Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-03T02:06:18.264Z Has data issue: false hasContentIssue false

References

Published online by Cambridge University Press:  05 May 2014

Erik Bølviken
Erik Bølviken
Affiliation:
Universitetet i Oslo
Get access

Summary

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Computation and Modelling in Insurance and Finance , pp. 656 - 679
Publisher: Cambridge University Press
Print publication year: 2014

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abramowitz, M. and Stegun, I. (1965). Handbook of Mathematical Functions. New York: Dover.Google Scholar
Adelson, R. M. (1966). Compound Poisson distributions. Operational Research Quarterly, 17, 73–75.Google Scholar
Ahrens, J. and Dieter, U. (1974). Computer methods for sampling from Gamma, Beta, Poisson and binomial distributions. Computing, 12, 223–246.CrossRefGoogle Scholar
Allen, M.B. and Isaacson, E.L. (1998). Numerical Analysis for Applied Science. New York: John Wiley & Sons.Google Scholar
Antonio, K. and Beirlant, J. (2007). Actuarial statistics with generalized linear mixed models. Insurance: Mathematics and Economics, 40, 58–76.Google Scholar
Applebaum, D. (2004). Lévy Processes and Stochastic Calculus. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Asmussen, p. and Glynn, p. w. (2007). Stochastic Simulation. Algorithms and Analysis. New York: Springer-Verlag.Google Scholar
Asmussen, s. (2000). Ruin Probabilities. Singapore: World Scientific.CrossRefGoogle Scholar
Asmussen, s. and Kroese, D.p. (2006). Improved algorithms for rare event simulation with heavy tails. Advances in Applied Probability, 38, 545–558.CrossRefGoogle Scholar
Atkinson, A. C. (1979). The computer generation of poisson random variables. Applied Statistics, 28, 29–35.Google Scholar
Azcue, P. and Muler, N. (2005). Optimal reinsurance and dividend distribution policies in the Cramér-Lundberg model. Mathematical Finance, 15, 261–308.CrossRefGoogle Scholar
Babbel, D., Gold, J. and Merrill, C. B. (2002). Fair value of liabilities: The financial economics perspective. North American Actuarial Journal, 6, 12–27.CrossRefGoogle Scholar
Bacro, J. N. and Brito, M. (1998). A tail bootstrap procedure for estimating the tail Pareto-index. Journal of Statistical Planning and Inference, 71, 245–260.CrossRefGoogle Scholar
Baier, C. and Katoen, J.-P. (2008). Principles of Model Checking. Cambridge, MA: MIT Press.Google Scholar
Balakrishnan, N. (2004a). Continuous multivariate distributions. In Teugels, J. and Sundt, B. (eds), Encyclopedia of Actuarial Science. Chichester: John Wiley & Sons; pp. 330–357.Google Scholar
Balakrishnan, N. (2004b). Discrete multivariate distributions. In Teugels, J. and Sundt, B. (eds), Encyclopedia of Actuarial Science. Chichester: John Wiley & Sons; pp. 549–571.Google Scholar
Balakrishnan, N. and Nevzorov, V. B. (2003). A Primer on Statistical Distributions. Hoboken, NJ: John Wiley & Sons.CrossRefGoogle Scholar
Ball, C. A. and Torous, W. N. (2000). Stochastic correlation across international stock markets. Journal of Empirical Finance, 7, 373–388.CrossRefGoogle Scholar
Ballotta, L., Esposito, G. and Haberman, s. (2006). The IASB insurance project for life insurance contracts: Impact on reserving methods and solvency. Insurance: Mathematics and Economics, 39, 356–375.Google Scholar
Barndorff-Nielsen, O. (1997). Normal inverse Gaussian distributions and stochastic volatility modelling. Scandinavian Journal of Statistics, 24, 1–13.CrossRefGoogle Scholar
Barndorff-Nielsen, O. and Shepard, N. (2004). Econometric analysis of realized covariation: High frequency based covariation, regression and correlation in financial economics. Econometrica, 72, 885–925.CrossRefGoogle Scholar
Barndorff-Nielsen, O. and Stelzer, R. (2005). Absolute moments of generalized hyperbolic distributions and approximate scaling of normal inverse Gaussian Lévy processes. Scandinavian Journal of Statistics, 32, 617–637.CrossRefGoogle Scholar
Bäuerle, N. (2004). Traditional versus non-traditional reinsurance in a dynamic setting. Scandinavian Actuarial Journal, 5, 355–371.Google Scholar
Bäuerle, N. (2005). Benchmark and mean-variance problems for insurers. Mathematical Methods of Operations Research, 62, 159–165.CrossRefGoogle Scholar
Bäuerle, N. and Griibel, R. (2005). Multivariate counting processes. Copulas and Beyond. Astin Bulletin, 35, 379–408.CrossRefGoogle Scholar
Bauwens, L., Lubrano, M. and Richard, J.-F. (1999). Bayesian Inference in Dynamic Econometric Models. Oxford: Oxford University Press.Google Scholar
Beirlant, J. (2004). Extremes. In Teugels, J. and Sundt, B. (eds), Encyclopedia of Actuarial Science. Chichester: John Wiley & Sons pp. 654–661.Google Scholar
Beirlant, J. and Goegebeur, Y. (2004). Local polynomial maximum likelihood estimation for pareto type distributions. Journal of Multivariate Analysis, 89, 97–118.CrossRefGoogle Scholar
Beirlant, J., Teugels, J.L. and Vynckier, P. (1996). Practical Analysis of Extreme Values. Leuven: Leuven University Press.Google Scholar
Beirlant, J., Goegebeur, Y., Segers, J. and Teugels, J. (2004). Statistics of Extremes: Theory and Applications. Chichester: John Wiley & Sons.CrossRefGoogle Scholar
Benninga, S. (2008). Financial Modelling, 3rd edn. Cambridge, MA: MIT Press.Google Scholar
Benth, F. (2004). Option Theory with Stochastic Analysis. An Introduction to Mathematical Finance. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Beran, J. (1994). Statistics for Long-Memory Processes. New York: Chapman & Hall.Google Scholar
Berkelaar, A., Dert, C., Oldenkamp, B. and Zhang, S. (2002). A primal-dual decomposition-based interior point approach to two-stage stochastic linear programming. Operations Research, 50, 904–915.CrossRefGoogle Scholar
Bernardo, J.M. and Smith, A.F.M. (2009). Bayesian Theory. Chichester: John Wiley & Sons.Google Scholar
Best, P. J. (1978). Letter to the Editor. Applied Statistics, 28, 181.Google Scholar
Bhar, R., Chiarella, C. and Runggaldier, W. J. (2002). Estimation in models of the instantaneous short term interest rate by use of a dynamic Bayesian algorithm. In Sandmann, K. and Schönbucher, P. J. (eds), Advances in Finance and Stochastics. Berlin: Springer-Verlag; pp. 177–195.Google Scholar
Bingham, N. H. and Kiesel, R. (2004). Risk-neutral Valuation, Pricing and Hedging of Financial Derivatives, 2nd edn. London: Springer-Verlag.Google Scholar
Björk, T. (2006). Arbitrage Theory in Continuous Time, 2nd edn. Oxford: Oxford University Press.Google Scholar
Black, F. and Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81, 637–654.CrossRefGoogle Scholar
Blum, P. and Dacorogna, M. (2004). DFA - dynamic financial analysis. In Teugels, J. and Sundt, B. (eds), Encyclopedia of Actuarial Science. Chichester: John Wiley & Sons; pp. 505–519.Google Scholar
Bodie, Z. and Davis, E. P. (eds) (2000). The Foundation of Pension Finance, Vols I and II. Cheltenham: Edward Elgar Publishing.
