This study introduces a novel method for driving risk assessment based on the analysis of near-miss events captured in telematics data. Near-miss events, which are highly correlated with accidents, are employed as proxies for accident prediction. This research employs histogram-based gradient boosting regressors (HGBRs) for the analysis of telemati...
The present research provides two methodological advances, simulation evidence and a real data analysis, all contributing to the area of local linear survival function estimation and bandwidth selection. The first contribution is the development of a double smoothed local linear survival function estimator which admits an arbitrary number of covari...
Purpose
The Surprise Question (SQ) "Would you be surprised if the patient died in the next 12 months?" lacks pediatric research, particularly in neonatal patients. Our study aims to analyze the SQ’s predictive ability in neonates and explore pediatricians’ views on palliative care (PC) patient identification.
Methods
A prospective cross-sectional s...
Telematics boxes integrated into vehicles are instrumental in capturing driving data encompassing behavioral and contextual information, including speed, distance travelled by road type, and time of day. These data can be amalgamated with drivers' individual attributes and reported accident occurrences to their respective insurance providers. Our s...
Children with palliative needs present physical and psychological symptoms and it is important to be aware of their self-perception to improve their quality of life.
Purpose: Determine the predictive value of sociodemographic, disease and psychological variables in relation to the self-perceived Quality of Life (pQoL) of children with limiting and...
Este artículo estudia qué factores influyen en la aparición de incertidumbre económica subjetiva y cuáles son las expectativas económicas de las personas. Se analiza el impacto de la pandemia sobre la economía doméstica e incertidumbre subjetiva a partir de un conjunto de factores socioeconómicos no observables en los estudios macroeconómicos sobre...
The patient’s perspective is an essential component of understanding the individual experience of suffering in children with palliative needs, but it is a perspective that is often overlooked. The aim of this study was to compare the perception of quality of life (QoL) of children with life-limiting and life-threatening conditions expressed by the...
Home and leisure accidents (HLAs) are one of the main causes of mortality attributable to causes other than aging and Home and leisure accidents (HLAs) are one of the main causes of mortality attributable to causes other than aging and have a major impact on health systems. However, to date, studies measuring their socioeconomic impact are limited,...
The patient's perspective is an essential component of understanding the individual experience of suffering in children with palliative needs, but it is a perspective that is often overlooked. Purpose: the aim of this study was to compare the perception of quality of life (QoL) of children with life-limiting and life-threatening conditions expresse...
Home and leisure accidents (HLAs) are one of the main causes of mortality attributable to causes other than aging and have a major impact on health systems. However, to date, studies measuring their socioeconomic impact are limited, unlike those associated with other causes such as accidents on the roads or in the workplace. We seek to analyze the...
The capital allocation framework presents capital allocation principles as solutions to particular optimisation problems and provides a general solution of the quadratic allocation problem via a geometric proof. However, the widely used haircut allocation principle is not reconcilable with that optimisation setting. Our study complements and genera...
The capital allocation framework proposed by [] presents capital allocation principles as solutions to particular optimization problems and provides a general solution of the quadratic allocation problem via a geometric proof. However, the widely used haircut allocation principle is not reconcilable with that optimization setting. In this paper we...
The global energy transition to low-carbon technologies for transportation is heavily dependent on lithium. By leveraging advances in time-series econometrics we show that lithium prices (carbonate and hydroxide) have recently experienced market explosive behaviors, particularly from 2016 to mid-2018, and in most lithium markets also from October 2...
Objectives:
Our research aims to compare the perception that children in the pediatric palliative care setting have of their emotional well-being, or that expressed by the parents, with the perception held by the professionals involved in their care.
Methods:
In this cross-sectional study, the emotional well-being of 30 children with a mean age...
In this paper, we summarize and analyze the relevant research on the cash management problem appearing in the literature. First, we identify the main dimensions of the cash management problem. Next, we review the most relevant contributions in this field and present a multidimensional analysis of these contributions, according to the dimensions of...
Risk analysis in motor insurance aims to identify factors that increase the frequency of accidents. Telematics data is used to measure behavioural information of drivers. Contextual variables include temperature, rain, wind and traffic conditions that are external to the driver, but may also influence the probability of having an accident, as well...
Usage-based insurance has allowed insurers to dynamically tailor insurance premiums by understanding when and how safe policyholders drive. However, telematics information can also be used to understand the driving contexts experienced by the driver within each trip (e.g., road types, weather, traffic). Since different combinations of these conditi...
