Control and supervision of economic activities and especially those related to finance and insurance requires that the quantification of risk
is carried out in a manner that is increasingly objective and rigorous. We study how to quantify the risk in complex situations from the
following points of view: 1) there is a multivariate behaviour and dependence between different sources of risk, 2) the distribution of losses
does not follow a normal statistical distribution, or close to the normal, 3) extreme events have a very different behavior to the rest, 4) the
dynamics of risk need to be captured and 5) there is a vhigh volume of data. From previous results on multivariate risk quantification and
further risk measures more general results are obtained with numerous applications in economics and risk management. The main
research lines of this project are: 1) methodology in the analysis of statistical distributions, 2) the relationship between measures of risk,
entropy and multivariate inequality 3) extending measures of multidimensional risk and the dynamics of risk 4) models for panels data and
joint modelling 5) allocation of risks. Applications in insurance include, pension, where the multidimensionality of risk comes from
combining investment risk, longevity and loss of personal autonomy (functional or cognitive dependence). The implications for financial
and insurance regulation through directives Basel III and Solvency II are studied. The results have an impact on all economic sectors due
to the enforcement of the Audit Act that will require additional and explicit control of risks in financial reporting. Furthermore, this research
is of interest to all citizens at a micro level, at least in regard to their expectations for retirement.

Publications (2017-2019)


  1. Ayuso, M.M., Guillen M. and Nielsen, J.P. “Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data” Transportation, 46(3), 735-752 DOI:
  2. Berthe, E., Dang, D.M. and Ortiz-Gracia, L. (2019) «A Shannon wavelet method for pricing foreign exchange options under the Heston multi-factor
    CIR model» Applied Numerical Mathematics, 136, 1-22. DOI:
  3. Bolancé, and Vernic, R. (2019) “Multivariate count data generalized linear models: Three approaches based on the Sarmanov distribution”, Insurance: Mathematics and Economics, 85, 89-103. DOI:
  4. Boonen, T.J., Guillen, M. and Santolino, M. (2019) “Forecasting compositional risk allocations” Insurance, Mathematics and Economics, 84, 79-86. DOI:
  5. Cohen, L., Gómez-Puig, M., and Sosvilla-Rivero, S. (2019). “Has de ECB’s monetary policy prompted companies to investor pay dividends? Applied Economics, 51 (45), 4920-4938. DOI:
  6. Chuliá, H., Furió, M.D. and Uribe, J.M. (2019). “Volatility Spillovers in Energy Markets”, Energy Journal, 40(3):127-152.
  7. Colldeforns-Papiol, G., Ortiz-Gracia, L. and Oosterlee, C.W. (2019) ”Quantifying credit portfolio losses under multi-factor models” International Journal of Computer Mathematics, 96(11), 2135–2156. DOI:
  8. Gómez-Puig, M. and Sosvilla-Rivero, S. (2019). “New empirical evidence on the impact of public debt on economic growth in EMU countries», Revista de Economía Mundial-Journal of World Economy, 51, 101-120. DOI:
  9. Gómez-Puig, M., Singh, M.K. and Sosvilla-Rivero S. (2019). “The sovereign-bank nexus in peripheral euro area: Further evidence from contingent claim analysis”. North American Journal of Economics and Finance, 49, 1-46. DOI:
  10. Denuit, M., Guillen, M. and Trufin, J. (2019) “Multivariate credibility modeling for usage-based motor insurance pricing with behavioural data” Annals of Actuarial Science 13(2), 378-399. DOI:
  11. Fondevila-McDonald, Y., Molinero-Ruiz, E., Vergara-Duarte, M., Guillen, M., Ollé-Espluga, L., Menéndez, M. and Benach, J. (2019) “Is there an estimation bias in occupational health and safety surveys? The mode of administration and informants as a source of error” Sociological Methods and Research, 48, 1, 185-201. DOI:
  12. Guillen, M., Nielsen, J.P., Ayuso, M. and Pérez-Marin, A.M. (2019)“The use of telematics devices to improve automobile insurance rates” Risk Analysis, 39, 3, 662-672. DOI:
  13. Pérez-Marín, A.M. and Guillen, M. (2019) “Semi-autonomous vehicles: Usage-based data evidences of what could be expected from eliminating speed limit violations” Accident Analysis and Prevention, 123, 99-106.  DOI:
  14. Perez-Marin, A. M., Ayuso, M. and Guillen, M. (2019) “Do young insured drivers slow down after suffering an accident?” Transportation Research Part F: Psychology and Behaviour 62, 690-699. DOI:
  15. Pérez-Marín, A. M., Guillen, M., Alcañiz, M. and Bermúdez, L. (2019) “Quantile regression with telematics information to assess the risk of driving above the posted speed limit”, Risks, 7, 80. DOI:
  16. Pesántez-Narváez, J., Guillén, M. and Alcañiz, M. (2019). “Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression”. Risks, 7(2), 70. DOI:


