Valuation and Risk Management of Guaranteed Minimum Death Benefits (GMDB) by Randomization
Freitag, 13.1.23, 12:00-13:00, Hörsaal II, Albertstr. 23b
Randomization is a technique in Finance to replace known quantities (like the time to maturity) by random variables. This sometimes gives moments or quantiles of the payoff in closed-form, avoiding any kind of integration, Fourier inversion or simulation algorithm. We apply this idea to insurance and Guaranteed Minimum Death Benefits (GMDB) where payoff dates are per se random. The remaining lifetime is expanded in terms of a Laguerre series while the financial market follows a regime switching model with two-sided phase-type jumps. For European-type GMDBs, we obtain the density of the payoff in closed form as a Laurent series. Payoff distributions of contracts with path-dependent guarantee features can be expressed in terms of solutions of Sylvester equations (=matrix equations of the form AX + XB =C).\n\nThis is joint work with Griselda Deelstra (Université Libre de Bruxelles).\n\nA paper version is available here: Deelstra, Griselda and Hieber, Peter, Randomization and the Valuation of Guaranteed Minimum Death Benefits, https://ssrn.com/abstract=4115505.
A phase field model for soma-germline interactions in Drosophila oogenesis
Freitag, 20.1.23, 12:00-13:00, online: Zoom
In [1], we study the signals mediating the mechanical interaction between somatic epithelial cells and the germline of Drosophila. We discover that, during the development of the egg chamber, the transcriptational regulator “Eyes absent” (Eya) modulates the affinity of the apical surface of epithelial cells to the nurse cells and the oocyte in the egg chamber. Using a phase field model, we develop a quantitative, mechanical description of epithelial cell behavior and demonstrate that the spatio-temporal expression of Eya controls the epithelial cells’ shape and movement during all phases of Drosophila oogenesis to establish a suitable match between epithelial cells and germline cells. Further we show that differential expression of Eya in follicle cells also controls oocyte growth via cell-cell affinity.\n\n \n\n[1] V. Weichselberger, P. Dondl, A.-K. Classen (2022): Eya-controlled affinity between cell lineages drives tissue self-organization during Drosophila oogenesis. Nat Commun 13(1):6377. DOI: 10.1038/s41467-022-33845-1
Generalized Covariance Estimator
Freitag, 27.1.23, 12:00-13:00, Hörsaal II, Albertstr. 23b
We consider a class of semi-parametric dynamic models with iid errors, including the nonlinear mixed causal-noncausal Vector Autoregressive (VAR), Double-Autoregressive (DAR) and stochastic volatility models. To estimate the parameters characterizing the (nonlinear) serial dependence, we introduce a generic Generalized Covariance (GCov) estimator, which minimizes a residual-based multivariate portmanteau statistic. In comparison to the standard methods of moments, the GCov estimator has an interpretable objective function, circumvents the inversion of high-dimensional matrices, and achieves semi-parametric efficiency in one step. We derive the asymptotic properties of the GCov estimator and show its semi-parametric efficiency. We also prove that the associated residual-based portmanteau statistic is asymptotically chi-square distributed. The finite sample performance of the GCov estimator is illustrated in a simulation study. The estimator is then applied to a dynamic model of commodity futures.\nChristian Gourieroux & Joann Jasiak (2022): Generalized Covariance Estimator,Journal of Business & Economic Statistics, DOI:10.1080/07350015.2022.2120486