Summary

Risk quantification is mainly present in economic activities related to finance and insurance, although its methodology is increasingly extended to a greater number of sectors within the framework of comprehensive risk management. This subproject studies how to analyze and predict risk in complex situations from the following points of view: 1) when there is a multivariate behavior and dependencies between the different sources of risk, 2) when analyzing the presence of uncertainty in the economic environment and / or whenever volatility should be calibrated, 3) in the presence of big data, that is, when data are varied, with a large volume and generated at high speed and 4) when risk prediction can bring benefits in decisions and in preventive actions. Methodological results with numerous applications in economics are obtained from the studies on multivariate risk quantification. The main objectives of this subproject are: 1) to establish methodologies in the specification, estimation and analysis of explanatory and predictive models of multivariate risk, as well as to define alternatives based on a generalization of quantile regression, 2) to improve quantitative risk analysis in applications related to financial markets, for example, in the valuation of financial assets at risk and in the identification of the connection between credit and liquidity risks in the public debt market, 3) to propose advances that are relevant in the field of insurance, specifically, for automobile usage-based insurance through telematic data or for pension systems, where the multidimensionality of risk comes from combining investment risk, longevity and loss of personal autonomy (functional or cognitive dependence). The implications in financial and insurance regulations are studied, in the directives of Basel III and Solvency II. In addition, predictive modeling is studied as a prescription tool, which gives importance to the use of quantitative methodology for the design of risk mitigation systems. The results have an impact on all economic sectors because comprehensive risk management systems are increasingly present in all companies. Likewise, the topic is of interest to all citizens at a microeconomic level, and in general, to all economic agents in decision making process that seek a balance between maximizing their expectations and minimizing the level of risk within acceptable margins.

Publications (2020-2022)

2022

  1. Andrada-Félix, J., Fernandez-Perez, A. and Fernández-Rodríguez, F., Sosvilla-Rivero, S. (2022) “Time connectedness of fear”. Empirical Economics, 62: 905–931. DOI: https://doi.org/10.1007/s00181-021-02056-w
  2. Andrada-Félix, J., Fernández-Rodríguez, F. and Sosvilla-Rivero, S. (2022) “Financial market analogies of the COVID-19 pandemic: evidence from the Dow Jones Industrial Average Index”. Applied Economics Letters, 1-6. DOI: https://doi.org/10.1080/13504851.2022.2097172
  3. Bermúdez, L. and Karlis, D. (2022) “Copula-based bivariate finite mixture regression models with an application for insurance claim count data”. Test. DOI: https://doi.org/10.1007/s11749-022-00814-1
  4. Bolancé, C., Acuña, C.A. and Torra, S. (2022) “Non-Normal Market Losses and Spatial Dependence Using Uncertainty Indices”. Mathematics, 10, 8, 1317. DOI: https://doi.org/10.3390/math10081317
  5. Gómez-Déniz, E., Pérez-Rodríguez, J. V. and Sosvilla-Rivero, S. (2022) “Analyzing How the Social Security Reserve Fund in Spain Affects the Sustainability of the Pension System”. Risks, 10(6), 120. DOI: https://doi.org/10.3390/risks10060120
  6. Gómez-Puig, M., Sosvilla-Rivero, S. and Martínez-Zarzoso, I. (2022) “On the heterogeneous link between public debt and economic growth”. Journal of International Financial Markets, Institutions and Money, 77, 101528. DOI: https://doi.org/10.1016/j.intfin.2022.101528
  7. Guillen, M., Robles, I.B., Cabrera, E.B., Roldán, X.A., Bolancé, C., Jorba, D. and Moriña, D. (2022) “Acute respiratory infection rates in primary care anticipate ICU bed occupancy during COVID-19 waves”. PLoS ONE, 17, 5 May, e0267428. DOI: https://doi.org/10.1371/journal.pone.0267428
  8. Pitarque, A. and Guillen M. (2022) ”Interpolation of Quantile Regression to Estimate Driver’s Risk of Traffic Accident Based on Excess Speed”. Risks, 10, 1, 19. DOI: https://doi.org/10.3390/risks10010019
  9. Santolino, M., Alcañiz, M. and Bolancé, C. (2022) ”Hospitalizations from covid-19: a health planning tool”. Revista de Saude publica, 56, 51. DOI: https://doi.org/10.11606/s1518-8787.2022056004315
  10. Vernic, R., Bolancé, C. and Alemany, R. (2022) ”Sarmanov distribution for modeling dependence between the frequency and the average severity of insurance claims”. Insurance: Mathematics and Economics, 102, 111-125. DOI: https://doi.org/10.1016/j.insmatheco.2021.12.001

