Grant: Discovery Grants Program - Individual

<p>The key scientific objective of my research program is to further advance the methodology of, and train highly qualified personnel in, modeling and forecasting the behavior, reliability and resilience of various systems, such as</p> <p>a) economic (e.g. re-distribution of wealth and incomes among geographic regions)</p> <p>b) financial (e.g. construction of investment portfolios)</p> <p>c) industrial (e.g. reliability, survivability)</p> <p>d) insurance (e.g. risk measurement and management, trends, forecasting)</p> <p>Specifically, my research has been, and will continue, progressing in the following four intertwined directions:</p> <p>DIRECTION 1: Aggregation and allocation techniques for quantitative and qualitative risks. Such techniques are pivotal in risk measurement and management, as well as in decision making. Indeed, quite a number of risks (e.g. operational, strategic) originate from complex systems (e.g. co-operative, competing, vague, multi-state, etc.) and are observed by the researcher in a qualitative form that may be difficult to meaningfully quantify.</p> <p>DIRECTION 2: Risk-reward measurement and management techniques with and without sufficiently large data sets. Whereas in the ""with"" case I will work on developing appropriate parametric, semi- and non-parametric techniques of statistical inference, in the ""without"" case I will crucially rely on knowledge-based probabilities (e.g. behavioral analysis), calibration and updating techniques (e.g. information flows). </p> <p>DIRECTION 3: Risk-reward forecasting and decision-making by combining techniques of stochastic processes (e.g. empirical and quantile processes, non-linear time series) as well as those of the rapidly developing area of quantitative psychological heuristics (e.g. consumer behavioral analysis). The latter techniques allow decision-makers to relatively quickly assess and balance decision precision with real-life imposed time constraints.</p> <p>DIRECTION 4: Systemic-risk modeling, which plays a pivotal role in developing risk-reward aggregation and allocation methods, portfolio diversification and optimization techniques (e.g. stochastic dominance) in static and dynamic environments.</p> <p>All of these research directions make up an integral part of the nowadays rapidly developing integrated risk management approach that has been of paramount importance when dealing with business, economic, financial, and industrial systems, and has evolved into a predominant research trend nationally and internationally.</p>