Year 2017,
Volume: 5 Issue: 1, 77 - 84, 30.04.2017
Emel Kizilok Kara
,
Sibel Acik Kemaloglu
References
- [1] Jorion, P. Risk management lessons from long term capital management. European financial management, 6 (2000)
No.3, 277-300.
- [2] Denuit M., Dhaene J. and, Goovaerts M.J., Kaas R. Actuarial Theory for Dependent Risks; Measures, Orders
and Models, John Wiley and Sons, 2005.
- [3] Rockafellar, R. T., and Uryasev, S. . Conditional value-at-risk for general loss distributions. Journal of banking &
finance, 26(2002), 7, 1443-1471.
- [4] Artzner, P., Delbaen, F., Eber, J. M., and Heath, D. . Coherent measures of risk. Mathematical finance, 9 (1999),3,
203-228.
- [5] Embrechts, P., Resnick, S. I., and Samorodnitsky, G. Extreme value theory as a risk management tool. North
American Actuarial Journal, 3(1999), 2, 30-41.
- [6] Pflug, G. Some Remarks on the Value at Risk and the Conditional Value at Risk, in: S.P. Uryasev (ed.), Probabilistic
Constrained Optimization: Methodology and Applications. Kluwer Academic Publishers, Dordrecht,
Netherlands, pp. 272-281, 2000.
- [7] Krokhmal, P., Palmquist, J., and Uryasev, S. Portfolio optimization with conditional value-at-risk objective and
constraints. Journal of risk, 4 (2002), 43-68.
- [8] Jiménez, J. A., and Arunachalam, V. . Using Tukey’sg and h family of distributions to calculate value-at-risk
and conditional value-at-risk. Journal of Risk, 13 (2011), 4, 95-116.
- [9] Embrechts, P., Kluppelberg, S., and Mikosch, T. Extremal events in finance and insurance, 1997.
- [10] Rau-Bredow, H. Value at risk, expected shortfall, and marginal risk contribution. Risk Measures for the 21st
Century, Szego, G.(ed.), Wiley Finance, 61-68, 2004.
- [11] Cummins, J. D., Dionne, G., McDonald, J. B., and Pritchett, B. M. Applications of the GB2 family of distributions
in modeling insurance loss processes. Insurance: Mathematics and Economics, 9 (1990), 4, 257-272.
- [12] Dickson, D. C., and Hipp, C. On the time to ruin for Erlang (2) riskprocesses. Insurance: Mathematics and
Economics, 29(2001), 3, 333-344.
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Insurance: Mathematics and Economics, 31(2002), 2, 205-214.
- [14] Lefèvre, C., and Picard, P. Appell pseudopolynomials and Erlang-type risk models. Stochastics An International
Journal of Probability and Stochastic Processes, 86 (2014), 4, 676-695.
- [15] Straub, E. Swiss Association of Actuaries (Zürich). Non-life insurance mathematics (No. 517/S91n). Berlin:
Springer, 1988.
- [16] Chen, S. X. Probability density function estimation using gamma kernels. Annals of the Institute of Statistical
Mathematics, 52 (2000), 3, 471-480.
- [17] Lee, S. C., and Lin, X. S. Modeling and evaluating insurance losses via mixtures of Erlang distributions. North
American Actuarial Journal, 14 (2010), 1, 107-130.
- [18] Jeon, Y., and Kim, J. H. A gamma kernel density estimation for insurance loss data. Insurance: Mathematics and
Economics, 53 (2013), 3, 569-579.
- [19] Pascal B. Varia opera mathematica D.Petri de Fermat, Tolossae,1679.
- [20] Student. On the error of counting with a haemacytometer. Biometrika, (1907), 351-360.
- [21] Gómez-Déniz, E., Sarabia, J. M., and Calderín-Ojeda, E. Univariate and multivariate versions of the negative
binomial-inverse Gaussian distributions with applications. Insurance: Mathematics and Economics, 42(2008), 1,
39-49.
- [22] Pudprommarat, C., Bodhisuwan, W., and Zeephongsekul, P. . A new mixed negative binomial distribution.
Journal of Applied Sciences, 12 (2012),17, 1853.
- [23] Ramos, M. W. A., Percontini, A., Cordeiro, G. M., and da Silva, R. V. The Burr XII negative binomial distribution
with applications to lifetime data. International Journal of Statistics and Probability, 4 (2015), 1, 109.
- [24] Kongrod S., Bodhisuwan W., Payakkapong P. The negative binomial-Erlang distribution with applications
Introduction Journal of Pure and Applied Mathematics, 92 (2014), 3, 389-401.
- [25] Percontini, A., Cordeiro, G. M., and Bourguignon, M. The G-Negative Binomial Family: General Properties and
Applications. Advances and Applications in Statistics, 35 (2013), 127-160.
- [26] Erlang A.K. Solution of some problems in the theory of probabilities of significance in automatic telephone
exchanges. Elektrotkeknikeren, 13 (1917), 513.
Risk Measures of the ERNB Distribution Generated by G-NB Family
Year 2017,
Volume: 5 Issue: 1, 77 - 84, 30.04.2017
Emel Kizilok Kara
,
Sibel Acik Kemaloglu
Abstract
This paper provides VaR and CVaR risk measures, calculated for the Erlang-Negative Binomial (ERNB)
distribution. The Erlang and negative binomial distributions are given and then ERNB distribution is
obtained using a family of univariate distributions which is called G-Negative Binomial (G-NB). It is
defined as compounding the negative binomial distribution (NB) with any continuous distribution (G).
