Ruin probabilities as functions of the roots of a polynomial
Volume 10, Issue 3 (2023), pp. 247–266
Pub. online: 15 March 2023
Type: Research Article
Open Access
Received
18 December 2022
18 December 2022
Revised
8 March 2023
8 March 2023
Accepted
9 March 2023
9 March 2023
Published
15 March 2023
15 March 2023
Abstract
A new formula for the ultimate ruin probability in the Cramér–Lundberg risk process is provided when the claims are assumed to follow a finite mixture of m Erlang distributions. Using the theory of recurrence sequences, the method proposed here shifts the problem of finding the ruin probability to the study of an associated characteristic polynomial and its roots. The found formula is given by a finite sum of terms, one for each root of the polynomial, and allows for yet another approximation of the ruin probability. No constraints are assumed on the multiplicity of the roots and that is illustrated via a couple of numerical examples.
References
Asmussen, S., Albrecher, H.: Ruin Probabilities vol. 14. World scientific Singapore (2010). MR2766220. https://doi.org/10.1142/9789814282536
Borchers, H.W.: Pracma: Practical Numerical Math Functions. (2021). R package version 2.3.3. https://CRAN.R-project.org/package=pracma
Coen, A., Mena, R.H.: Ruin probabilities for bayesian exchangeable claims processes. Journal of Statistical Planning and Inference 166, 102–115 (2015). MR3390137. https://doi.org/10.1016/j.jspi.2015.01.005
Constantinescu, C., Kortschak, D., Maume-Deschamps, V.: Ruin probabilities in models with a markov chain dependence structure. Scandinavian Actuarial Journal 2013(6), 453–476 (2013). MR3176014. https://doi.org/10.1080/03461238.2011.627745
Cramér, H.: Historical review of filip lundberg’s works on risk theory. Scandinavian Actuarial Journal 1969(sup3), 6–12 (1969). MR0347028. https://doi.org/10.1080/03461238.1969.10404602
Delbaen, F., Haezendonck, J.: Classical risk theory in an economic environment. Insur. Math. Econ. 6(2), 85–116 (1987). MR0896414. https://doi.org/10.1016/0167-6687(87)90019-9
Dickson, D.C.M.: Introduction to Ruin Theory, 2nd. edn. International Series on Actuarial Science. Cambridge University Press (2016). https://doi.org/10.1017/9781316650776.007
Embrechts, P., Schmidli, H.: Ruin estimation for a general insurance risk model. Adv. Appl. Probab. 26(2), 404–422 (1994). MR1272719. https://doi.org/10.2307/1427443
Gerber, H.U., Yang, H.: Absolute ruin probabilities in a jump diffusion risk model with investment. N. Am. Actuar. J. 11(3), 159–169 (2007). MR2393866. https://doi.org/10.1080/10920277.2007.10597474
Goffard, P.-O., Loisel, S., Pommeret, D.: A polynomial expansion to approximate the ultimate ruin probability in the compound poisson ruin model. J. Comput. Appl. Math. 296, 499–511 (2016). MR3430154. https://doi.org/10.1016/j.cam.2015.06.003
Gordienko, E., Vazquez-Ortega, P.: Simple continuity inequalities for ruin probability in the classical risk model. ASTIN Bull. 46(3), 801–814 (2016). MR3551965. https://doi.org/10.1017/asb.2016.10
Grigutis, A.: Exact expression of ultimate time survival probability in homogeneous discrete-time risk model. AIMS Math. 8(3), 5181–5199 (2023). MR4525843. https://doi.org/10.3934/math.2023260
Horn, R.A., Johnson, C.R.: Matrix Analysis. Cambridge university press (2012). MR2978290
Kalashnikov, V., Konstantinides, D.: Ruin under interest force and subexponential claims: a simple treatment. Insur. Math. Econ. 27(1), 145–149 (2000). MR1796976. https://doi.org/10.1016/S0167-6687(00)00045-7
Klugman, S.A., Panjer, H.H., Willmot, G.E.: Loss Models: from Data to Decisions vol. 715. John Wiley & Sons (2012). MR3222004
Landriault, D., Renaud, J.-F., Zhou, X.: An insurance risk model with parisian implementation delays. Methodol. Comput. Appl. Probab. 16(3), 583–607 (2014). MR3239810. https://doi.org/10.1007/s11009-012-9317-4