2021

  1. Alcañiz, M., Guillen, M. and Santolino, M. (2021) “Differences in the risk profiles of drunk and drug drivers: Evidence from a mandatory roadside survey”. Accident Analysis & Prevention, 151, 105947. DOI:https://doi.org/10.1016/j.aap.2020.105947
  2. Andrada-Félix, J., Fernandez-Perez, A. and Sosvilla-Rivero, S. (2021). » Stress spillovers among financial markets: Evidence from Spain». Journal of Risk and Financial Management, Vol. 23, Art. DOI: https://doi.org/10.3390/jrfm14110527
  3. Ayuso, M., Bravo, J. M., Holzmann, R. and Palmer, E. (2021) “Automatic Indexation of the Pension Age to Life Expectancy: When Policy Design Matters”. Risks, 9(5), 96. DOI: https://doi.org/10.3390/risks9050096
  4. Bermúdez, L. and Karlis, D. (2021). «Multivariate INAR (1) Regression Models Based on the Sarmanov Distribution». Mathematics 2021, 9(5), 505. DOI: https://doi.org/10.3390/math9050505
  5. Bolancé, C. and Acuña, C. A. (2021). “A New Kernel Estimator of Copulas Based on Beta Quantile Transformations”. Mathematics, 9(10), 1078. DOI: https://doi.org/10.3390/math9101078
  6. Bolancé, C. and Guillen, M. (2021) “Nonparametric Estimation of Extreme Quantiles with an Application to Longevity Risk”. Risks, 9(4), 77. DOI:https://doi.org/10.3390/risks9040077
  7. Bravo, J. M., Ayuso, M., Holzmann, R. and Palmer, E. (2021) «Addressing the life expectancy gap in pension policy». Insurance: Mathematics and Economics, 99, 200-221. DOI: https://doi.org/10.1016/j.insmatheco.2021.03.025
  8. Bravo, J.M. and Ayuso, M. (2021) “Linking pensions to life expectancy: Tackling conceptual uncertainty through bayesian model averaging”. Mathematics, 9, 24, 3307. DOI: https://doi.org/10.3390/math9243307
  9. Chen, A., Guillen, M. and Rach, M. (2021) “Fees in tontines”. Insurance: Mathematics and Economics, 100, 89-106. DOI: https://doi.org/10.1016/j.insmatheco.2021.05.001
  10. Claveria, O., Monte, E. and Torra S. (2021) “A genetic programming approach for estimating economic sentiment in the baltic countries and the European union”. Technological and Economic Development of Economy, 27, 1, 262-279.  DOI: https://doi.org/10.3846/tede.2021.13989
  11. Frees, E.W., Bolancé, C., Guillen, M. and Valdez, E.A. (2021) “Dependence modeling of multivariate longitudinal hybrid insurance data with dropout”. Expert Systems with Applications, 185, 115552.
    DOI: https://doi.org/10.1016/j.eswa.2021.115552
  12. Guillen, M., Bermúdez, L. and Pitarque, A. (2021) “Joint generalized quantile and conditional tail expectation regression for insurance risk analysis”. Insurance: Mathematics and Economics, 99, 1-8. DOI:https://doi.org/10.1016/j.insmatheco.2021.03.006
  13. Guillen, M., Bolancé, C., Frees, E.W. and Valdez, E.A. (2021) “Case study data for joint modeling of insurance claims and lapsation”. Data in Brief, 39, 107639. DOI: https://doi.org/10.1016/j.dib.2021.107639
  14. Guillen, M., Nielsen, J.P. and Pérez-Marín, A.M. (2021) “Near‐miss telematics in motor insurance”. Journal of Risk and Insurance, 1-21. DOI:https://doi.org/10.1111/jori.12340
  15. Guillen, M., Pérez-Marín, A.M. and Alcañiz, M. (2021) “Percentile charts for speeding based on telematics information” Accident Analysis & Prevention, 150,105865. DOI: https://doi.org/10 Alcañiz, M..1016/j.aap.2020.105865
  16. Pesantez-Narvaez, J., Guillen, M. and (2021) “RiskLogitboost Regression for Rare Events in Binary Response: An Econometric Approach”. Mathematics 2021, 9, 579. DOI: https://doi.org/10.3390/math9050579
  17. Piulachs, X., Andrinopoulou, E. R., Guillén, M. and Rizopoulos, D. (2021) “A Bayesian joint model for zero‐inflated integers and left‐truncated event times with a time‐varying association: Applications to senior health care”. Statistics in Medicine, 40(1), 147-166. DOI:https://doi.org/10.1002/sim.8767
  18. Romo, E. and Ortiz-Gracia, L. (2021) «SWIFT calibration of the Heston model». Mathematics,9(5), 529. DOI: https://doi.org/10.3390/math9050529
  19. Santolino, M. (2021) “Median bilinear models in presence of extreme values”. Statistics and Operations Research Transactions, 2021, 45(2). DOI: https://doi.org/10.2436/20.8080.02.114
  20. Santolino, M., Belles-Sampera, J., Sarabia, J.M. and Guillen, M. (2021) ”An examination of the tail contribution to distortion risk measures”. Journal of Risk, 23, 6. DOI: https://doi.org/10.21314/JOR.2021.014
  21. Sarabia, J.M., Prieto, F., Jordá, V. and Guillen, M. (2021) “Multivariate Classes of GB2 Distributions with Applications”. Mathematics 2021, 9(1), 72.  DOI: https://doi.org/10.3390/math9010072
  22. Urbina, J., Santolino, M. and Guillen, M. (2021) ”Covariance principle for capital allocation: A time-varying approach”. Mathematics, 9, 16, 2005.  DOI: https://doi.org/10.3390/math9162005

