62P05 Applications to actuarial sciences and financial mathematics
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- Doctoral Thesis (3)
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- Arbitrage (1)
- Archimedische Kopula (1)
- Bewertung (1)
- CDO (1)
- Chi-Quadrat-Test (1)
- Derivat <Wertpapier> (1)
- Elliptische Verteilung (1)
- Erwartungswert-Varianz-Ansatz (1)
- Heavy-tailed Verteilung (1)
- Heston-Modell (1)
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Life insurance companies are asked by the Solvency II regime to retain capital requirements against economically adverse developments. This ensures that they are continuously able to meet their payment obligations towards the policyholders. When relying on an internal model approach, an insurer's solvency capital requirement is defined as the 99.5% value-at-risk of its full loss probability distribution over the coming year. In the introductory part of this thesis, we provide the actuarial modeling tools and risk aggregation methods by which the companies can accomplish the derivations of these forecasts. Since the industry still lacks the computational capacities to fully simulate these distributions, the insurers have to refer to suitable approximation techniques such as the least-squares Monte Carlo (LSMC) method. The key idea of LSMC is to run only a few wisely selected simulations and to process their output further to obtain a risk-dependent proxy function of the loss. We dedicate the first part of this thesis to establishing a theoretical framework of the LSMC method. We start with how LSMC for calculating capital requirements is related to its original use in American option pricing. Then we decompose LSMC into four steps. In the first one, the Monte Carlo simulation setting is defined. The second and third steps serve the calibration and validation of the proxy function, and the fourth step yields the loss distribution forecast by evaluating the proxy model. When guiding through the steps, we address practical challenges and propose an adaptive calibration algorithm. We complete with a slightly disguised real-world application. The second part builds upon the first one by taking up the LSMC framework and diving deeper into its calibration step. After a literature review and a basic recapitulation, various adaptive machine learning approaches relying on least-squares regression and model selection criteria are presented as solutions to the proxy modeling task. The studied approaches range from ordinary and generalized least-squares regression variants over GLM and GAM methods to MARS and kernel regression routines. We justify the combinability of the regression ingredients mathematically and compare their approximation quality in slightly altered real-world experiments. Thereby, we perform sensitivity analyses, discuss numerical stability and run comprehensive out-of-sample tests. The scope of the analyzed regression variants extends to other high-dimensional variable selection applications. Life insurance contracts with early exercise features can be priced by LSMC as well due to their analogies to American options. In the third part of this thesis, equity-linked contracts with American-style surrender options and minimum interest rate guarantees payable upon contract termination are valued. We allow randomness and jumps in the movements of the interest rate, stochastic volatility, stock market and mortality. For the simultaneous valuation of numerous insurance contracts, a hybrid probability measure and an additional regression function are introduced. Furthermore, an efficient seed-related simulation procedure accounting for the forward discretization bias and a validation concept are proposed. An extensive numerical example rounds off the last part.
Diese Doktorarbeit befasst sich mit Volatilitätsarbitrage bei europäischen Kaufoptionen und mit der Modellierung von Collateralized Debt Obligations (CDOs). Zuerst wird anhand einer Idee von Carr gezeigt, dass es stochastische Arbitrage in einem Black-Scholes-ähnlichen Modell geben kann. Danach optimieren wir den Arbitrage- Gewinn mithilfe des Erwartungswert-Varianz-Ansatzes von Markowitz und der Martingaltheorie. Stochastische Arbitrage im stochastischen Volatilitätsmodell von Heston wird auch untersucht. Ferner stellen wir ein Markoff-Modell für CDOs vor. Wir zeigen dann, dass man relativ schnell an die Grenzen dieses Modells stößt: Nach dem Ausfall einer Firma steigen die Ausfallintensitäten der überlebenden Firmen an, und kehren nie wieder zu ihrem Ausgangsniveau zurück. Dieses Verhalten stimmt aber nicht mit Beobachtungen am Markt überein: Nach Turbulenzen auf dem Markt stabilisiert sich der Markt wieder und daher würde man erwarten, dass die Ausfallintensitäten der überlebenden Firmen ebenfalls wieder abflachen. Wir ersetzen daher das Markoff-Modell durch ein Semi-Markoff-Modell, das den Markt viel besser nachbildet.
The question of how to model dependence structures between financial assets was revolutionized since the last decade when the copula concept was introduced in financial research. Even though the concept of splitting marginal behavior and dependence structure (described by a copula) of multidimensional distributions already goes back to Sklar (1955) and Hoeffding (1940), there were very little empirical efforts done to check out the potentials of this approach. The aim of this thesis is to figure out the possibilities of copulas for modelling, estimating and validating purposes. Therefore we extend the class of Archimedean Copulas via a transformation rule to new classes and come up with an explicit suggestion covering the Frank and Gumbel family. We introduce a copula based mapping rule leading to joint independence and as results of this mapping we present an easy method of multidimensional chi²-testing and a new estimate for high dimensional parametric distributions functions. Different ways of estimating the tail dependence coefficient, describing the asymptotic probability of joint extremes, are compared and improved. The limitations of elliptical distributions are carried out and a generalized form of them, preserving their applicability, is developed. We state a method to split a (generalized) elliptical distribution into its radial and angular part. This leads to a positive definite robust estimate of the dispersion matrix (here only given as a theoretical outlook). The impact of our findings is stated by modelling and testing the return distributions of stock- and currency portfolios furthermore of oil related commodities- and LME metal baskets. In addition we show the crash stability of real estate based firms and the existence of nonlinear dependence in between the yield curve.