Boland, P. J. (2007). Statistical and Probabilistic Methods in Actuarial Science. Boca Raton, F1: Chapman & Hall/CRC.Google Scholar
Bolia, N. and Juneja, S. (2005). Monte Carlo methods for pricing financial options. Sadhana, 30, 347–385.CrossRefGoogle Scholar
Bollerslev, T. (2001). Financial econometrics: past developments and future challenges. Journal of Econometrics, 100, 41–51.CrossRefGoogle Scholar
Bølviken, E. (2004) Stochastic simulation. In Teugels, J. and Sundt, B. (eds), Encyclopedia of Actuarial Science. Chichester: John Wiley & Sons; pp. 1613–1615.Google Scholar
Booth, P., Chadburn, R., Cooper, D., Haberman, s. and James, D. (1999). Modern Actuarial Theory and Practice. London: Chapman & Hall/CRC.Google Scholar
Bos, C. S. and Shephard, N. (2006). Inference for adaptive time series models: Stochastic volatility and conditionally gaussian state space form. Econometric Reviews, 25, 219–244.CrossRefGoogle Scholar
Box, G. E. P. and Muller, M. E. (1958). A note on the generation of random normal deviates. Annals of Mathematical Statistics, 29, 610–611.CrossRefGoogle Scholar
Boyarchenko, S.I. and Levendorskiῐ, Z. (2002) Non-Gaussian Merton-Black-Scholes Theory. River Edge, NJ: World Scientific.CrossRefGoogle Scholar
Brennan, M. J. and Schwartz, E. S. (1976). The pricing of equity-linked insurance policies with an asset value guarantee. Journal of Financial Economics, 3, 195–213.CrossRefGoogle Scholar
Brigo, D. and Mercurio, F. (2001). Interest Rate Models. Theory and Practice. Berlin: Springer-Verlag.Google Scholar
Brito, M. and Freitas, A. C. M. (2006). Weak convergence of bootstrap geometric-type estimator with applications to risk theory. Insurance: Mathematics and Economics, 38, 571–584.Google Scholar
Brockwell, P. J. and Davis, R. A. (2011). Introduction to Time Series and Forecasting. New York: Springer-Verlag.Google Scholar
Brouhns, N., Denuit, M. and Van Keilegom, I. (2005). Bootstrapping the Poisson log-linear model for forecasting. Scandinavian Actuarial Journal, 3, 212–224.Google Scholar
Biihlmann, H. and Gisler, A. (2005). A Course in Credibility Theory and its Applications. Berlin: Springer-Verlag.Google Scholar
Bühlmann, H. and Straub, E. (1970). Glaubwüdigkeit für Schadebsätze. Mitteleiungen der Vereinigung Scweizerischer Versicherungsmatematiker, 70, 111–133.Google Scholar
Buhmann, M. D. (2003). Radial Basis Functions: Theory and Implementations. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Butenko, S., Golodnikov, A. and Uryasev, S. (2005). Optimal security liquidation algorithms. Computational Optimization and Applications, 32, 9–27.CrossRefGoogle Scholar
Butt, Z. and Haberman, S. (2004). Application of frailty-based mortality models using generalized linear models. Astin Bulletin, 34, 175–197.CrossRefGoogle Scholar
Cai, J. and Tan, K. s. (2007). Optimal retention for a stop-loss reinsurance under the VaR and CTE risk measure. Astin Bulletin, 37, 93–112.CrossRefGoogle Scholar
Cairns, A. (2000a). A discussion of parameter and model uncertainty in insurance. Insurance: Mathematics and Economics, 27, 313–330.Google Scholar
Cairns, A. (2000b). Some notes on the dynamics and optimal control of stochastic pension fund models in continuous Time. Astin Bulletin, 30, 19–55.CrossRefGoogle Scholar
Cairns, A. (2004). Interest Rate Models: An Introduction. Princeton, NJ: Princeton University Press.Google Scholar
Cairns, A., Blake, D. and Dowd, K. (2006). Pricing death: Frameworks for the valuation and securization of mortality risk. Astin Bulletin, 36, 79–120.CrossRefGoogle Scholar
Cairns, A., Blake, D., Dowd, K., Couglan, G. D., Epstein, D. and Khalaf-Allah, M. (2011). Mortality density forecasts. An analysis of six stochastic mortality models. Insurance: Mathematics and Economics, 48, 355–367.Google Scholar
Capinski, M. and Zastavniak, T. (2003). Mathematics for Finance. An Introduction to Financial Engineering. London: Springer-Verlag.Google Scholar
Carlin, B.P. (1992). State space modelling of nonstandard actuarial time series. Insurance: Mathematics and Economics, 11, 209–222.Google Scholar
Carmona, R. A. (2004). Statistical Analysis of Financial Data in S-plus. New York: Springer-Verlag.Google Scholar
Carmona, R. A. and Tehranchi, M. R. (2006). Interest Rate Models. An Infinite Dimensional Stochastic Analysis Perspective. Berlin: Springer-Verlag.Google Scholar
Carriére, J. F. (2000). Bivariate survival models for coupled lives. Scandinavian Actuarial Journal, 1, 17–32.Google Scholar
Casti, J. (1997). Would-be World: How Simulation is Changing Frontiers of Science. New York: John Wiley & Sons.Google Scholar
Castillo, E., Hadi, A. S., Balakrishnan, N. and Sarabia, J.-M. (2005). Extreme Value and Related Models with Applications in Engineering and Science. Hoboken, NJ: John Wiley & Sons.Google Scholar
Chan, N. H. and Wong, H. Y. (2006). Simulation Techniques in Financial Risk Management. Hoboken, NJ: John Wiley & Sons.CrossRefGoogle Scholar
Chen, H. C. and Asau, Y. (1974). On generating random variates from an empirical distribution. AIEE Transactions, 6, 163–166.Google Scholar
Chen, X. and Fan, Y. (2006). Estimation of copula-based semi-parametric time series models. Journal of Econometrics, 130, 307–335.CrossRefGoogle Scholar
Cheng, R. C. H. and Feast, G. M. (1979). Some simple gamma variable generators. Applied Statistics, 28, 290–295.CrossRefGoogle Scholar
Cherubini, U., Luciano, E. and Vecchiato, W. (2004). Copula Methods in Finance. Chichester: John Wiley & Sons.CrossRefGoogle Scholar
Chib, s. (2004). Markov Chain Monte Carlo technology. In Gentle, J.E., Härdle, W. and Mori, Y. (eds), Handbook of Computational Statistics. Concepts and Methods. New York: Springer-Verlag; pp. 71–102.Google Scholar
Chib, S., Nardari, F. and Shepard, N. (2006). Analysis of high dimensional stochastic volatility models. Journal of Econometrics, 134, 341–371.CrossRefGoogle Scholar
Chiu, M. C. and Li, D. (2006). Asset and liability management under a continuous-time mean-variance optimization framework. Insurance: Mathematics and Economics, 39, 330–355.Google Scholar
Chivers, I. and Sleightholme, J. (2006). Introduction to Programming with Fortran. London: Springer-Verlag.Google Scholar
Christof fersen, R. (2003). Elements of Financial Risk Management. San Diego, CA: Academic Press.Google Scholar
Commandeur, J.J.F. and Koopman, S.J. (2007). An Introduction to State Space Time Series Analyis. Oxford: Oxford University Press.Google Scholar
Congdon, P. (2003). Applied Bayesian Modelling. Chichester: John Wiley & Sons.CrossRefGoogle Scholar
Cont, R. (2006). Model uncertainty and its impact on the pricing of derivative instruments. Mathematical Finance, 16, 519–547.CrossRefGoogle Scholar
Cont, R. and Tankov, R. (2004). Financial Modelling with Jump Processes. Boca Raton, FL: Chapman & Hall/CRC.Google Scholar
Cook, R. D. and Weisberg, S. (1982). Residuals and Influence in Regression. London: Chapman & Hall.Google Scholar
Copeland, T. E., Weston, J. F. and Shastri, K. (2005). Financial Theory and Corporate Policy, 4th edn. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Cornil, J.-M. and Testud, P. (2000). Introduction to Maple V. Berlin: Springer-Verlag.Google Scholar
Cox, D. R. (1955). Some statistical methods connected with series of events. Journal of the Royal Statistical Society, Series B, 17, 129–164.Google Scholar
Cox, J., Ingersoll, J. and Ross, S. (1985). A theory of the term structure of interest rates. Econometrica, 53, 385–407.CrossRefGoogle Scholar
Czado, C., Delwarde, A. and Denuit, M. (2005). Bayesian Poisson log-bilinear mortality projections. Insurance: Mathematics and Economics, 36, 260–284.Google Scholar
Dagpunar, J. S. (1989). An easily implemented generalised inverse Gaussian generator. Communications in Statistics. Simulation and Computation, 18, 703–710.CrossRefGoogle Scholar
Dagpunar, J. S. (2007). Simulation and Monte Carlo with Applications in Finance and MCMC. Chichester: John Wiley & Sons.CrossRefGoogle Scholar
Dahl, M. (2004). Stochastic mortality in life Insurance: Market reserves and mortality-linked insurance contracts. Insurance: Mathematics and Economics, 35, 113–136.Google Scholar
Dana, R.-A. and Jeanblanc-Picqué, M. (2003). Financial Markets in Continuous Time. Berlin: Springer-Verlag.Google Scholar
Danthine, J.-P. and Donaldson, J. B. (2005). Intermediate Financial Theory. San Diego, CA: Academic Press.Google Scholar