2020

  1. Andrada-Félix, J., Fernandez-Perez, A. and Sosvilla-Rivero, S. (2020). “Distant or close cousins: Connectedness between cryptocurrencies and traditional currencies volatilities”. Journal of International Financial Markets, Institutions and Money, 67, 101219. DOI: https://doi.org/10.1016/j.intfin.2020.101219
  2. Arvelo, E., de Armas, J. and Guillen, M. (2020) “Assessing the Distribution of Elderly Requiring Care: A Case Study on the Residents in Barcelona and the Impact of COVID-19”, International Journal of Environmental Research and Public Health, 17(20), 7486.DOI: https://doi.org/10.3390/ijerph17207486
  3. Bolancé, C., Guillen, M. and Pitarque, A. (2020) “A Sarmanov Distribution with Beta Marginals: An Application to Motor Insurance Pricing”, Mathematics, 8(11, 2020. DOI: https://doi.org/10.3390/math8112020
  4. Bravo, J. M., and Ayuso, M. (2020) “Previsões de mortalidade e de esperança de vida mediante combinação Bayesiana de modelos: Uma aplicação à população portuguesa”. RISTI – Revista Iberica de Sistemas e Tecnologias de Informacao, 40(12), 128-144.     DOI: 10.17013/risti.40.128–145
  5. Sun, S., Bi, J., Guillen, M. and Pérez-Marín, A. M. (2020) “Assessing driving risk using internet of vehicles data: an analysis based on generalized linear models” Sensors, 20(9), 2712. DOI: https://doi.org/10.3390/s20092712
  6. Uribe, J. M. and Guillen, M. (2020) “Generalized Market Uncertainty Measurement in European Stock Markets in Real Time” Mathematics, 8(12), 2148. DOI: https://doi.org/10.3390/math8122148
  7. Uribe, J., Mosquera-López, S. and Guillen, M. (2020) “Characterizing electricity market integration in Nord Pool” Energy, 208,118368.
    DOI: https://doi.org/10.1016/j.energy.2020.118368
  8. Vida-Llana, X. and Guillen, M. (2020) “Advanced analytics pricing for the calculation of post-covid19 scenarios in automobile insurance” Anales del Instituto de Actuarios Españoles, 26, 157-179  DOI:https://doi.org/10.26360/2020_7

Contributions to meetings (2020-2022)

See link

PhD theses defended (2020-2022)

  • Garrón, I. (2023) Essays on Tail Risks in Macroeconomics, University of Barcelona, PhD in Economics. Dir: Helena Chulià / Jorge M. Uribe.
  • Vidal-Llana, J.J. (2023) Essays on Machine Learning for Risk Analysis in Finance, Insurance and Energy, University of Barcelona, PhD in Business.  Dir: Montserrat Guillen / Jorge M. Uribe.
  • Acuña, C.A. (2022) Dependence and Systematic Risks in Financial Markets: Spatial and Upper Tail Analysis, University of Barcelona, PhD in Business. Dir: Catalina Bolance / Salvador Torra.
  • Pitarque Méndez, A. (2022) Essays on Estimation, Prediction and Evaluation of Insurance Risk, University of Barcelona, PhD in Business.  Dir: Montserrat Guillen.
  • Pesántez-Narváez, J.E. (2021) Risk Analytics in Econometrics, University of Barcelona, PhD in Economics. Dir: Montserrat Guillen / Manuela Alcañiz.
  • Koser, C. (2020) Essays on Liquidity in Financial Markets, University of Barcelona, PhD in Economics. Dir: Helena Chulià / Jorge M. Uribe.