Risks will soon celebrate its tenth anniversary [...]
The literature suggests that the country in which a company is listed, i.e., its country membership, is the main determinant of its price volatility co-movements in the global stock market but, at the same time, this body of literature also recognizes the relevance of industry sector membership for understanding risk transmission. Drawing on recent...
A loss of the ability to buy and prepare meals, especially in people aged 65 and over, leads to a deterioration in their optimal level of nutrition. The Index of Autonomy in Food Acquisition (IAFA) was used to identify contributing factors. This is a composite indicator for shopping and meal preparation that can be used to assess the degree of auto...
Evaluating value at risk (VaR) for a firm’s returns during periods of financial turmoil is a challenging task because of the high volatility in the market. We propose estimating conditional VaR and expected shortfall (ES) for a given firm’s returns using quantile regression with cross-sectional (CSQR) data about other firms operating in the same ma...
Background
Bed occupancy in the ICU is a major constraint to in-patient care during COVID-19 pandemic. Diagnoses of acute respiratory infection (ARI) by general practitioners have not previously been investigated as an early warning indicator of ICU occupancy.
Methods
A population-based central health care system registry in the autonomous communi...
These are the data used in manuscript "Acute Respiratory Infection Rates in Primary Care Anticipate ICU Bed Occupancy During COVID-19 Waves"
These are the data used in manuscript "Acute Respiratory Infection Rates in Primary Care Anticipate ICU Bed Occupancy During COVID-19 Waves"
Quantile regression provides a way to estimate a driver’s risk of a traffic accident by means of predicting the percentile of observed distance driven above the legal speed limits over a one year time interval, conditional on some given characteristics such as total distance driven, age, gender, percent of urban zone driving and night time driving....
We propose a spillover index of external connectedness that measures the outer influences among countries from estimated return volatilities of 645 European firms. We find that Gross Domestic Product per capita is directly related to this index, as countries with lower Gross Domestic Product per capita are influenced in a greater way than they infl...
While insurance was originally devised as a safety net that steps in to compensate for financial losses after an accident
has occurred, the information generated by sensors and digital devices now offers insurance companies the opportunity
to transform their business by considering prevention. We discuss a new form of risk analytics based on big da...
The dataset tracks 40,284 insurance clients over five years, between 2010 and 2015, who subscribed to both automobile and homeowners insurance. We have combined information on these customers. First, the characteristics including age, gender or driving experience, among others and dates of renewal for the two types of policies considered here. Note...
The covariance allocation principle is one of the most widely used capital allocation principles in practice. Risks change over time, so capital risk allocations should be time-dependent. In this paper, we propose a dynamic covariance capital allocation principle based on the variance-covariance of risks that change over time. The conditional corre...
Financial services industries, such as insurance, increasingly use data from their broad cross-section of customers and follow these customers over time. In other areas such as medicine, engineering, and communication systems, it is well known that following subjects over time may result in biased data, for example, the so-called ”dropout effect”....
This study proposes a method for identifying and evaluating driving risk as a first step towards calculating premiums in the newly emerging context of usage-based insurance. Telematics data gathered by the Internet of Vehicles (IoV) contain a large number of near-miss events which can be regarded as an alternative for modeling claims or accidents f...
In the hotel sector, online reputation and customer satisfaction help measure the quality of service based on the opinions of the survey participants. This research takes information provided by TripAdvisor from a sample of 247 hotels in Barcelona in order to obtain users’ reaction to each establishment. A robust compositional regression is modelle...
The Living Conditions Survey of Ecuador contains a count variable measuring the subjective happiness of respondents. Two machine learning models are implemented to predict the level of happiness as a function of economic security among other factors. Even if the predictive performance is low, due to the fact that individuals tend to polarize extrem...
We present a method to integrate telematics data in a pay‐how‐you‐drive insurance pricing scheme that penalizes some near‐miss events. We illustrate our method with a sample of drivers for whom information on near‐miss events and claims frequency records are available. We discuss the implications for motor insurance ratemaking. Our pricing principl...
This paper studies the incorporation of an explicit fee in modern tontine schemes and investigates how it affects the attractiveness of these products from the point of view of both the insurer and tontine participants. We consider a single initial fee and a variable fee, where the latter can be designed to meet different liquidity needs and risk a...
A new method to estimate longevity risk based on the kernel estimation of the extreme quantiles of truncated age-at-death distributions is proposed. Its theoretical properties are presented and a simulation study is reported. The flexible yet accurate estimation of extreme quantiles of age-at-death conditional on having survived a certain age is fu...
