Değişik sektörlere uygulamalarıyla birlikte sağlam konik optimizasyon ile eğri modelleri ve ağların sağlam tanımlanmasındaki gelişimler (original) (raw)

Eco-Finance Networks under Uncertainty

2008

In this paper we survey recent advances and mathematical foundations of regulation networks. We explain their interdisciplinary implications with special regard to Operational Research and flnancial sciences and introduce the so-called eco-flnance networks. These networks, originally developed in the context of modeling and prediction of gene-expression patterns, have proved to provide a conceptual framework for the modeling of dynamical systems with respect to errors and uncertainty as well as the in∞uence of certain environmental items. Given the noise-prone measurement data we extract nonlinear difierential equations to describe and investigate the interactions and regulating efiects between the data items of interest and the environmental items. In particular, these equations re∞ect data uncertainty by the use of interval arithmetics and comprise unknown parameters resulting in a wide variety of the model. For an identiflcation of these parameters Chebychev approximation and gen...

Risk modelling and management: An overview

Mathematics and Computers in Simulation, 2013

The papers in this special issue of Mathematics and Computers in Simulation are substantially revised versions of the papers that were presented at the 2011 Madrid International Conference on "Risk Modelling and Management" (RMM2011). The papers cover the following topics: currency hedging strategies using dynamic multivariate GARCH, risk management of risk under the Basel Accord: A Bayesian approach to forecasting value-at-risk of VIX futures, fast clustering of GARCH processes via Gaussian mixture models, GFC-robust risk management under the Basel Accord using extreme value methodologies, volatility spillovers from the Chinese stock market to economic neighbours, a detailed comparison of Value-at-Risk estimates, the dynamics of BRICS's country risk ratings and domestic stock markets, U.S. stock market and oil price, forecasting value-at-risk with a duration-based POT method, and extreme market risk and extreme value theory.

MATHEMATICAL MODELS IN THE RISK ASSESSMENT IN THE DECISION PROCESS

Currently, financial audit has a role to play in assessing the efficiency, economy and effectiveness of the organization's control environment, with other recommendations for improving the system and control processes, including anticipating the future of the institution's decision-making system. Risk analyzes during the audit planning phase are directed to management approaches and methods that establish a link with mathematics and computation, which is responsible for substantiating the managerial decision-making process in terms of auditing effectiveness. Audit recommendations in shaping a decision-making process under uncertain conditions determine us to specify its elements, namely: risk, problem formulation and specification of the minimization / maximization objectives of some technical / economic indicators, the set of possible alternatives / alternatives that characterize a decision maker of the situation, set of anticipated consequences for each variant,-independent factors of the decision makers and the conjuncture type. From the set of possible variants calculated by one or more mathematical methods, the auditor should choose only one, that is, the most convenient solution. The evaluation of risk and uncertainty decisions in the process of knowledge and analysis of the current situation is accomplished through a multitude of methods, ways and means to identify, determine the audit planning activity. The method of mathematical models or models whose significance is described and applied in the analysis and evaluation of scientific research which does not constitute a new discovery but which reproduces certain aspects of the studied objectives in order to facilitate the analysis of processes or systems. Today, it is impossible to conceive an audit, an economic discipline, or a financial discipline that does not use quantification, numerical expression, law, interdependence, and measurement of trends in decision-making in its knowledge process. Moreover, the complexity of the interconnection of the activity in the decision making process of several disciplines makes it necessary for the audit of its evaluation to use this modern tool, which guarantees not only the precision of the formulation of the conclusions but also the efficiency of the recommendations on this basis in the concrete economic activity .

Modeling, Measuring and Managing Risk

2007

It is problematic to treat systemic risk as a merely technical problem that can be solved by natural-science methods and through biological and ecological analogies. There appears to be a discrepancy between understanding systemic risk from a natural-science perspective and the unresolved challenges that arise when humans with their initiatives and interactions are included in systemic-risk considerations. It is therefore necessary to investigate possible fundamental differences and similarities of systemic risk with and without accounting for human involvement. Focusing on applied and implementation aspects of measuring, modeling, and managing systemic risks, we identify three important and distinct features characterizing such fundamental differences: indetermination, indecision, and responsibility. We contend that, first, including human initiatives and interactions in systemicrisk considerations must emphasize a type of variability that is especially relevant in this context, namely the role of free will as a fundamental source of essential indetermination in human agency. Second, we postulate that collective indecision generated by mutual uncertainty often leads to the suspension or alteration of rules, procedures, scripts, and norms. Consequently, the associated systemic risks cannot be incorporated into explanatory models, as the new causal rules cannot be predicted and accounted for. Third, analogies from biology and ecology, especially the idea of 'contagion,' downplay human agency, and therefore human responsibility, promoting the false belief that systemic risk is a merely technical problem. For each of these three features, we provide recommendations for future directions and suggest how measuring, modeling, and managing approaches from the natural-science domain can best be applied in light of human agency.

Modeling, inference and optimization of regulatory networks based on time series data

European Journal of Operational Research, 2011

In this survey paper, we present advances achieved during the last years in the development and use of OR, in particular, optimization methods in the new gene-environment and eco-finance networks, based on usually finite data series, with an emphasis on uncertainty in them and in the interactions of the model items. Indeed, our networks represent models in the form of time-continuous and time-discrete dynamics, whose unknown parameters we estimate under constraints on complexity and regularization by various kinds of optimization techniques, ranging from linear, mixed-integer, spline, semi-infinite and robust optimization to conic, e.g., semi-definite programming. We present different kinds of uncertainties and a new time-discretization technique, address aspects of data preprocessing and of stability, related aspects from game theory and financial mathematics, we work out structural frontiers and discuss chances for future research and OR application in our real world.

Risk and Financial Management Article Systemic Risk Indicators Based on Nonlinear PolyModel

2020

The global financial market has become extremely interconnected as it demonstrates strong nonlinear contagion in times of crisis. As a result, it is necessary to measure financial systemic risk in a comprehensive and nonlinear approach. By establishing a large set of risk factors as the main bones of the financial market network and applying nonlinear factor analysis in the form of so-called PolyModel, this paper proposes two systemic risk indicators that can prognosticate the advent and trace the development of financial crises. Through financial network analysis, theoretical simulation, empirical data analysis and final validation, we argue that the indicators suggested in this paper are proved to be very effective in forecasting and tracing the financial crises from 1998 to 2017. The economic benefit of the indicator is evidenced by the enhancement of a protective put/covered call strategy on major stock markets.