  1. Acuña, C., Bolancé, C. and Torra, S. (2018) «Análisis de la dependencia espacial entre índices bursátiles» Anales del Instituto de Actuarios Españoles, 4ª época, 24, 2018/79-97 DOI:
  2. Alcañiz, M., Guillen, M. and Santolino, M. (2018) “Prevalence of drug use among drivers based on mandatory, random tests in a roadside survey” PLoS ONE, 13, 6, art. no. e0199302. DOI:
  3. Bermúdez, Ll, Karlis, D. and Santolino, M. (2018) “A discrete mixture regression for modeling the duration of non-hospitalization medical leave of motor accident victims” Accident Analysis and Prevention,. 121, 157-165.
  4. Bermúdez, Ll., Guillen, M. and Karlis, D. (2018) “Allowing for time and cross dependence assumptions between claim counts in ratemaking models” Insurance: Mathematics and Economics, 83, 161-169.
  5. Bolancé, C., Alemany, R. and Padilla-Barreto, A. E. (2018) “Impact of D-Vine Structure on Risk Estimation” The Journal of Risk, 20, 1-32.
  6. Bolancé, C., Guillen, M., Nielsen, J. P. and Thuring, F. (2018) “Price and Profit Optimization for Financial Services” Risks, 6, 1, 9.
  7. Chen, A., Vigna, E. and Guillen, M. (2018) “Solvency requirement in a unisex mortality model” Astin Bulletin, 48(3), 1219-1243.
  8. Claveria, O., Monte, E. and Torra, S. (2018) “A data-driven approach to construct survey-based indicators by means of evolutionary algorithms”. Social Indicators Research, 135 (1), 1-14. DOI:
  9. Chuliá, H., Fernández, J. and Uribe, J.M. (2018) “Currency downside risk, liquidity, and financial stability” Journal of International Money and Finance, 89, 83-102. DOI:
  10. Chuliá, H., Pinchao, A.D. and Uribe, J.M. (2018) “Risk Synchronization in International Stock Markets”, Global Economic Review, 47(2),135-150.
  11. Colldeforns-Papiol, G. and Ortiz-Gracia. L.(2018) “Computation of market risk measures with stochastic liquidity horizon” Journal of Computational and Applied Mathematics, 342, 431-450.
  12. Dang, D.M. and Ortiz-Gracia, L. (2018) “A dimension reduction Shannon-wavelet based method for option pricing” Journal of Scientific Computing, 75, 2, 733 761 DOI:
  13. Donnelly, C., Guillen, M., Nielsen, J.P. and Pérez-Marín, A.M. (2018) “Implementing individual savings decisions for retirement with bounds on wealth” Astin Bulletin, 48, 1, 111-137. DOI:
  14. Gómez-Puig, M. and Sosvilla-Rivero, S. (2018). “Nonfinancial debt and economic growth in euro-area countries” Journal of International Financial Markets, Institutions and Money, 56, 17-37
  15. Gómez-Puig, M. and Sosvilla-Rivero, S. (2018) “Public debt and economic growth: Further evidence for the Euro Area”. Acta Oeconomica, 68, 209-229 DOI:
  16. Gómez-Puig, M. and Sosvilla-Rivero, S. (2018). “On the time-varying nature of the debt-growth nexus: Evidence from the euro area”. Applied Economics Letters, 25, 9, 597-600. DOI:
  17. Guillen, M., Sarabia, J.M., Belles-Sampera, J. and Prieto, F. (2018). “Distortion Risk Measures for Non-negative Multivariate Risks” Journal of Operational Risk, 13, 2, 35–57. DOI:
  18. Ladrón de Guevara, R., Torra, S. and Monte, E. (2018). “Extraction of the underlying structure of systematic risk from Non-Gaussian multivariate financial time series using Independent Component Analysis. Evidence from the Mexican Stock Exchange.” Computación y Sistemas, 22 (4), 1049-1064. México: ISSN Impreso: 1405-5546, ISSN electrónico: 2007-9737
  19. Leitao, A., C.W. Oosterlee, C.W., Ortiz-Gracia, L. and Bohte, S.M. (2018) “On the data-driven COS method” Applied Mathematics and Computation, 317, 68-84. DOI:
  20. Leitao, A., Ortiz-Gracia, L. and Wagner, E.I. (2018) «SWIFT valuation of discretely monitored arithmetic Asian options» Journal of Computational Science, 28, 120–139. DOI:
  21. Salas-Molina, F., Rodríguez-Aguilar, J. A., Serrà, J., Guillen, M. and Martin, F. J. (2018) “Empirical analysis of daily cash flow time series and its implications for forecasting” SORT-Statistics and Operations Research Transactions, 42, 1, 73-98. DOI:
  22. Schulze-Darup, A., Guillen, M. and Piulachs, X. (2018) “Consumer preferences for electric vehicles in Germany” International Journal of Transport Economics, 45, 1, 97-122 DOI:
  23. Söderberg, M., Menezes, F. and Santolino, M. (2018) “Regulatory behaviour under threat of court reversal: theory and evidence from the Swedish electricity market” Energy Economics, , 302-310.
  24. Torra, V., Guillen, M. and Santolino, M. (2018) “Continuous m-dimensional distorted probabilities”, Information Fusion, 44, 97-102.
  25. Uribe, J.M., Chuliá, H. and Guillen, M. (2018) “Trends in the quantiles of the life table survivorship function” European Journal of Population, 34, 5, 793-817.
  26. Uribe, J.M., Guillen M. and Mosquera-Lopez, E. (2018) “Uncovering the nonlinear predictive causality between natural gas and electricity prices” Energy Economics, 74, 904-916. DOI:



  1. Alcañiz, M., Santolino, M. and Ramon, Ll. (2017) “A comparative analysis of tree-based models classifying imbalanced breath alcohol data” Boletín de Estadística e Investigación Operativa, 33, 3, 189-222.
  2. Bermúdez, Ll., Karlis, D. and Santolino, M. (2017) “A finite mixture of multiple discrete distributions for modelling heaped count data” Computational Statistics and Data Analysis, 112, 14-23.
  3. Bølviken, E. and Guillen, M. (2017) “Risk aggregation in Solvency II through recursive log-normals” Insurance: Mathematics and Economics, 73, 20-26.
  4. Boucher, J-P., Côté, S. and Guillen, M. (2017) “Exposure as duration and distance in telematics motor insurance using generalized additive models” Risks, 5(4), 54; DOI:
  5. Bräutigam, M., Guillen, M. and Nielsen, J.P. (2017) “Facing up to longevity with old actuarial methods: a comparison of pooled funds and income tortines” The Geneva Papers on Risk and Insurance – Issues and Practice, 42, 3, 406-422.
  6. Chuliá, H., Guillen, M. and Uribe, J.M. (2017) “Measuring uncertainty in the stock markets” International Review of Economics and Finance, 48, 18-33.
  7. Chuliá, H., Guillen, M. and Uribe, J.M. (2017) “Spillovers from the United States to Latin American and G7 stock markets: A VAR quantile analysis” Emerging Markets Review, 31, 32-46.
  8. Chuliá, H., Gupta, R., Uribe, J.M. and Wohar, M.E. (2017) “Impact of US Uncertainties on Emerging and Mature Markets: Evidence from a Quantile-Vector Autoregressive Approach” Journal of International Financial Markets, Institutions and Money, 48, 178-191. DOI:
  9. Chuliá H., Pinchao, A.D. and Uribe, J.M. (2017) “Risk Synchronization in International Stock Markets” Global Economic Review, 47, 2, 135-150.
  10. Clavería, O., Monte, E. and Torra, S. (2017) “Using survey data to forecast real activity with evolutionary algorithms. A cross-country analysis” Journal Of Applied Economics, 20, 2, 329-349.
  11. Clavería, O., Monte, E. and Torra, S. (2017) “Data pre-processing for neural network-based forecasting: does it really matter?” Technological and Economic Development of Economy. 235, 709-725.
  12. Clavería, O., Monte, E. and Torra, S. (2017) “A new approach for the quantification of qualitative measures of economic expectations” Quality & Quantity, 51, 6, 2685-2706. DOI:
  13. Claveria, O., Monte, E. and Torra, S. (2017) “Assessment of the effect of the financial crisis on agents’ expectations through symbolic regression” Applied Economics Letters, 24, 648-652.
  14. Colldeforns-Papiol, G., Ortiz-Gracia, L. and C.W. Oosterlee (2017) “Two-dimensional Shannon wavelet inverse Fourier technique for pricing European options” Applied Numerical Mathematics, 117, 115–138. DOI:
  15. D’Amico, G.; Guillen, M.; Manca, R. (2017) “Multi-state models for evaluating conversion options in life insurance” Modern Stochastics Theory and Applications, 4(2), 127-139.DOI:
  16. Gómez-Puig, M. and Sosvilla-Rivero, S. (2017) “Public debt and economic growth: Further evidence for the Euro Area” Acta Oeconomica, 68, 209-229. DOI:
  17. Gómez-Puig, M. and Sosvilla-Rivero, S. (2017) “On the time-varying nature of the debt-growth nexus: Evidence from the euro area”. Applied Economics Letters, 25, 9, 597-600. DOI:
  18. Gómez-Puig, M. and Sosvilla-Rivero, S. (2017) “Heterogeneity in the debt-growth nexus: Evidence from EMU countries” International Review of Economics and Finance, 51, 470-486.DOI:
  19. Maree, S.C., Ortiz-Gracia L. and C.W. Oosterlee (2017) “Pricing early-exercise and discrete barrier options by Shannon wavelet expansions” Numerische Mathematik, 136, 4, 1035-1070. DOI:
  20. Marí del Cristo, M.L. and Gómez-Puig, M. (2017) “Dollarization and the relationship between EMBI and fundamentals in Latin American countries” Cuadernos de Economía: Spanish Journal of Economics and Finance, 40, 14–30. DOI:
  21. Mosquera, S., Manotas, D. and Uribe, J.M. (2017) “Risk asymmetries in hydrothermal power generation markets” Electric Power Systems Research, 147, 154-164. DOI:
  22. Mosquera-López, S., Uribe, J.M. and Manotas, D. (2017) “Nonlinear empirical pricing in electricity markets using fundamental weather factors” Energy, 139(15): 594-605. DOI:
  23. Piulachs, X., Alemany, R. and Guillen, M. (2017) “Emergency care usage and longevity have opposite effects on health insurance rates” Kybernetes, 46(1), 102-113. DOI:
  24. Piulachs, X., Alemany, R., Guillen, M. and Rizopoulos, D. (2017) “Joint models for longitudinal counts and left-truncated time-to event data with applications to health insurance” Sort-Statistics and Operations Research Transactions, 41(2), 347-372. DOI:
  25. Uribe, J. M., Chuliá, H., & Guillen, M. (2017) “Uncertainty, systemic shocks and the global banking sector: Has the crisis modified their relationship?” Journal of International Financial Markets, Institutions and Money, 50, 52-68. DOI:

Contributions to meetings (2017-2019)

See link

PhD theses defended (2017-2019)

  • Padilla-Barreto, A. (2019) Cuantificación del riesgo global del asegurado para mejorar la tarificación, Universitat Politècnica de Catalunya. PhD in Statistics. Industrial PhD. Dir: C. Bolancé / Montserrat Guillen.
  • Manish K. Singh (2018) “Bank and sovereign risk: The case of European Economic and Monetary Union”. PhD in Economics. Dir: Marta Gómez-Puig.
  • Uribe, J.M. (2018) Essays on Risk and Uncertainty in Economics and Finance. University of Barcelona, PhD in Economics. Dir: Helena Chulià / Montserrat Guillen.
  • Gemma Colldeforns (Department of Mathematics, UAB, February 23, 2018). PhD project: “Wavelet approach in computational finance”. Dir: Luis Ortiz-Gracia.
  • Piulachs, X. (2017) Joint Modeling of Longitudinal and Time-to-Event Data with Applications in Health Insurance. University of Barcelona, PhD in Statistics. Dir: Montserrat Guillen / Ramon Alemany.