Dassios, A. and Jang, J.-W. (2003). Pricing of catastrophe reinsurance and derivatives using the cox process with shot noise intensity. Finance and Stochastics, 7, 73–95.CrossRefGoogle Scholar
Dassios, A. and Jang, J.-W. (2005). Kalman-Busy filtering for linear systems driven by the cox process with shot noise intensity and its Application to the pricing of reinsurance contracts. Journal of Applied Probability, 42, 93–107.CrossRefGoogle Scholar
David, H. A. (1981) Order Statistics, 2nd edn. New York: John Wiley & Sons.Google Scholar
Davison, A. C. and Hinkley, D. V. (1997). Bootstrap Methods and their Application. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Davison, A. C. and Smith, R. L. (1990). Models for exceedances over high thresholds. Journal of the Royal Statistical Society, Series B, 5, 393–442.Google Scholar
Daykin, c. D., Pentikäinen, T. and Pesonen, M. (1994). Practical Risk Theory for Actuaries. London: Chapman & Hall/CRC.Google Scholar
De Alba, E. (2004). Bayesian claims reserving. In Teugels, J. and Sundt, B. (eds), Encyclopedia of Actuarial Science. Chichester: John Wiley & Sons; pp. 146–153.Google Scholar
De Alba, E. (2006). Claims reserving when there are negative values in the run off triangle: Bayesian analysis the three-parameter log-normal distribution. North American Actuarial Journal, 10, 45–59.CrossRefGoogle Scholar
Deb, P., Munkin, M. K. and Trivedi, P. K. (2006). Private insurance, selection and health care use: A Bayesian analysis of a Roy-type model. Journal of Business & Economic Statistics, 24, 403–415.CrossRefGoogle Scholar
De Haan, L. and Ferreira, F. (2006). Extreme Value Theory: An Introduction. New York: Springer-Verlag.CrossRefGoogle Scholar
De Jong, P. and Ferris, S. (2006). Adverse selection spirals. Astin Bulletin, 36, 589–628.Google Scholar
De Jong, P. and Heller, G. Z. (2008). Generalized Linear Models for Insurance Data. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
De Jong, P. and Tickle, L. (2006). Extending Leesarter mortality forecasting. Mathematical Population Studies, 13, 1–18.CrossRefGoogle Scholar
De Jong, P. and Zehnwirth, B. (1983). Credibility theory and the Kalman filter. Insurance: Mathematics and Economics, 2, 281–286.Google Scholar
De Lange, P.E., Fleten, S.-E. and Gaivorinsky, A.A. (2004). Modeling financial reinsurance in the casualty insurance business via stochastic programming. Journal of Economic Dynamics and Control, 28, 991–1012.CrossRefGoogle Scholar
Delbaen, F. and Schachermayer, W. (2006). The Mathematics of Arbitrage. Berlin: Springer-Verlag.Google Scholar
Denuit, M. and Lang, S. (2004). Non-life rate making with Bayesian GAMs. Insurance: Mathematics and Economics, 35, 627–647.Google Scholar
Denuit, M., Maréchal, x., Pitrebois, S. and Wahlin, J.-F. (2007). Actuarial Modelling of Claim Counts: Risk Classification, Credibility and Bonus-Malus Systems. Chichester: John Wiley & Sons.CrossRefGoogle Scholar
De Santis, G., Litterman, B., Vesval, A. and Winkelman, K. (2003). Covariance matrix estimation. In Litterman, B. (ed.), Modern Investment Management: An Equilibrium Approach. Hoboken, NJ: John Wiley & Sons; pp. 224–248.Google Scholar
Devroye, L. (1986). Non-uniform Random Variate Generation. New York: Springer-Verlag.CrossRefGoogle Scholar
Devroye, L. and Györfi, L. (1985). Nonparametric Density Estimation>: The L1 View. New York: John Wiley & Sons.Google Scholar
Dickson, D. C. M. (2005). Insurance Risk and Ruin. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Dickson, D. C. M. and Waters, H. (2006). Optimal dynamic reinsurance. Astin Bulletin, 36, 415–432.CrossRefGoogle Scholar
Dimakos, X. K. and Frigessi, A. (2002). Bayesian premium rating with latent stracture. Scandinavian Actuarial Journal, 3, 162–184.Google Scholar
Djehiche, B. and Hörfelt, p. (2005). Standard approaches to asset & liability risk. Scandinavian Actuarial Journal, 5, 377–400.Google Scholar
Dobson, A. J. and Barnett, A. G. (2008). An Introduction to Generalized Linear Models, 3rd edn. Boca Raton, FL: CRC Press.Google Scholar
Doucet, A., De Freitas, N. and Gordon, N. (eds) (2001). Sequential Monte Carlo in Practice. New York: Springer-Verlag.CrossRef
Dunteman, G. H. and Ho, M.-H. R. (2006). An Introduction to Generalized Linear Models. Thousand Oaks, CA: Sage Publications.CrossRefGoogle Scholar
Dupuis, D. J. (1998). Exceedances over high thresholds: A guide to threshold selection. Extremes, 1, 251–261.Google Scholar
Durbin, J. and Koopman, S.J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press.Google Scholar
Efron, B. (1979). Bootstrap methods: An other look at the jacknife. Annals of Statistics, 7, 1–26.CrossRefGoogle Scholar
Efron, B. and Tibshirani, R. J. (1993). An Introduction to the Bootstrap. New York: Chapman & Hall.CrossRefGoogle Scholar
Elliott, R. J. and Kopp, P. E. (2005). Mathematics of Financial Markets, 2nd edn. New York: Springer-Verlag.Google Scholar
Ellis, T.M.R., Philips, I.R. and Lahey, T.M. (1994). Fortran 90 Programming. Harlow: Addison-Wesley.Google Scholar
Embrechts, P. and Maejima, M. (2002). Selfsimilar Processes. Princeton, NJ: Princeton University Press.Google Scholar
Embrechts, P., Kliippelberg, C. and Mikosch, T. (1997). Modelling Extremal Events for Insurance and Finance. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Embrechts, P., Lindskog, F. and Mcneil, A. (2003). Modelling dependence with copulas and applications to risk management. In Rachev, S. T. (ed.), Handbook of Heavy Tailed Distributions in Finance. Amsterdam: Elsevier; pp. 329–384.Google Scholar
England, P. and Verrall, R. (1999). Analytic and bootstrap estimates of prediction errors in claimreserving. Insurance: Mathematics and Economics, 25, 281–293.Google Scholar
England, P. D. and Verrall, R. J. (2006). Predictive distributions of outstanding liabilities in general insurance. Annals of Actuarial Science, 1, 221–270.CrossRefGoogle Scholar
Engle, R.F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation. Econometrica, 50, 987–1007.CrossRefGoogle Scholar
Escarela, G. and Carrière, J. F. (2006). A bivariate model of claim frequencies and severities. Journal of Applied Statistics, 33, 867–883.CrossRefGoogle Scholar
Evans, J. R. and Olson, D. L. (2002). Introduction to Simulation and Risk Analysis. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Evans, M. and Schwarz, T. (2000). Approximating Integrals via Monte Carlo and Deterministic Methods. Oxford: Oxford University Press.Google Scholar
Fabozzi, F. J. (ed.) (2002). Interest Rate, Term Structure and Valuation Modeling. Hoboken, NJ: John Wiley & Sons.
Falk, M., Hiisler, J. and Reiss, R.-D. (2010). Laws of Small Numbers: Extremes and Rare Events. 3rd edn. Basel: Birkhauser-Verlag.Google Scholar
Fang, K.T., Kotz, S. and Ng, K.w. (1990). Symmetric Multivariate and Related Distributions. London: Chapman & Hall.CrossRefGoogle Scholar
Faraway, J. J. (2006). Extending the Linear Model with R, generalized, mixed effects and non-parametric regression models. Boca Raton, FL: Chapman & Hall/CRC.Google Scholar
Feller, W. (1968). An Introduction to Probability Theory and its Applications, New York: Vol. I. John Wiley & Sons.Google Scholar
Feller, W. (1971). An Introduction to Probability Theory and its Applications, Vol. II. New York: John Wiley & Sons.Google Scholar
Ferguson, N. (2008). The Ascent of Money. A Financial History of the World. London: Penguin Press.Google Scholar
Fieller, E. C. and Hartley, H. O. (1954). Sampling with control variables. Biometrika, 41, 494–501.CrossRefGoogle Scholar
Finkelstädt, B. and Rootzén, H. (eds) (2004). Extreme Values in Finance, Telecommunications and the Environment. Boca Raton, FL: Chapman & Hall/CRC.
Finkelstein, A. and Poterba, J. (2002). Selection effects in the United Kingdom Individual annuities market. The Economic Journal, 112, 28–50.CrossRefGoogle Scholar
Fishman, G. S. (2001). Discrete-Event Simulation, Modeling, Programming and Analysis. New York: Springer-Verlag.CrossRefGoogle Scholar
Fishman, G. S. (2006). A First Course in Monte Carlo. Belmont, CA: Thomson Brooks/Cole.Google Scholar
Fletcher, R. (1987). Practical Methods of Optimization. Chichester: John Wiley & Sons.Google Scholar
Fleten, S.-E., Høyland, K. and Wallace, S. W. (2002). The performance of stochastic dynamic and fixed mix portfolios models. European Journal of Operational Research, 140, 37–39.CrossRefGoogle Scholar
Fomby, T. B. and Carter Hill, R. (eds) (2003). Maximum Likelihood of Misspecified Models. Twenty Years Later. Amsterdam: Elsevier.
