UID:
almahu_9949709192902882
Format:
XVII, 137 p. 18 illus., 17 illus. in color. Textbook for German language market.
,
online resource.
Edition:
1st ed. 2024.
ISBN:
9783658438531
Series Statement:
Mathematische Optimierung und Wirtschaftsmathematik / Mathematical Optimization and Economathematics,
Content:
Maximilian Klein analyses nested Monte Carlo simulations for the approximation of conditional expected values. Thereby, the book deals with two general risk functional classes for conditional expected values, on the one hand the class of moment-based estimators (notable examples are the probability of a large loss or the lower partial moments) and on the other hand the class of quantile-based estimators. For both functional classes, the almost sure convergence of the respective estimator is proven and the underlying convergence speed is quantified. In particular, the class of quantile-based estimators has important practical consequences especially for life insurance companies since the Value-at-Risk falls into this class and thus covers the solvency capital requirement problem. Furthermore, a novel non parametric confidence interval method for quantiles is presented which takes the additional noise of the inner simulation into account. About the author Maximilian Klein holds a PhD in mathematics from the University of Augsburg. Currently, he works as a portfolio manager at an asset management company.
Note:
Introduction -- Basic Concepts, Probability Inequalities and Limit Theorems -- Almost Sure Convergence of Moment-Based Estimators -- Almost Sure Convergence of Quantile-Based Estimators -- Non Parametric Confidence Intervals for Quantiles -- Numerical Analysis -- Conclusion.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783658438524
Additional Edition:
Printed edition: ISBN 9783658438548
Language:
English
Subjects:
Mathematics
Keywords:
Hochschulschrift
;
Hochschulschrift
;
Hochschulschrift
;
Hochschulschrift
DOI:
10.1007/978-3-658-43853-1
URL:
https://doi.org/10.1007/978-3-658-43853-1
URL:
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