A boosting-based machine learning algorithm is presented to model a binary response with large imbalance, i.e., a rare event. The new method (i) reduces the prediction error of the rare class, and (ii) approximates an econometric model that allows interpretability. RiskLogitboost regression includes a weighting mechanism that oversamples or undersa...
Background
The study assesses the prevalence rates of alcohol- and drug-involved driving in Catalonia (Spain).
Method
Drivers were randomly selected for roadside testing using a stratified random sampling procedure representative of all vehicles circulating on non-urban roads. Mandatory alcohol and drug tests were performed during autumn 2017. A s...
Based on recent developments in joint regression models for quantile and expected shortfall, this paper seeks to develop models to analyse the risk in the right tail of the distribution of non-negative dependent random variables. We propose an algorithm to estimate conditional tail expectation regressions, introducing generalized risk regression mo...
Reference charts are widely used as a graphical tool for assessing and monitoring children’s growth given gender and age. Here, we propose a similar approach to the assessment of driving risk. Based on telematics data, and using quantile regression models, our methodology estimates the percentiles of the distance driven at speeds above the legal li...
Most classical econometric methods and tree boosting based algorithms tend to increase the prediction error with binary imbalanced data. We propose a synthetic penalized logitboost based on weighting corrections. The procedure (i) improves the prediction performance under the phenomenon in question, (ii) allows interpretability since coefficients c...
The general beta of the second kind distribution (GB2) is a flexible distribution which includes several relevant parametric families of distributions. This distribution has important applications in earnings and income distributions, finance and insurance. In this paper, several multivariate classes of the GB2 distribution are proposed. The differ...
We estimate generalized market uncertainty indicators for the stock markets of eight European countries greatly affected by the recent Covid-19 crisis and the economic measures implemented for its containment and mitigation. Our statistics emphasize the difference between risk and uncertainty, in the aggregate, and provide readily and easily interp...
Background: The Beta distribution is useful for fitting variables that measure a probability or a relative frequency. Methods: We propose a Sarmanov distribution with Beta marginals specified as generalised linear models. We analyse its theoretical properties and its dependence limits. Results: We use a real motor insurance sample of drivers and an...
The paper studies the incorporation of an explicit fee in modern tontine schemes and investigates how it affects the attractiveness of these products, both from the insurer's and tontine participants' viewpoint. We consider a single initial fee, and a variable fee, where the latter can be designed to meet different liquidity needs and risk aversion...
Population aging in most industrialized societies has led to a dramatic increase in emergency medical demand among the elderly. In the context of private health care, an optimal allocation of the medical resources for seniors is commonly done by forecasting their life spans. Accounting for each subject's particularities is therefore indispensable,...
In this work, we establish a methodological framework to analyze the care demand for elderly citizens in any area with a large proportion of elderly population, and to find connections to the cumulative incidence of COVID-19. Thanks to this analysis, it is possible to detect deficiencies in the public elderly care system, identify the most disadvan...
Marketers are faced with the daunting challenge of identifying insights anddelivering the right combination of online and offline tactics to engageconsumers at various stages along the consumer journey. In this paper, weinvestigate the effects of retargeting in a multichannel environment. Using athree-stage modeling approach, we find retargeting is...
Driving data record information on style and patterns of vehicles that are inmotion. These data are analysed to obtain risk scores that can later beimplemented in insurance pricing schemes. Scores may also be used in on-board sensors to create risk alerts that help drivers to keep up with safetymargins. Regression methods are proposed and a prototy...
We empirically study market integration and the propagation of shocks in the interconnected market of Nord Pool. We document an increasing trend towards market integration over recent decades in Nord Pool and identify clear cycles accounting for greater integration (larger transmission of shocks) in the cold seasons. Greater market integration perm...
With the major advances made in internet of vehicles (IoV) technology in recent years, usage-based insurance (UBI) products have emerged to meet market needs. Such products, however, critically depend on driving risk identification and driver classification. Here, ordinary least square and binary logistic regressions are used to calculate a driving...
In this chapter, we discuss the goodness of fit in the context of quantile regression. We present the coefficient of determination and its adaptation to the quantile regression context. We illustrate how to calculate this goodness of fit statistic and how to interpret it using data for energy consumption, its determinants and electricity prices.
In this chapter, we describe three recent lines of research in quantile regression, the first one is rather an extension of nonparametric regression into the quantile regression framework. The second topics are an extension for time series quantile regressions of the traditional time series tool, the cross-correlogram. The third possible extension...