Forfar, D. O. (2004). Life table. In Teugels, J. and Sundt, B. (eds), Encyclopedia of Actuarial Science, Chichester: John Wiley & Sons; pp. 1005–1009.Google Scholar
Fornari, F. and Mele, A. (2000). Stochastic Volatility in Financial Markets. Crossing the Bridge to Continuous Time. Dordrecht: Kluwer.CrossRefGoogle Scholar
Fouque, J.-P., Papanicolaou, G. and Sircar, K.R. (2000). Derivatives in Financial Markets with Stochastic Volatility. Cambridge: Cambridge University Press.Google Scholar
Franke, J., Härdle, w. and Hafner, C. (2004). Statistics of Financial Markets. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Franses, p. H. and Van Dijk, D. (2000). Non-linear Time Series Models in Empirical Finance. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Frees, E. (2003). Multivariate credibility for aggregate loss models. North American Actuarial Journal, 7, 13–37.CrossRefGoogle Scholar
Frees, E.W. and Valdez, E.A. (1998). Understanding relationships using copulas. North American Actuarial Journal, 2, 1–25.CrossRefGoogle Scholar
Frees, E. and Wang, P. (2006). Copula credibility for aggregate loss models. Insurance: Mathematics and Economics, 38, 360–373.Google Scholar
Fries, C. (2007). Mathematical Finance. Theory, Modelling, Implementation. Hoboken, NJ: John Wiley & Sons.Google Scholar
Frigessi, A., Haug, O. and Rue, H. (2002). A dynamic mixture model for unsupervised tail estimation without threshold selection. Extremes, 5, 219–235.CrossRefGoogle Scholar
Fu, M. and Hu, J.-Q. (1997). Conditional Monte Carlo. Gradient Estimation and Optimization Applications. Boston, MA: Kluwer.CrossRefGoogle Scholar
Fuh, C.-D. (2006). Efficient likelihood estimation in state space models. Annals of Statistics, 34, 2026–2068.CrossRefGoogle Scholar
Gajek, L. (2005). Axiom of solvency and portfolio immunization under random interest rates. Insurance: Mathematics and Economics, 36, 317–328.Google Scholar
Gajek, L. and Zagrodny, D. (2004). Optimal reinsurance under general risk measures. Insurance: Mathematics and Economics, 34, 227–240.Google Scholar
Gamerman, D. and Lopes, H.F. (2006). Markov Chain Monte Carlo. Stochastic Simulation for Bayesian Inference. Boca Raton, FL: Chapman & Hall/CRC.Google Scholar
Gelb, A. (ed.) (1974). Applied Optimal Estimation. Cambridge, MA: MIT Press.
Genest, c. and MacKay, J. (1986). Thejoy of copulas: Bivariate distributions with uniform marginals. The American Statistician, 40, 280–283.Google Scholar
Genon-Catalot, V., Jeantheau, T. and Larédo, C. (2000). Stochastic volatility models as hidden markov models and statistical applications. Bernoulli, 6, 1051–1079.CrossRefGoogle Scholar
Gentle, J.E. (1998). Numerical Linear Algebrafor Applications in Statistics. New York: Springer-Verlag.CrossRefGoogle Scholar
Gentle, J.E. (2002). Elements of Computational Statistics. New York: Springer-Verlag.Google Scholar
Gentle, J. E. (2003). Random Number Generation and Monte Carlo Methods, 2nd edn. New York: Springer-Verlag.Google Scholar
Gentle, J.E., Härdle, W. and Mori, Y. (eds) (2004). Handbook of Computational Statistics. Concepts and Methods. New York: Springer-Verlag.
Gerber, H.U. (1997). Life Insurance Mathematics, 3rd edn. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Gerber, H. U. and Shiu, E. S. W. (2003). Geometrie brownian motion models for assets and liabilities: From pension funding to optimal dividends. North American Actuarial Journal, 7, 37–51.CrossRefGoogle Scholar
Gilks, W.R., Richardson, S. and Spiegelhalter, D.J. (eds) (1996). Markov Chain Monte Carlo in Practice. London: Chapman & Hall.
Gill, R E., Murray, W. and Wright, M. H. (1981). Practical Optimization. London: Academic Press.Google Scholar
Gisler, A. (2006). The estimation error in the chain ladder reserving method: A bayesian approach. Astin Bulletin, 36, 554–565.CrossRefGoogle Scholar
Glasserman, p. (2004). Monte Carlo Methods in Financial Engineering. New York: Springer-Verlag.Google Scholar
Gloter, A. (2007). Efficient estimation of drift parameters in stochastic volatility models. Finance and Stochastics, 11, 495–519.CrossRefGoogle Scholar
Góméz-Déniz, E., Vázquez-Polo, F. and Pérez, J. (2006). A note on computing bonus-malus insurance premiums using a hierarchical bayesian framework. Sociedad de Estadísticae Investigación Operativa, 15, 345–359.Google Scholar
Gondzio, J. and Kouwenberg, R. (2001). High-performance computing for asset-liability management. Operations Research, 49, 879–891.CrossRefGoogle Scholar
Goovaerts, M. J. and Hoogstad, W. J. (1987). Credibility Theory. Surveys of Actuarial Studies, Vol. 4, Rotterdam: Nationale-Nederlanden N.V.Google Scholar
Grama, A., Gupta, A., Karypis, G. and Kumar, V. (2003). Introduction to Parallel Computing, 2nd edn. Harlow: Pearson/Addison-Wesley.Google Scholar
Grandell, J. (2004). Poisson processes. In Teugels, J. and Sundt, B. (eds), Encyclopedia of Actuarial Science. Chichester: John Wiley & Sons, pp. 1296–1301.Google Scholar
Greenwood, M. and Yule, G. U. (1920). An inquiry into the nature of frequency-distributions of multiple happenings, with particular reference to the occurrence of multiple attacks of disease or repeated accidents. Journal of Royal Statistical Society, 83, 255–279.CrossRefGoogle Scholar
Guerra, M. and Centeno, M. (2008). Optimal reinsurance policy: The adjustment coefficient and the expected utility criteria. Insurance: Mathematics and Economics, 42, 529–539.Google Scholar
Haberman, S. and Pitacco, E. (1999). Actuarial Modelsfor Disability Insurance. Boca Raton, FL: Chapman & Hall/CRC.Google Scholar
Haberman, S. and Renshaw, A. E. (1996). Generalized linear models and actuarial science. The Statistician, 45, 407–436.CrossRefGoogle Scholar
Haberman, S. and Sibbett, T.A. (eds) (1995). History of Actuarial Science. London: Pickering and Chatto.
Hafner, R. (2004). Stochastic Implied Volatility. A Factor-Based Model. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Hall, P. (1992). The Bootstrap and Edgeworth Expansion. New York: Springer-Verlag.CrossRefGoogle Scholar
Hämmerlin, G. and Hof fmann, K.-H. (1991). Numerical Mathematics. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Hammersley, J. M. and Handscomb, D. C. (1964). Monte Carlo Methods. London: Methuen.CrossRefGoogle Scholar
Hammersley, J. M. and Morton, K. W. (1956). A new Monte Carlo technique: Antithetic variates. Proceedings of the Cambridge Philosophical Society, 52, 449–475.CrossRefGoogle Scholar
Hanson, D.R. (1997). CInterfaces and Implementations: Techniquesfor Creating Reusable Sof tware. Reading, MA: Addison-Wesley.Google Scholar
Harbison, S. P. and Steele, G. L. (2002). A Reference Manual, 5th edn. Engle-wood Cliffs, NJ: Prentice-Hall.Google Scholar
Hardy, M. (2002). Bayesian risk management for equity-linked insurance. Scandinavian Actuarial Journal, 3, 185–211.Google Scholar
Hardy, M. (2003). Investment Guarantees. Modeling and Risk Managementfor Equity-Linked Insurance. Hoboken, NJ: John Wiley & Sons.Google Scholar
Harel, A. and Harpaz, G. (2007). Fair actuarial values for deductible insurance policies in the presence of parameter uncertainty. International Journal of Theoretical and Applied Finance, 10, 389–397.CrossRefGoogle Scholar
Harvey, A., Ruiz, E. and Shephard, N. (1994). Multivariate stochastic variance models. Review of Economic Studies, 61, 247–264.CrossRefGoogle Scholar
Harvey, A., Koopman, S. J. and Shephard, N. (eds) (2004). State Space and Unobserved Components Models: Theory and Applications. Cambridge: Cambridge University Press.CrossRef
Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97–109.CrossRefGoogle Scholar
Herold, U. and Maurer, R. (2006). Portfolio choice and estimation risk: A comparison of bayesian to heuristic approaches. Astin Bulletin, 36, 135–160.Google Scholar
Hilbe, J. M. (2007). Negative Binomial Regression. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Hilli, P., Koivu, M., Pennanen, T. and Ranne, A. (2007). A stochastic programming model for asset liability management of a finnish pension company. Annals of Operations Research, 152, 115–139.CrossRefGoogle Scholar
Hoedemakers, T., Beirlant, J., Goovaerts, M. J. and Dhaene, J. (2005). On the distribution of discounted loss reserves using generalized linear models. Scandinavian Actuarial Journal, 1, 25–45.Google Scholar
Højgaard, B. and Taksar, M. (2004). Optimal dynamic portfolio selection for a corporation with controllable risk and dividend distribution policy. Quantitative Finance, 4, 315–327.CrossRefGoogle Scholar
Hörmann, w., Leydold, J. and Derflinger, G. (2004). Automatic Non-Uniform Random Variate Generation. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Howison, S. D., Kelly, F. P. and Wilmott, P. (eds) (1995). Mathematical Models in Finance. London: Chapman & Hall.