Lee, S.C., Lin, X.S.: Modeling and evaluating insurance losses via mixtures of erlang distributions. N. Am. Actuar. J. 14(1), 107–130 (2010). MR2720423. https://doi.org/10.1080/10920277.2010.10597580
Lefèvre, C., Picard, P.: A nonhomogeneous risk model for insurance. Comput. Math. Appl. 51(2), 325–334 (2006). MR2203083. https://doi.org/10.1016/j.camwa.2005.11.005
Li, J., Liu, Z., Tang, Q.: On the ruin probabilities of a bidimensional perturbed risk model. Insur. Math. Econ. 41(1), 185–195 (2007). MR2324573. https://doi.org/10.1016/j.insmatheco.2006.10.012
Miljkovic, T., Grün, B.: Modeling loss data using mixtures of distributions. Insur. Math. Econ. 70, 387–396 (2016). MR3543061. https://doi.org/10.1016/j.insmatheco.2016.06.019
Mnatsakanov, R.M., Sarkisian, K., Hakobyan, A.: Approximation of the ruin probability using the scaled laplace transform inversion. Appl. Math. Comput. 268, 717–727 (2015). MR3399457. https://doi.org/10.1016/j.amc.2015.06.087
Peralta, O., Rojas-Nandayapa, L., Xie, W., Yao, H.: Approximation of ruin probabilities via erlangized scale mixtures. Insur. Math. Econ. 78, 136–156 (2018). MR3761073. https://doi.org/10.1016/j.insmatheco.2017.12.005
Philipson, C.: A note on moments of a poisson probability distribution. Scandinavian Actuarial Journal 1963(3-4), 243–244 (1963). MR0174073. https://doi.org/10.1080/03461238.1963.10410613
R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2020). R Foundation for Statistical Computing. https://www.R-project.org/
Rincón, L., Santana, D.J.: Ruin probability for finite erlang mixture claims via recurrence sequences. Methodol. Comput. Appl. Probab., 24, 2213–2236 (2022). MR4457584. https://doi.org/10.1007/s11009-021-09913-2
Rincón, L., Santana, D.J.: Ruin probability for finite negative binomial mixture claims via recurrence sequences. Commun. Stat., Theory Methods, 1–17 (2022). https://doi.org/10.1080/03610926.2022.2087091
Santana, D.J., González-Hernández, J., Rincón, L.: Approximation of the ultimate ruin probability in the classical risk model using erlang mixtures. Methodol. Comput. Appl. Probab. 19(3), 775–798 (2017). MR3683971. https://doi.org/10.1007/s11009-016-9515-6
Schassberger, R.: Warteschlangen. Springer (1973). MR0365768
Sedgewick, R., Flajolet, P.: An Introduction to the Analysis of Algorithms. Pearson Education India (2013). MR2876111. https://doi.org/10.1051/ita/2011110
Tamturk, M., Utev, S.: Ruin probability via quantum mechanics approach. Insur. Math. Econ. 79, 69–74 (2018). MR3771914. https://doi.org/10.1016/j.insmatheco.2017.12.009
Thorin, O.: Some remarks on the ruin problem in case the epochs of claims form a renewal process. Scandinavian Actuarial Journal 1970(1-2), 29–50 (1970). MR0290480. https://doi.org/10.1080/03461238.1970.10405645
Tijms, H.C.: Stochastic Modelling and Analysis: a Computational Approach. John Wiley & Sons, Inc. (1986). MR0847718
Verbelen, R., Gong, L., Antonio, K., Badescu, A., Lin, S.: Fitting mixtures of erlangs to censored and truncated data using the em algorithm. ASTIN Bull. 45(3), 729–758 (2015). MR3394072. https://doi.org/10.1017/asb.2015.15
Willmot, G., Lin, S.: Risk modelling with the mixed erlang distribution. Appl. Stoch. Models Bus. Ind. 27(1), 2–16 (2011). MR2752449. https://doi.org/10.1002/asmb.838
Willmot, G., Woo, J.K.: On the class of erlang mixtures with risk theoretic applications. N. Am. Actuar. J. 11(2), 99–115 (2007). MR2380721. https://doi.org/10.1080/10920277.2007.10597450
You, H., Guo, J., Jiang, J.: Interval estimation of the ruin probability in the classical compound Poisson risk model. Comput. Stat. Data Anal. 144, 106890 (2020). MR4040161. https://doi.org/10.1016/j.csda.2019.106890