Here, we use the ERNB distribution obtained as taking Erlang distribution instead of G. In this paper,
we focus on the estimation of VaR and CVaR risk measures for this distribution in closed form and the
explicit expressions are also presented for some parameter values. The results are portrayed in the figures.
In additionally, numerical examples are given to illustrate changing of the risk measure according to
some parameters on a real data set of automobile insurance policies.
References
- [1] Jorion, P. Risk management lessons from long term capital management. European financial management, 6 (2000)
No.3, 277-300.
- [2] Denuit M., Dhaene J. and, Goovaerts M.J., Kaas R. Actuarial Theory for Dependent Risks; Measures, Orders
and Models, John Wiley and Sons, 2005.
- [3] Rockafellar, R. T., and Uryasev, S. . Conditional value-at-risk for general loss distributions. Journal of banking &
finance, 26(2002), 7, 1443-1471.
- [4] Artzner, P., Delbaen, F., Eber, J. M., and Heath, D. . Coherent measures of risk. Mathematical finance, 9 (1999),3,
203-228.
- [5] Embrechts, P., Resnick, S. I., and Samorodnitsky, G. Extreme value theory as a risk management tool. North
American Actuarial Journal, 3(1999), 2, 30-41.
- [6] Pflug, G. Some Remarks on the Value at Risk and the Conditional Value at Risk, in: S.P. Uryasev (ed.), Probabilistic
Constrained Optimization: Methodology and Applications. Kluwer Academic Publishers, Dordrecht,
Netherlands, pp. 272-281, 2000.
- [7] Krokhmal, P., Palmquist, J., and Uryasev, S. Portfolio optimization with conditional value-at-risk objective and
constraints. Journal of risk, 4 (2002), 43-68.
- [8] Jiménez, J. A., and Arunachalam, V. . Using Tukey’sg and h family of distributions to calculate value-at-risk
and conditional value-at-risk. Journal of Risk, 13 (2011), 4, 95-116.
- [9] Embrechts, P., Kluppelberg, S., and Mikosch, T. Extremal events in finance and insurance, 1997.
- [10] Rau-Bredow, H. Value at risk, expected shortfall, and marginal risk contribution. Risk Measures for the 21st
Century, Szego, G.(ed.), Wiley Finance, 61-68, 2004.
- [11] Cummins, J. D., Dionne, G., McDonald, J. B., and Pritchett, B. M. Applications of the GB2 family of distributions
in modeling insurance loss processes. Insurance: Mathematics and Economics, 9 (1990), 4, 257-272.
- [12] Dickson, D. C., and Hipp, C. On the time to ruin for Erlang (2) riskprocesses. Insurance: Mathematics and
Economics, 29(2001), 3, 333-344.
- [13] Yuen, K. C., Guo, J., and Wu, X. On a correlated aggregate claims model with Poisson and Erlang risk processes.
Insurance: Mathematics and Economics, 31(2002), 2, 205-214.
- [14] Lefèvre, C., and Picard, P. Appell pseudopolynomials and Erlang-type risk models. Stochastics An International
Journal of Probability and Stochastic Processes, 86 (2014), 4, 676-695.
- [15] Straub, E. Swiss Association of Actuaries (Zürich). Non-life insurance mathematics (No. 517/S91n). Berlin:
Springer, 1988.
- [16] Chen, S. X. Probability density function estimation using gamma kernels. Annals of the Institute of Statistical
Mathematics, 52 (2000), 3, 471-480.
- [17] Lee, S. C., and Lin, X. S. Modeling and evaluating insurance losses via mixtures of Erlang distributions. North
American Actuarial Journal, 14 (2010), 1, 107-130.
- [18] Jeon, Y., and Kim, J. H. A gamma kernel density estimation for insurance loss data. Insurance: Mathematics and
Economics, 53 (2013), 3, 569-579.
- [19] Pascal B. Varia opera mathematica D.Petri de Fermat, Tolossae,1679.
- [20] Student. On the error of counting with a haemacytometer. Biometrika, (1907), 351-360.
- [21] Gómez-Déniz, E., Sarabia, J. M., and Calderín-Ojeda, E. Univariate and multivariate versions of the negative
binomial-inverse Gaussian distributions with applications. Insurance: Mathematics and Economics, 42(2008), 1,
39-49.
- [22] Pudprommarat, C., Bodhisuwan, W., and Zeephongsekul, P. . A new mixed negative binomial distribution.
Journal of Applied Sciences, 12 (2012),17, 1853.
- [23] Ramos, M. W. A., Percontini, A., Cordeiro, G. M., and da Silva, R. V. The Burr XII negative binomial distribution
with applications to lifetime data. International Journal of Statistics and Probability, 4 (2015), 1, 109.
- [24] Kongrod S., Bodhisuwan W., Payakkapong P. The negative binomial-Erlang distribution with applications
Introduction Journal of Pure and Applied Mathematics, 92 (2014), 3, 389-401.
- [25] Percontini, A., Cordeiro, G. M., and Bourguignon, M. The G-Negative Binomial Family: General Properties and
Applications. Advances and Applications in Statistics, 35 (2013), 127-160.
- [26] Erlang A.K. Solution of some problems in the theory of probabilities of significance in automatic telephone
exchanges. Elektrotkeknikeren, 13 (1917), 513.