Huang, X., Song, L. and Liang, Y. (2003). Semiparametric credibility ratemaking using a piecewise linear prior. Insurance: Mathematics and Economics, 33, 585–593.Google Scholar
Huber, P. (1967). The behaviour of maximum likelihood estimates under nonstandard conditions. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA: University of California Press; pp. 221–233.Google Scholar
Hughston, L. (ed.) (2000). The New Interest Rate Models. London: RISK Books.
Hull, J. C. (2006). Options, Futures and Other Derivatives, 6th edn. Upper Saddle River, NJ: Prentice Hall, New Jersey.Google Scholar
Hunt, B.R., Lipsman, R.L. and Rosenberg, J. (2001). A Guide to MATLAB:for Beginners and Experienced Users. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Hiirlimann, w. (2002). On immunization, stop-loss order and the maximum shiu measure. Insurance: Mathematics and Economics, 31, 315–325.Google Scholar
Irgens, C. and Paulsen, J. (2004). Optimal control of risk exposure, reinsurance and investments for insurance portfolios. Insurance: Mathematics and Economics, 35, 21–51.Google Scholar
Jäckel, P. (2002). Monte Carlo Methods in Finance. Chichester: John Wiley & Sons.Google Scholar
Jacobsen, M. (2006). Point Process Theory and Applications. Marked Point and Piecewise Deterministic Processes. Boston, MA: Birkhäuser.Google Scholar
James, J. and Webber, N. (2000). Interest Rate Modelling. Chichester: John Wiley & Sons.Google Scholar
Jensen, J.L. (1995). Saddle point Approximations. New York: Oxford University Press.Google Scholar
Jewell, W. S. (1974). Credible means are exact Bayesian for exponential families. Astin Bulletin, 8, 77–90.CrossRefGoogle Scholar
Joe, H. (1997). Multivariate Models and Dependence Concepts. London: Chapman & Hall.CrossRefGoogle Scholar
Johnson, N.L., Kotz, S. and Balakrishnan, N. (1994). Continuous Univariate Distributions. New York: John Wiley & Sons.Google Scholar
Johnson, N. L., Kotz, S. and Balakrishnan, N. (1997). Discrete Multivariate Distributions. New York: John Wiley & Sons.Google Scholar
Johnson, N.L., Kemp, A.W. and Kotz, S. (2005). Univariate Discrete Distributions, 3rd edn. Hoboken, NJ: John Wiley & Sons.CrossRefGoogle Scholar
Jørgensen, B. and Paes De Souza, M. C. (1994). Fitting Tweedie's compound poisson model to insurance claims data. Scandinavian Actuarial Journal, 1, 69–93.Google Scholar
Jorion, P. (2001). Value at Risk: The New Benchmark for Managing Financial Risk, 2nd edn. New York: McGraw-Hill.Google Scholar
Josa-Fombellida, R. and Rincón-Zapatero, J.P. (2006). Optimal investment decisions with a liability: The case of defined benefit pension plans. Insurance: Mathematics and Economics, 39, 81–98.Google Scholar
Josa-Fombellida, R. and Rincón-Zapatero, J. P. (2008). Mean-variance portfolio and contribution selection in stochastic pension funding. European Journal of Operational Research, 187, 120–137.CrossRefGoogle Scholar
Journel, A. G. and Huijbregts, C. J. (1978). Mining Geostatistics. New York: Academic Press.Google Scholar
Kaas, R.Dannenburg, D. and Goovaerts, M. (1997). Exact credibility for weighted observations. Astin Bulletin, 27, 287–295.CrossRefGoogle Scholar
Kaluszka, M. (2004). Mean-variance optimal reinsurance arrangements. Scandinavian Actuarial Journal, 1, 28–31.Google Scholar
Kaluszka, M. (2005). Truncated stop loss as optimal reinsurance agreementin One-period Models. Astin Bulletin, 35, 337–349.CrossRefGoogle Scholar
Kaminsky, K. (1987). Prediction of IBNR claim counts by modeling the distribution of report lags. Insurance: Mathematics and Economics, 6, 151–159.Google Scholar
Kaner, C., Falk, J. and Ngyuen, H. (1999). Testing Computer Sof tware, 2nd edn. Hoboken, NJ: John Wiley & Sons.Google Scholar
Karlin, s. and Taylor, H. M. (1975). A First Course in Stochastic Processes. New York: Academic Press.Google Scholar
Karlis, D. and Kostaki, A. (2002). Bootstrap techniques for mortality models. Bio-metrical Journal, 44, 850–866.Google Scholar
Karlis, D. and Lillestöl, J. (2004). Bayesian estimation of NIG models via Markov chain Monte Carlo methods. Applied Stochastic Models in Business and Industry, 20, 323–338.CrossRefGoogle Scholar
Kendall, M. G. and Stuart, A. (1977). The Advanced Theory of Statistics. Volume 1. Distribution Theory, 4th edn. London: Edward Arnold.Google Scholar
Kendall, M. G. and Stuart, A. (1979). The Advanced Theory of Statistics. Volume 2. Inference and Relationship, 4th edn. London: Edward Arnold.Google Scholar
Keyfitz, N. and Caswell, H. (2005). Applied Mathematical Demography, 3rd edn. New York: Springer-Verlag.Google Scholar
Kijima, M. (2003). Stochastic Processes with Applications to Finance. Boca Raton, FL: Chapman & Hall/CRC.Google Scholar
Kimberling, C. H. (1974). A probabilistic interpretation of complete monotonicity. Aequationes Mathematicae, 10, 152–164.CrossRefGoogle Scholar
Kinderman, A. J. and Monahan, J. F. (1977). Computer generation of random variables using ratio of uniform deviates. ACM Transactions of Mathematical Sof tware, 3, 257–260.Google Scholar
Kinderman, A. J. and Monahan, J. F. (1980). New methods for generating Student's t and Gamma variables. Computing, 25, 369–377.CrossRefGoogle Scholar
Kleiber, C. and Kotz, S. (2003). Statistical Size Distributions in Economic and Actuarial Sciences. Hoboken, NJ: John Wiley & Sons.CrossRefGoogle Scholar
Klugman, S. A. (1992). Bayesian Statistics in Actuarial Science: with Emphasis on Credibility. Dordrecht: Kluwer.CrossRefGoogle Scholar
Klugman, S.A. (2004). Continuous parametric distributions. In Teugels, J. and Sundt, B. (eds), Encyclopedia of Actuarial Science. Chichester: John Wiley & Sons, pp. 357–362.Google Scholar
Klugman, S. A. and Parsa, R. (1999). Fitting bivariate loss distributions with copulas. Insurance: Mathematics and Economics, 24, 139–148.Google Scholar
Klugman, S. A., Panjer, H. H. and Willmot, G. E. (2008). Loss Models: From Data to Decisions, 3rd edn. New York: John Wiley & Sons.CrossRefGoogle Scholar
Knight, J. and Satchell, S. (eds) (2001). Return Distributions in Finance. Oxford: Butterworth-Heinemann.
Knight, J. and Satchell, S. (eds) (2002). Forecasting Volatility in the Financial Markets, 2nd edn. Oxford: Butterworth-Heinemann.
Koissi, M.-C., Shapiro, A.F. and Högnäs, G. (2006). Evaluating and extending the Lee-Carter model for mortality forecasting: Bootstrap confidence interval. Insurance: Mathematics and Economics, 38, 1–20.Google Scholar
Kolb, R. W. and Overdahl, J. A. (2003). Financial Derivatives, 3rd edn. Hoboken, NJ: John Wiley & Sons.Google Scholar
Kontoghiorges, E. J., Rustem, B. and Siokos, S. (eds) (2002). Computational Methods in Decision-Making, Economics and Finance. Dordrecht: Kluwer.CrossRef
Kotz, S. and Nadarajah, S. (2000). Extreme Value Distributions: Theory and Applications. London: Imperial College Press.CrossRefGoogle Scholar
Kotz, S. and Nadarajah, S. (2004). Multivariatet Distributions and their Applications. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Kotz, S., Balakrishnan, N. and Johnson, N.L. (2000). Continuous, Multivariate, Distributions. Volume 1. Models and Applications, 2nd edn. New York: John Wiley & Sons.CrossRefGoogle Scholar
Kouwenberg, R. (2001). Scenario generation and stochastic programming models for asset liability management. European Journal of Operational Research, 134, 279–292.CrossRefGoogle Scholar
Krause, A. and Olson, M. (2005). The Basics of S-plus, 4th edn. New York: Springer-Verlag.Google Scholar
Krokhmal, P. and Uryasev, S. (2007). A sample-path approach to optimal position liquidation. Annals of Operations Research, 152, 193–225.CrossRefGoogle Scholar
Krokhmal, P., Uryasev, S. and Palmquist, J. (2002). Portfolio optimization with conditional value-at-risk objective and constraints. Journal of Risk, 4, 43–68.Google Scholar
Krvavych, Y. and Sherris, M. (2006). Enhancing reinsurer value through reinsurance optimization. Insurance: Mathematics and Economics, 38, 495–517.Google Scholar
Lamberton, D. and Lapeyre, B. (1996). Introduction to Stochastic Calculus Applied to Finance. Boca Raton, FL: Chapman & Hall/CRC.Google Scholar
Lancaster, H. O. (1957). Some properties of the bivariate normal distribution considered in the form of a contingency table. Biometrika, 44, 289–292.CrossRefGoogle Scholar
Landau, R. H. (ed.) (2005). A First Course in Scientific Computing: Symbolic, Graphic, and Numerical Modeling Using Maple, Java, Mathematica and Fortran 90. Princeton, NJ: Princeton University Press.
