Biography: Prof. Stefano Zedda is associate professor in banking at the University of Cagliari. His research is mainly focused on quantitative analyses for banking and finance, and in particular on banking systems modeling and simulation. In 2008 he started and developed the mathematical modeling and software implementation of the Systemic Model for Banking Originated Losses (SYMBOL), further developed during his activity at the European Commission (2010-2012), that subsequently adopted it as a standard tool for testing banking regulation. His studies were published in international leading journals such as The Journal of Banking and Finance, Journal of Financial Services Research, Computational Economics, Sustainability, in a book on Banking Systems Simulation published by Wiley, and presented in many international conferences in Europe, Asia, America and Australia. He also presented his studies and analyses as invited speaker, among the others, at the European Commission, and at the Treasury Department of the USA.
Speech Title: Modeling and Simulation of Banking Systems
Abstract: The Global financial crisis which started in 2008 has shown the need for models and methods to assess the risk of bank defaults, of financial contagion effects, and to prevent new financial crises. While the lack of data limits the use of the traditional econometric approach, simulation models can deal with this problem, by representing the mechanisms trough which contagion starts and propagates, and simulating the possible resulting scenarios in terms of losses and probability to occur. In this lecture, I’ll present the main issues, how each aspect can be represented in a formal way, and how these models can be implemented in a simulation tool. I'll also describe how to use it for what-if analyses, to assess the effects of variations in minimum capital requirements, for Deposits Guarantee Schemes dimensioning, to quantify the possible effects of financial crises on public finances stability, and more.
Biography: Prof. Dariusz Jacek Jakóbczak was born in Koszalin, Poland, on December 30, 1965. He graduated in mathematics (numerical methods and programming) from the University of Gdansk, Poland in 1990. He received the Ph.D. degree in 2007 in computer science from the Polish – Japanese Institute of Information Technology, Warsaw, Poland. From 1991 to 1994 he was a civilian programmer in the High Military School in Koszalin. He was a teacher of mathematics and computer science in the Private Economic School in Koszalin from 1995 to 1999. Since March 1998 he has worked in the Department of Electronics and Computer Science, Koszalin University of Technology, Poland and since October 2007 he has been an Assistant Professor in the Chair of Computer Science and Management in this department. His research interests connect mathematics with computer science and include computer vision, artificial intelligence, shape representation, curve interpolation, contour reconstruction and geometric modeling, numerical methods, probabilistic methods, game theory, operational research and discrete mathematics.
Speech Title: Interpolation and Extrapolation of Curves and Functions
Abstract: Artificial Intelligence is applied for prediction and calculations of unknown values of data or coordinates. Decision makers, academicians, researchers, advanced-level students, technology developers, and government officials will find this text useful in furthering their research exposure to pertinent topics in AI, computer science, numerical analysis or operations research and assisting in furthering their own research efforts in these fields. Proposed method, called Two-Points Smooth Interpolation (TPSI), is the method of 2D curve interpolation and extrapolation using the set of key points (knots or nodes). Nodes can be treated as characteristic points of data for modeling and analyzing. The model of data can be built by choice of probability distribution function and nodes combination. TPSI modeling via nodes combination and parameter γ as probability distribution function enables value anticipation in AI, risk analysis and decision making. Two-dimensional curve is extrapolated and interpolated via nodes combination and different functions as continuous probability distribution functions: polynomial, sine, cosine, tangent, cotangent, logarithm, exponent, arc sin, arc cos, arc tan, arc cot or power function.