In this chapter, we explore a topic that has gained considerable attention in the academic literature during the latter years, namely quantile regression for time series data. We will illustrate how to use conditional quantile regression to model the dynamics of electricity prices as a function of the price of an input and also substitute energy co...
In this chapter, we conclude and we emphasize that quantile regression is suitable for predictive modeling when the response is asymmetric or non-normally distributed conditional on the covariates. We note that usually, the model aims at the lower or upper percentiles of the response.
In this chapter, the main methodological concepts related to quantile regression are described. We provide the definition of conditional and unconditional quantiles and present the minimization problem with asymmetric loss that underlies the quantile estimation via quantile regressions. Additionally, weighted quantile regression tools that will be...
In this chapter, we illustrate the classical problem of quantile regression for cross-sectional data. We develop one practical exercise using R. We visualize and interpret our results, where quantile regression is used to explain which factors affect excess consumption of electricity by a sample of US households with different characteristics. Thes...
Quantile regression is a way to disclose predictive relationships between a response variable and some regressors or explanatory variables when the interest is to find a causal link beyond the mean-to-mean effects. Quantile regression is a procedure to model the cut points of the cumulated conditional probability distribution of a response variable...
Quantile regression is a potent tool to analyze frequently found issues in economics and finance, such as the identification of consumption and production determinants and their potential impacts on demand and supply decisions, or the dynamics of prices that are featured by seasonality and other stylized facts that complicate traditional empirical...
Recent reforms of public pension systems implemented in various countries, including Spain, have sought to ensure the sustainability of benefits. However, achieving a structural budget balance (i.e. long-run equality between income and expenditure) may result in a decrease in the replacement rate or in the percentage that the pension represents of...
Demographic changes and social transformations affect the design of public pension systems. Population ageing and factors such as the progressive reduction in fertility rates and the increase in longevity have a direct impact on the sustainability of public pensions, especially in defined-benefit schemes. The study of longer life expectancy require...
We propose a logistic regression model combined with a weighting estimation procedure that incorporates a tuning parameter. We analyse predictive performance indicators. Results show that the parameter defining the weights can be used to improve predictive accuracy, at least when the original predictive value is distant from the response average. W...
This brief addresses the estimation of quantile regression models from a practical perspective, which will support researchers who need to use conditional quantile regression to measure economic relationships among a set of variables. It will also benefit students using the methodology for the first time, and practitioners at private or public orga...
Two aspects of the aging process in Latin America should be specially taken into account in order to evaluate future perspectives of morbidity among the elderly in the region: 1) Cohorts who will compose the bulk of the elderly population in the 21st century in Latin America survived to old age largely because of improvements in medicine and to a m...
Given a risk level or tolerance, quantile regression is a predictive model that fits the corresponding percentile of the continuous response variable. Given a fixed percentage value, we identify the effect of each predictor variable in the cumulative distribution up to that level of the dependent variable. In this article, we show how this methodol...
Straightforward methods to evaluate risks arising from several sources are specially difficult when risk components are dependent and, even more if that dependence is strong in the tails. We give an explicit analytical expression for the probability distribution of the sum of non-negative losses that are tail-dependent. Our model allows dependence...
Sometimes one needs to classify individuals into groups, but there is no available grouping information due to social desirability bias in reporting behavior like unethical or dishonest intentions or unlawful actions. Assessing hard-to-detect behaviors is useful; however it is methodologically difficult because people are unlikely to self-disclose...
Telematics data from usage-based motor insurance provide valuable information – including vehicle usage, attitude toward speeding, and time and proportion of urban/nonurban driving, which can be used for ratemaking. Additional information on acceleration, braking, and cornering can likewise be usefully employed to identify near-miss events, a conce...
We analyzed real telematics information for a sample of drivers with usage-based insurance policies. We examined the statistical distribution of distance driven above the posted speed limit—which presents a strong positive asymmetry—using quantile regression models. We found that, at different percentile levels, the distance driven at speeds above...
XGBoost is recognized as an algorithm with exceptional predictive capacity. Models for a binary response indicating the existence of accident claims versus no claims can be used to identify the determinants of traffic accidents. This study compared the relative performances of logistic regression and XGBoost approaches for predicting the existence...
We analyze real telematics information for a sample of drivers with usage-based insurance policies. We examine the statistical distribution of distance driven above the posted speed limit – which presents a strong positive asymmetry – using quantile regression models. We find that, at different percentile levels, the distance driven at speeds above...