Lange, K. (1999). Numerical Analysis for Statisticians. New York: Springer-Verlag.Google Scholar
Lange, K. (2004). Optimization. New York: Springer-Verlag.CrossRefGoogle Scholar
Langtangen, H. P. (2003). Computational Partial Differential Equations: Numerical Methods and Diffpack Programming, 2nd edn. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Lawless, J.F. (1987). Negative binomial and mixed poisson regression. Canadian Journal of Statistics, 15, 209–225.CrossRefGoogle Scholar
Lee, R. D. and Carter, L. w. (1992). Modeling and forecasting us mortality (with discussion). Journal of the American Statistical Association, 87, 659–675.Google Scholar
Lee, J.-P. and Yu, M.-T. (2007). Valuation of catastrophe reinsurance with catastrophe bonds. Insurance: Mathematics and Economics, 41, 264–278.Google Scholar
Lee, P. J. and Wilkie, A. D. (2000). A comparison of stochastic asset models. Proceedings of AFIR 2000, Tromsø, Norway.
Lee, S.-Y., Poon, W.-Y. and Song, X.-Y. (2007). Bayesian analysis of the factor model with finance applications. Quantitative Finance, 7, 343–356.CrossRefGoogle Scholar
Lee, Y., Nelder, J. A. and Pawitan, Y. (2006). Generalized Linear Models with Random Effects: Unified Analysis via H-Likelihood. Boca Raton, FL: Chapman & Hall/CRC.CrossRefGoogle Scholar
Lehmann, E. and Casella, G. (1998). Theory of Point Estimation, 2nd edn. New York: Springer-Verlag.Google Scholar
Leippold, M., Trojani, F. and Vanini, p. (2004). A geometric approach to multi-period mean variance optimization of assets and liabilities. Journal of Economic Dynamics and Control, 28, 1079–1113.CrossRefGoogle Scholar
Levy, G. (2004). Computational Finance. Numerical Methodsfor Pricing Financial Instruments. Oxford: Butterworth-Heinemann.Google Scholar
Levy, M., Levy, H. and Solomon, S. (2000). Microscopic Simulation of Financial Markets. From Investor Behaviour to Market Phenomena. London: Academic Press.Google Scholar
Liang, Z. and Guo, J. (2007). Optimal proportional reinsurance and ruin probability. Stochastic Models, 23, 333–350.CrossRefGoogle Scholar
Lin, X. S. (2006). Introductory Stochastic Analysis for Finance and Insurance. Hoboken, NJ: John Wiley & Sons.CrossRefGoogle Scholar
Litterman, B. (2003). Beyond Equilibrium, the Black-Litterman approach. In Litterman, B. (ed.), Modern Investment Management: An Equilibrium Approach. Hoboken, NJ: John Wiley & Sons; pp. 76–88.Google Scholar
Liu, J. S. (2001). Monte Carlo Strategies in Scientific Computing. New York: Springer-Verlag.Google Scholar
Lø, C.H., Fung, W.K. and Zhu, Z.Y. (2006). Generalized estimating equations for variance and covariance parameters in regression credibility models. Insurance: Mathematics and Economics, 39, 99–113.Google Scholar
Longin, F. and Solnik, B. (2001). Extreme correlation of international equity markets. Journal of Finance, 56, 649–676.CrossRefGoogle Scholar
Luenberger, D. G. (1998). Investment Science. Oxford: Oxford University, Press.Google Scholar
Luo, Y., Young, V.R and Frees, E. W. (2004). Credibility ratemaking using collateral information. Scandinavian Actuarial Journal, 448–461.Google Scholar
Luo, S., Taksar, M. and Tsoi, A. (2008). On reinsurance and investment for large insurance portfolios. Insurance: Mathematics and Economics, 42, 434–444.Google Scholar
Lütkepohl, H. and Krätzig, M. (eds) (2004). Applied Time Series Econometrics. Cambridge: Cambridge University Press.CrossRef
Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer-Verlag, Berlin.CrossRefGoogle Scholar
Lyuu, Y. D. (2002). Financial Engineering and Computation. Principles, Mathematics, Algorithms. Cambridge: Cambridge University Press.Google Scholar
Maddala, G. S. and Rao, C.R. (1996). Statistical Methods in Finance, Handbook of Statistics 14. Amsterdam: Elsevier.Google Scholar
Makov, u. (2001). Principal applications of Bayesian methods in actuarial science: A perspective. North American Actuarial Journal, 5, 53–57.CrossRefGoogle Scholar
Mardia, K. V., Kent, J. T. and Bibby, J. M. (1979). Multivariate Analysis. New York: Academic Press.Google Scholar
Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7, 77–91.Google Scholar
Marsaglia, G.(1980). Generating random variables with a i-distribution. Mathematics of Computation, 34, 235–236.Google Scholar
Marshall, A. and Olkin, I. (1988). Families of multivariate distributions. Journal of American Statistical Association, 83, 834–841.CrossRefGoogle Scholar
McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models, 2nd edn. London: Chapman & Hall.CrossRefGoogle Scholar
McDonald, R. L. (2009). Derivatives Markets. Boston, MA: Addison-Wesley.Google Scholar
McLeish, D. L. (2005). Monte Carlo Simulation and Finance. Hoboken, NJ: John Wiley & Sons.Google Scholar
McMahon, D. and Topa, D. M. (2006). A Beginner's Guide to Mathematica. Boca Raton, FL: Chapman & Hall/CRC.Google Scholar
Merton, R.C. (1973). Theory of rational option pricing. Bell Journal of Economics and Management Science, 4, 141–183.CrossRefGoogle Scholar
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H. and Teller, E. (1953). Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21, 1087–1092. [Reprinted in Kotz S. and Johnson N.L. (eds) (1997). Breakthroughs in Statistics, Volume III. New York: Springer-Verlag; pp. 127-139].CrossRefGoogle Scholar
Migon, H. S. and Moura, F. A. S. (2005). Hierarchical Bayesian collective risk model: An application to health insurance. Insurance: Mathematics and Economics, 36, 119–135.Google Scholar
Mikosch, T. (2004). Non-Life Insurance Mathematics: An Introduction with Stochastic Processes. Berlin: Springer-Verlag.Google Scholar
Mikosch, T. (2006). Copulas: Facts and tales. Extremes, 9, 3–20.Google Scholar
Mikosch, T. and Stărică, C. (2000). Is it really long memory we see in financial returns? In Embrechts, P. (ed), Extremes and Risk Management. London: RISK Books, pp. 149–168.Google Scholar
Milevsky, M. A. and Promislow, D. (2001). Mortality derivatives and the Option to Annuitise. Insurance: Mathematics and Economics, 29, 299–318.Google Scholar
Mills, T. C. and Markellos, R. N. (2008). The Econometric Modelling of Financial Time Series, 3rd edn. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Mitchell, O. S. and McCarthy, D. (2002). Estimating international adverse selection in annuities. North American Actuarial Journal, 6, 38–54.CrossRefGoogle Scholar
Nakano, J. (2004). Parallel computing techniques. In Gentle, J.E., Härdle, W. and Mori, Y. (eds), Handbook of Computational Statistics. Concepts and Methods. New York: Springer-Verlag; pp. 237–266.Google Scholar
Nelsen, R. B. (2006). An Introduction to Copulas, 2nd edn. New York: Springer-Verlag.Google Scholar
Neftci, S. N. (2000). An Introduction to the Mathematics of Financial Derivatives, Second Edition. San Diego, CA: Academic Press.Google Scholar
Neuhaus, w. (1987). Early warning. Scandinavian Actuarial Journal, 128–156.Google Scholar
Niederreiter, H. (1992). Random Number Generation and Quasi-Monte Carlo Methods. Philadelphia, PA: SIAM.CrossRefGoogle Scholar
Nietert, B. (2003). Portfolio Insurance and model uncertainty. OR Spectrum, 25, 295–316.Google Scholar
Nordberg, R. (1989). Empirical Bayes in the unbalanced case: Basic theory and applications to insurance. Doctoral Thesis, Department of Mathematics, University of Oslo.Google Scholar
Nordberg, R. (1999). Ruin problems with assets and liabilities of Diffusion Type. Stochastic Processes and their Applications, 81, 255–269.Google Scholar
Nordberg, R. (2004a). Credibility theory. In Teugels, J. and Sundt, B. (eds), Encyclopedia of Actuarial Science, Chichester: John Wiley & Sons, pp. 398–406.Google Scholar
Nordberg, R. (2004b). Life insurance mathematics. In Teugels, J. and Sundt, B. (eds), Encyclopedia of Actuarial Science, Chichester: John Wiley & Sons, pp. 986–997.Google Scholar
Ntzoufras, I., Katsis, A. and Karlis, D. (2005). Bayesian assessment of the distribution of insurance claim counts using reversible jump MCMC. North American Actuarial Journal, 9, 90–108.CrossRefGoogle Scholar
Odeh, R. E. and Evans, J.O. (1974). Algorithm A570: The percentage points of the normal distribution. Applied Statistics, 23, 96–97.CrossRefGoogle Scholar
Ohlsson, E. and Johansson, B. (2006). Exact credibility and Tweedie models. Astin Bulletin, 36, 121–133.CrossRefGoogle Scholar
Oksendal, B. (2003). Stochastic Differential Equations: An Introduction with Applications, 6th edn. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Omori, Y., Chib, S., Shephard, N. and Nakajima, J. (2007). Stochastic volatility with leverage: Fast and efficient likelihood inference. Journal of Econometrics, 140, 425–449.CrossRefGoogle Scholar
Otto, S. R. and Denier, J. p. (2005). An Introduction to Programming and Numerical Methods in MATLAB. London: Springer-Verlag.Google Scholar
Owadally, M. I. (2003). Pension funding and the actuarial assumption concerning investment returns. Astin Bulletin, 33, 289–312.CrossRefGoogle Scholar
Owadally, M. I. and Haberman, s. (1999). Pension fund dynamics and gainsosses due to random Rates of investment returns. North American Actuarial Journal, 3, 105–117.Google Scholar
Panjer, H. (1981). Recursive evaluation of a family of compound distributions. Astin Bulletin, 12, 22–26.CrossRefGoogle Scholar
Panjer, H. (ed.) (1998). Financial Economics: with Applications to Investments, Insurance and Pensions. Schaumburg, IL: The Actuarial Foundation.
