The Case of a Transitional Economy - Poland

Background information: The international conference „SustEcon Conference – The contribution of a sustainable economy to achieving the Sustainable Development Goals” took place on 25 and 26 September 2017 at the Freie Universität in Berlin, Germany (organised by the NaWiKo project). The focus of the conference was on the contributions of the sustainable economy to achieving the Sustainable Development Goals (SDGs). This contribution can be observed on a number of different levels: Innovations toward achieving the SDGs are to be as much a topic at the conference as methodological questions about measuring sustainability. In addition to that, the differences between various discourses and concepts and their respective contributions to the sustainable economy were also featured prominently in the conference. A further topic of interest was the (political) framework conditions and barriers to a sustainable economy as well as the contribution of science to the SDGs

A neural network-based framework for financial model calibration

A data-driven approach called CaNN (Calibration Neural Network) is proposed to calibrate financial asset price models using an Artificial Neural Network (ANN). Determining optimal values of the model parameters is formulated as training hidden neurons within a machine learning framework, based on available financial option prices. The framework consists of two parts: a forward pass in which we train the weights of the ANN off-line, valuing options under many different asset model parameter settings; and a backward pass, in which we evaluate the trained ANN-solver on-line, aiming to find the weights of the neurons in the input layer. The rapid on-line learning of implied volatility by ANNs, in combination with the use of an adapted parallel global optimization method, tackles the computation bottleneck and provides a fast and reliable technique for calibrating model parameters while avoiding, as much as possible, getting stuck in local minima. Numerical experiments confirm that this machine-learning framework can be employed to calibrate parameters of high-dimensional stochastic volatility models efficiently and accurately.Comment: 34 pages, 9 figures, 11 table

Wymiary dyskursu ekologicznego – przegląd problemów i wybranej literatury

In the last few decades the ecological (environmental or sustainability) discourse has been advanced and intensified. This discourse takes its place predominantly in highly developed countries and also in forums of international organizations and institutions (e.g. UN, Greenpeace, alter-globalist movements). There is more and more dimensions of research and discussions. New ideas, concepts, theoretical approaches, methodologies, also practical innovations and procedures are emerging. The discourse is to a growing extent multi- and interdisciplinary, not excluding however other perspectives like e.g. technological, economic, political, managerial, ethical. Important areas of studies and debates are such as government policies, business strategies, behavior of citizens, not to mention the global dimension of the problematique.W ostatnich dekadach rozwija się i nasila dyskurs ekologiczny (zwany też środowiskowym i zrównoważnościowym). Ma to miejsce przede wszystkim w krajach wysoko rozwiniętych oraz na forach organizacji i instytucji międzynarodowych (jak np. ONZ, Greenpeace, altergobaliści). Dyskurs ten nabiera coraz więcej wymiarów. Powstaje coraz więcej nowych idei, koncepcji, ujęć teoretycznych, metodologii, a także praktycznych innowacji i procedur. Cechą tego dyskursu jest multi- i interdyscyplinarność, co nie wyklucza ujęć z perspektywy technologii, ekonomii, polityki, zarządzania, etyki. Ważnym obszarem badań i debat jest polityka rządów, strategie biznesu, z zachowania obywateli, nie mówiąc o wymiarze globalnym problemów ekologicznych

Locally continuously perfect groups of homeomorphisms

The notion of a locally continuously perfect group is introduced and studied. This notion generalizes locally smoothly perfect groups introduced by Haller and Teichmann. Next, we prove that the path connected identity component of the group of all homeomorphisms of a manifold is locally continuously perfect. The case of equivariant homeomorphism group and other examples are also considered.Comment: 14 page

GPU acceleration of the Seven-League Scheme for large time step simulations of stochastic differential equations

Monte Carlo simulation is widely used to numerically solve stochastic differential equations. Although the method is flexible and easy to implement, it may be slow to converge. Moreover, an inaccurate solution will result when using large time steps. The Seven League scheme, a deep learning-based numerical method, has been proposed to address these issues. This paper generalizes the scheme regarding parallel computing, particularly on Graphics Processing Units (GPUs), improving the computational speed

Monte Carlo simulation of SDEs using GANs

Generative adversarial networks (GANs) have shown promising results when applied on partial differential equations and financial time series generation. We investigate if GANs can also be used to approximate one-dimensional Ito ^ stochastic differential equations (SDEs). We propose a scheme that approximates the path-wise conditional distribution of SDEs for large time steps. Standard GANs are only able to approximate processes in distribution, yielding a weak approximation to the SDE. A conditional GAN architecture is proposed that enables strong approximation. We inform the discriminator of this GAN with the map between the prior input to the generator and the corresponding output samples, i.e. we introduce a ‘supervised GAN’. We compare the input-output map obtained with the standard GAN and supervised GAN and show experimentally that the standard GAN may fail to provide a path-wise approximation. The GAN is trained on a dataset obtained with exact simulation. The architecture was tested on geometric Brownian motion (GBM) and the Cox–Ingersoll–Ross (CIR) process. The supervised GAN outperformed the Euler and Milstein schemes in strong error on a discretisation with large time steps. It also outperformed the standard conditional GAN when approximating the conditional distribution. We also demonstrate how standard GANs may give rise to non-parsimonious input-output maps that are sensitive to perturbations, which motivates the need for constraints and regularisation on GAN generators