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Download Mathematical and Statistical Methods for Actuarial Sciences by Marco Corazza, Claudio Pizzi (eds.) PDF

By Marco Corazza, Claudio Pizzi (eds.)

The interplay among mathematicians and statisticians has been proven to be an effective process for facing actuarial, assurance and financial difficulties, either from an instructional viewpoint and from an operative one. the gathering of unique papers offered during this quantity pursues accurately this objective. It covers a large choice of topics in actuarial, coverage and finance fields, all handled within the mild of the winning cooperation among the above quantitative ways.

The papers released during this quantity current theoretical and methodological contributions and their functions to actual contexts. With recognize to the theoretical and methodological contributions, the various thought of parts of research are: actuarial versions; substitute checking out methods; behavioral finance; clustering thoughts; coherent and non-coherent chance measures; credits scoring methods; information envelopment research; dynamic stochastic programming; financial contagion types; financial ratios; clever financial buying and selling platforms; mix normality methods; Monte Carlo-based tools; multicriteria tools; nonlinear parameter estimation recommendations; nonlinear threshold types; particle swarm optimization; functionality measures; portfolio optimization; pricing tools for dependent and non-structured derivatives; probability administration; skewed distribution research; solvency research; stochastic actuarial valuation tools; variable choice types; time sequence research instruments. As regards the purposes, they're with regards to genuine difficulties linked, one of the others, to: banks; collateralized fund tasks; credits portfolios; defined benefit pension plans; double-indexed pension annuities; efficient-market speculation; trade markets; financial time sequence; firms; hedge money; non-life insurance firms; returns distributions; socially accountable mutual cash; unit-linked contracts.

This ebook is aimed toward lecturers, Ph.D. scholars, practitioners, pros and researchers. however it may also be of curiosity to readers with a few quantitative historical past knowledge.

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Extra resources for Mathematical and Statistical Methods for Actuarial Sciences and Finance

Example text

7(2), 129–168 (2004) 16. : American option pricing using simulation and regression: numerical convergence results. H. ) Topics in Numerical Methods for Finance. Springer Proceedings in Mathematics & Statistics 19, pp. 57–94 (2012) 17. : Option pricing: mathematical models and computation. Oxford Financial Press (1993) Dynamic Tracking Error with Shortfall Control Using Stochastic Programming Diana Barro and Elio Canestrelli Abstract In this contribution we tackle the issue of portfolio management combining benchmarking and risk control.

Ann. of Stat. 30, 74–99 (2002) 21. : Selective overview of variable selection in high dimensional feature space. Stat. Sinica 20, 101–148 (2010) 22. : Bayesian variable selection method for censored survival data. Biom. 54(4), 1475–1485 (1998) 23. : A Statistical View of Some Chemometrics Regression Tools. Technometrics 35, 109–148 (1993) 24. : Regularization Paths for Generalized Linear Models via Coordinate Descent. J. of Stat. Softw. 33(1), 1–22 (2010) 25. : Analysis of Multivariate Survival Data, Statistics for Biology and Health.

In particular, the introduction of a shortfall control is effective but is done at the cost of a reduction in the gains when the market rises. Moreover, if we compare portfolios 2a and 1b, it is interesting to observe that when we allow for a higher tracking error we can improve the downside protection. From the bottom graph, which displays the behavior of non- 50 D. Barro and E. Canestrelli tracking portfolios, we can observe that there is an improvement in the downside protection but at a higher cost in terms of upside capture.

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