Panjer, H. and Willmot, G. E. (1992). Insurance Risk Models. Schaumburg, IL: Society of Actuaries.Google Scholar
Papi, M. and Sbaraglia, S. (2006). Optimal asset-liability management with constraints: A dynamic programming approach. Applied Mathematics and Computation, 173, 306–349.CrossRefGoogle Scholar
Pickands, J. III (1975). Statistical inference using extreme order statistics. Annals of Statistics, 3, 119–131.Google Scholar
Pitacco, E. (2004). Survival models in a dynamic context: A survey. Insurance: Mathematics and Economics, 35, 279–298.Google Scholar
Pitselis, G. (2004). A seemingly unrelated regression model in a credibility framework. Insurance: Mathematics and Economics, 34, 37–54.Google Scholar
Pitselis, G. (2008). Robustregression credibility. The Influence Function Approach. Insurance: Mathematics and Economics, 42, 288–300.Google Scholar
Pliska, S. (1997). Introduction to Mathematical Finance. Discrete Time Models. Oxford: Blackwell.Google Scholar
Pollard, J. (2004). Decrement analysis. In Teugels, J. and Sundt, B. (eds), Encyclopedia of Actuarial Science, Chichester: John Wiley & Sons; pp. 436–445.Google Scholar
Press, W. H., Teukolsky, S. A., Vetterling, W. T. and Hannery, B. P. (2007). Numerical Recipes in c, 3rd edn. Cambridge: Cambridge University Press.Google Scholar
Priestley, M.B. (1981). Spectral Analysis and Time Series. San Diego, CA: Academic Press.Google Scholar
Promislow, S. D. (2006). Fundamentals of Actuarial Mathematics. Chichester: John Wiley & Sons.Google Scholar
Purcaru, O. and Denuit, M. (2003). Dependence in dynamic claim frequency credibility models. Astin Bulletin, 33, 23–40.CrossRefGoogle Scholar
Rachev, S.T. (ed.) (2003). Handbook of Heavy Tailed Distributions in Finance. Amsterdam: Elsevier.
Rachev, S. T. and Mittnik, S. (2000). Stable Paretian Models in Finance, Chichester: John Wiley & Sons.Google Scholar
Rachev, S.T., Hsu, J.S.J., Bagasheva, B.S. and Fabozzi, F.J. (2008). Bayesian Methods in Finance. Hoboken, NJ: John Wiley & Sons.Google Scholar
Rebonato, R. (2004). Volatility and Correlation. The Perfect Hedger and the Fox, 2nd edn. Chichester: John Wiley & Sons.CrossRefGoogle Scholar
Reddington, F. M. (1952). Review of the principles of life of fice valuations. Journal of the Institute of Actuaries, 78, 286–340.Google Scholar
Rempala, G. A. and Szatzschneider, K. (2004). Bootstrapping parametric models of mortality. Scandinavian Actuarial Journal, 53–78.Google Scholar
Renshaw, A.E. and Haberman, S. (2003a). Leearter mortality forecasting with age-specific enhancement. Insurance: Mathematics and Economics, 33, 255–272.Google Scholar
Renshaw, A.E. and Haberman, S. (2003b). Lee-Carter mortality forecasting: A parallel generalized linear modelling approach for england and wales mortality projections. Journal of the Royal Statistical Society, c, 52, 119–137.Google Scholar
Resnick, S. I. (2006). Heavy-Tail Phenomena. Probabilistic and Statistical Modeling. New York: Springer-Verlag.Google Scholar
Ripley, C. (2006). Stochastic Simulation, 2nd edn. Hoboken, NJ: John Wiley & Sons.Google Scholar
Robert, C. R. and Casella, G. (2004). Monte Carlo Statistical Methods, 2nd edn. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Robinson, R M. (ed.) (2003). Time Series with Long Memory. Oxford: Oxford University Press.
Rockafeller, R. T. and Uryasev, S. (2000). Optimization of conditional value atrisk. Journal of Risk, 2, 21–41.Google Scholar
Rolski, T., Schmidli, H., Schmidt, V. and Teugels, J. (1999). Stochastic Processes for Insurance and Finance. Chichester: John Wiley & Sons.CrossRefGoogle Scholar
Roman, S. (2004). Introduction to the Mathematics of Finance. New York: Springer-Verlag.CrossRefGoogle Scholar
Rose, C. and Smith, M. D. (2001). Mathematical Statistics with Mathematica. New York: Springer-Verlag.Google Scholar
Ross, S. M. (2002). Simulation, 3rd edn. New York: Academic Press.Google Scholar
Rubinstein, R.Y. and Melamed, B. (1998). Modern Simulation and Modelling. New York: John Wiley & Sons.Google Scholar
Rudolf, M. and Ziemba, W. T. (2004). Intertemporal surplus management. Journal of Economic Dynamics and Control, 28, 975–990.CrossRefGoogle Scholar
Ruppert, D. (2004). Statistics and Finance: An Introduction. New York: Springer-Verlag.CrossRefGoogle Scholar
Salleh, S., Zomaya, A. Y. and Bakar, S. A. (2008). Computing for Numerical Methods Using Visual c++. Hoboken, NJ: John Wiley & Sons.Google Scholar
Savitch, W. (1995). Pascal: An Introduction to the Art and Science of Programming. Redwood City, CA: Benjamin Cummings.Google Scholar
Sbaraglia, S., Papi, M., Briani, M., Bernaschi, M. and Gozzi, F. (2003). A model for optimal asset-liability management for insurance companies. International Journal of Theoretical and Applied Finance, 6, 277–299.CrossRefGoogle Scholar
Sc-Häl, M. (2004). On discrete-time dynamic programming in Insurance: exponential utility and minimising the ruin probability. Scandinavian Actuarial Journal, 1, 189–210.Google Scholar
Schmidly, H. (2006). Optimisation in non-life insurance. Stochastic Models, 22, 689–722.Google Scholar
Schneider, D. I. (2006). An Introduction to Programming Using Visual Basic 2005. Upper Saddle River, NJ: Pearsonentice Hall.Google Scholar
Schnieper, R. (2004). Robust Bayesian experience rating. Astin Bulletin, 34, 125–150.CrossRefGoogle Scholar
Schoutens, w. (2003). Lévy Processes in Finance: Pricing Financial Derivatives. Chichester: John Wiley & Sons.CrossRefGoogle Scholar
Schrager, D.F. (2006). Affine stochastic mortality. Insurance: Mathematics and Economics, 38, 81–97.Google Scholar
Schumway, R. H. and Stof fer, R. S. (2006). Time Series Analysis and its Applications with R Examples. New York: Springer-Verlag.Google Scholar
Scott, R. W. (1992). Multivariate Density Estimation: Theory, Practice and Visualization, 2nd edn. New York: John Wiley & Sons.CrossRefGoogle Scholar
Seydel, R. (2009). Tools for Computational Finance, 2nd edn. Berlin: Springer-Verlag.Google Scholar
Shaw, W. (1998). Modelling Financial Derivatives with Mathematica. Cambridge: Cambridge University Press.Google Scholar
Shephard, N. (1994). Local scale models: State space alternative to integrated GARCH processes. Journal of Econometrics, 60, 181–202.CrossRefGoogle Scholar
Shephard, N. (ed), (2005). Stochastic Volatility. Selected Readings. Oxford: Oxford University Press.
