Research Seminars

Stochastic control under partial observation

Speaker(s): 
Huyen Pham (Université Paris Diderot)
Date: 
Thursday, June 15, 2017 - 4:00pm
Location: 
TU Berlin, Straße des 17. Juni 136, 10623 Berlin, Raum MA 043

We study and revisit the optimal control problem of partially observed stochastic systems. By using a control randomization method, we provide a backward stochastic differential equation (BSDE) representation for the value function in a general framework including path-dependence in the coefficients (both on the state and control) and without any non degeneracy condition on the diffusion coefficient.

Kantorovich distance based kernel for Gaussian Processes: estimation and forecast

Speaker(s): 
Jean-Michel Loubes (University Toulouse)
Date: 
Wednesday, June 14, 2017 - 10:00am
Location: 
Hausvogteiplatz 11a, 10117 Berlin, Room 4.13 (4th floor)

Monge-Kantorovich distances, otherwise known as Wasserstein distances, have received a growing attention in statistics and machine learning as a powerful discrepancy measure for probability distributions. Here, we focus on forecasting a Gaussian process indexed by probability distributions. For this, we provide a family of positive definite kernels built using transportation based distances. We provide a probabilistic understanding of these kernels and characterize the corresponding stochastic processes.

Unobserved Heterogeneity and Empirical Bayes Methods

Speaker(s): 
Roger Koenker (Illinois)
Date: 
Wednesday, June 7, 2017 - 10:00am
Location: 
HU Berlin, Heilig-Geist-Kapelle, Spandauerstr. 1, 10178 Berlin

Unobserved heterogeneity is a pervasive feature of modern econometric problems. Recent advances in convex optimization make it possible to efficiently estimate the nonparametric mixture models that underlie such applications and empirical Bayes methods provide a unified decision theoretic framework for interpreting them. This approach will be illustrated with applications to longitudinal models of income dynamics, fraility models in survival analysis and multiple testing.

Model-free bounds for multi-asset options -- improved Fréchet-Hoeffding and optimal transport approaches

Speaker(s): 
Antonis Papapantoleon (TU Berlin)
Date: 
Thursday, June 1, 2017 - 5:00pm
Location: 
TU Berlin, Straße des 17. Juni 136, 10623 Berlin, Raum MA 043

We consider a multivariate random variable with known marginals and unknown dependence structure. In this talk, we will present several methods for sharpening the classical Fréchet-Hoeffding bounds on copulas by using additional, partial information on the dependence structure. Then we will discuss applications of these results for deriving bounds on option prices and portfolio Value-at-Risk in this setting of model / dependence uncertainty. We will also discuss the detection of arbitrage in multi-asset markets and model-free hedging of multi-asset derivatives.

Singular Copulas

Speaker(s): 
Fabrizio Durante (Università del Salento)
Date: 
Thursday, June 1, 2017 - 4:00pm
Location: 
TU Berlin, Straße des 17. Juni 136, 10623 Berlin, Raum MA 043

We present both old and recent results about singular copulas and copulas with a singular component by discussing their relevance in (at least) three different domains. First, singular copulas may be used to obtain specific tail behavior in a multivariate distribution, a fact that has also been exploited to obtain worst-possible scenarios for risk measures. Second, special classes of singular copulas (e.g. shuffles of Min) can be used in the approximation of various dependence structures.

Quantile-Regression Inference With Adaptive Control of Size

Speaker(s): 
Juan Carlos Escanciano (Indiana University Bloomington)
Date: 
Wednesday, May 31, 2017 - 10:00am
Location: 
WIAS, Erhard-Schmidt-Saal, Mohrenstraße 39, 10117 Berlin

Regression quantiles have asymptotic variances that depend on the conditional densities of the response variable given regressors. This talk develops a new estimate of the asymptotic variance of regression quantiles that leads any resulting Wald-type test or confidence region to behave as well in large samples as its infeasible counterpart in which the true conditional response densities are embedded. We give explicit guidance on implementing the new variance estimator to control adaptively the size of any resulting Wald-type test.

Frequency domain likelihood approximations for time series bootstrapping and bayesian nonparametrics

Speaker(s): 
Claudia Kirch (Universität Magdeburg)
Date: 
Wednesday, May 24, 2017 - 10:00am
Location: 
WIAS, Erhard-Schmidt-Saal, Mohrenstraße 39, 10117 Berlin

A large class of time series methods are based on a Fourier analysis, which can be considered as a whitening of the data, giving rise for example to the famous Whittle likelihood. In particular, frequency domain bootstrap methods have been successfully applied in a large range of situations. In this talk, we will first review existing frequency domain bootstrap methodology for stationary time series before generalizing them for locally stationary time series.

Numerical Methods for SDEs in Mathematical Finance

Speaker(s): 
Michaela Szoelgyenyi (Vienna University of Economics and Business)
Date: 
Thursday, May 18, 2017 - 5:00pm
Location: 
TU Berlin, Straße des 17. Juni 136, 10623 Berlin, Raum MA 043

When solving certain stochastic control problems in insurance mathematics or mathematical finance, the optimal control policy sometimes turns out to be of threshold type, meaning that the control depends on the controlled process in a discontinuous way. The stochastic differential equations (SDEs) modeling the underlying process then typically have a discontinuous drift coefficient. This motivates the study of a more general class of such SDEs.

Optimal targeting position and (forward) backward stochastic differential equation

Speaker(s): 
Alexandré Popier (Université Lemans, France)
Date: 
Thursday, May 18, 2017 - 4:00pm
Location: 
TU Berlin, Straße des 17. Juni 136, 10623 Berlin, Raum MA 043

In this talk we present an optimal stochastic control problem related to portfolio liquidation problems. For the homogeneous case, we give a complete solution using backward stochastic differential equation with singular terminal condition (joint work with T. Kruse (Essen, Germany)). In the Brownian setting, we explain how it can be (partially) solved using forward backward SDE together with the decoupling field method (work in progress with S. Ankirchner, A. Fromm (Jena, Germany) and T. Kruse (Essen, Germany)).

Vast network analysis of Limit Order Books

Speaker(s): 
Shi Chen (HU Berlin)
Date: 
Wednesday, May 17, 2017 - 10:00am
Location: 
WIAS, Erhard-Schmidt-Saal, Mohrenstraße 39, 10117 Berlin

We propose vast network estimators for limit order books in high dimensional setting, and we argue that limit orders have significant market impacts. Both undirected and directed network estimators are constructed based on consistent estimator for covariance matrix. Furthermore, the undirected estimator is constructed with sparse concentration matrix using graphical lasso, so that the regularized covariance matrix is related to connectedness measure. The directed one is derived from VAR model through penalized variance decomposition.

Pages

Subscribe to Research Seminars