Shreve, S.E. (2004a). Stochastic Calculus for Finance I. The Binomial Asset Pricing Model. New York: Springer-Verlag.CrossRefGoogle Scholar
Shreve, S. E. (2004b). Stochastic Calculus for Finance II. Continuous Time Models. New York: Springer-Verlag.Google Scholar
Sklar, A. (1959). Fonctions de répartition à n dimensions et leur marges. Publications de l'Institut de Statistique de l'Université de Paris, 8, 229–231.Google Scholar
Sokal, R. S. and Rohlf, F. J., (1981). Biometry, 2nd edn. New York: W.H. Freeman.Google Scholar
Stoustrup, B. (2013). The c++ Programming Language, 4th edn. Reading, MA: Addison-Wesley.Google Scholar
Straub, E. (1997). Non-life Insurance Mathematics. Berlin: Springer-Verlag.Google Scholar
Stuart, A. (1962). Gamma-distributed products of independent random variables. Biometrika, 49, 564–565.CrossRefGoogle Scholar
Stuart, A. and Ord, K. (1987). Kendall's Advanced Theory of Statistics. Volume 1. Distribution Theory, 5th edn. London: Edward Arnold.Google Scholar
Sundt, B. (1979). An insurance model with collective seasonal random factors. Mitteilungen der Vereinigung Schweizericher Versicherungsmatemathematiker, 79, 57–64.Google Scholar
Sundt, B. (1983). Parameter estimation in some credibility models. Scandinavian Actuarial Journal, 239–255.Google Scholar
Sundt, B. (1999). An Introduction to Non-Life Insurance Mathematics. Karlsruhe: Verlag-Versicherungswirtschaft.Google Scholar
Sundt, B. and Vernic, R. (2009). Recursions for Convolutions and Compound Distributions with Insurance Applications. Berlin: Springer-Verlag.Google Scholar
Szabo, F. E. (2004). Actuaries' Survival Guide. How to Succeed in One of the Most Desirable Prof essions. San Diego, CA: Elsevier Academic Press.Google Scholar
Taksar, M. and Hunderup, C. L. (2007). The influence of bankruptcy value on optimal risk control for diffusion models with proportional reinsurance. Insurance: Mathematics and Economics, 40, 311–321.Google Scholar
Tapiero, C. (2004). Risk and Financial Management. Mathematical and Computational Methods. Chichester: John Wiley & Sons.Google Scholar
Taylor, G. (2000). Loss Reserving: An Actuarial Perspective. Boston, MA: Kluwer.CrossRefGoogle Scholar
Taylor, G. and McGuire, G. (2007). A synchronous bootstrap to account for dependencies between lines of business in the estimation of loss reserve prediction error. North American Actuarial Journal, 3, 70–88.Google Scholar
Taylor, S. (1986). Modelling Financial Time Series. Chichester: John Wiley & Sons.Google Scholar
Teugels, J. and Sundt, B. (eds) (2004). Encyclopedia of Actuarial Science. Chichester: John Wiley & Sons.CrossRef
Tsay, R. (2010). Analysis of Financial Time series, 3rd edn. Hoboken, NJ: John Wiley & Sons.CrossRefGoogle Scholar
Tuljapurkar, s., Li, N. and Boe, c. (2000). A universal pattern of decline in the G7 countries. Nature, 405, 789–792.CrossRefGoogle ScholarPubMed
Valdez, E., Piggott, J. and Wang, L. (2006). Demand and adverse selection in a pooled annuity fund. Insurance: Mathematics and Economics, 39, 251–266.Google Scholar
Van der Hoeck, J. and Elliot, R. J. (2006). Binomial Models in Finance. New York: Springer-Verlag.CrossRefGoogle Scholar
Vasicek, O. (1977). An equilibrium characterization of the term structure. Journal of Financial Economics, 5, 177–188.CrossRefGoogle Scholar
Venables, w. N. and Ripley, B. (2002). Modern Applied Statistics with S-plus, 4th edn. New York: Springer-Verlag.CrossRefGoogle Scholar
Venables, W. N. and Smith, D. M. (2010). An Introduction to R. Version 2.11.1, at http://www.r-project.org.Google Scholar
Venter, c. G. (2003). Quantifying correlated reinsurance exposures with copulas. In Casualty Actuarial Society Forum, Spring, 215–229.Google Scholar
Vose, D. (2008). Risk Analysis. A Quantitative Guide, 3rd edn. Chichester: John Wiley & Sons.Google Scholar
Wand, M.P. and Jones, M.C. (1995). Kernel Smoothing. Boca Raton, FL: Chapman & Hall/CRC.CrossRefGoogle Scholar
Wang, P. (2003). Financial Econometrics: Methods and Models. London: Rout-ledge.Google Scholar
Wang, D. and Lu, P. (2005). Modelling and forecasting mortality distributions in england and wales using the Leearter model. Journal of Applied Statistics, 32, 873–885.CrossRefGoogle Scholar
Wasserman, L. (2006). All of Nonparametric Statistics. New York: Springer-Verlag.Google Scholar
West, M. and Harrison, J. (1997). Bayesian Forecasting and Dynamic Models, 2nd edn. New York: Springer-Verlag.Google Scholar
Whelan, N. (2004). Sampling from Archimedean copulas. Quantitative Finance, 4, 339–352.CrossRefGoogle Scholar
White, H. (1982). Maximum likelihood estimation of misspecified models. Econometrica, 50, 1–25.CrossRefGoogle Scholar
Wilkie, A. D. (1995). More on a stochastic asset model for actuarial use (with discussion). British Actuarial Journal, 1, 777–964.CrossRefGoogle Scholar
Williams, C.A., Smith, M.L. and Young, P.C. (1998). Risk Management and Insurance, 8th edn. Boston MA: Irwin/McGraw-Hill.Google Scholar
Williams, R. J. (2006). Introduction to the Mathematics of Finance. Providence, RI: American Mathematical Society.CrossRefGoogle Scholar
Wilmoth, J. R., Andreev, K., Jdanov, D. and Glei, D. A. (2007). Methods Protocol forthe Human Mortality Database. Available at http://www.mortality.org.Google Scholar
Wilmott, P., Howison, S. and Dewynne, J. (1995). The Mathematics of Financial Derivatives. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Wolfram, s. (2003). The Mathematica Book, 5th edn. Champaign, IL: Wolfram Media.Google Scholar
Wood, s. N. (2006). Generalized Additive Models: An Introduction with R. Boca Raton, FL: Chapman & Hall.Google Scholar
Wiitrich, M. V. (2004). Extreme value theory and archimedean copulas. Scandinavian Actuarial Journal, 211–228.Google Scholar
Wiitrich, M. V. and Merz, M. (2008). Stochastic Claim Reserving Methods in Insurance. Chichester: John Wiley & Sons.Google Scholar
Wiitrich, M. V., Biihlmann, H. and Furrer, H. (2010). Market-Consistent Actuarial Valuation, 2nd edn. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Yau, K., Yip, K. and Yuen, H.K. (2003). Modelling repeated insurance claim frequency data using the generalized linear mixed model. Journal of Applied Statistics, 30, 857–865.CrossRefGoogle Scholar
Yeo, K. L. and Valdez, E. A. (2006). Claim dependence with common effects in credibility models. Insurance: Mathematics and Economics, 38, 609–623.Google Scholar
Young, V. R. (2004). Premium principles. In Teugels, J., and Sundt, B. (eds), Encyclopedia of Actuarial Science, Chichester: John Wiley & Sons; pp. 1322–1331.Google Scholar
Zhang, L., Mykland, P. A. and Ai't-Sahalia, Y. (2005). A tale of two time scales: Determining integrated volatility with noisy high-frequency data. Journal of American Statistical Association, 100, 1394–1411.CrossRefGoogle Scholar
Zhang, x., Zhou, M. and Guo, J. (2007). Optimal combination of quota-share and excess-of-loss reinurance policies in a dynamic setting: Research articles. Applied Stochastic Models in Business and Industry, 23, 63–71.CrossRefGoogle Scholar
Zivot, E. and Wang, J. (2003). Modelling Financial Time Series with S-plus. New York: Springer-Verlag.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • References
  • Erik Bølviken, Universitetet i Oslo
  • Book: Computation and Modelling in Insurance and Finance
  • Online publication: 05 May 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139020251.020
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • References
  • Erik Bølviken, Universitetet i Oslo
  • Book: Computation and Modelling in Insurance and Finance
  • Online publication: 05 May 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139020251.020
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • References
  • Erik Bølviken, Universitetet i Oslo
  • Book: Computation and Modelling in Insurance and Finance
  • Online publication: 05 May 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139020251.020
Available formats
×