Stable modeling in risk management

Paper one: “Value-at-risk: Recent advances ” (with Svetlozar Rachev). The Value-at-Risk (VaR) measurements are widely applied to estimate exposure to market risks. The traditional approaches to VaR computations do not provide satisfactory evaluation of possible losses. In this paper, we review the recent advances in the VaR methodologies. The proposed improvements still lack a convincing unified technique capturing the observed phenomena in financial data such as heavy-tails, time-varying volatility, and short- and long-range dependence. We suggest using stable Paretian distributions in VaR modeling.; Paper two: “Stable modeling of value-at-risk ” (with Svetlozar Rachev and Eduardo Schwartz). In this paper, we analyze the use of stable Paretian distributions in VaR modeling. The in-sample- and forecast-evaluations show that stable VAR modeling outperforms the normal modeling for high values of the VaR confidence level: the stable modeling generally produces conservative and accurate 99% VaR estimates; the normal method leads to overly optimistic forecasts of losses in the 99% VaR estimation.; Paper three: “Regulations and risk management in the Greek financial markets” (with Svetlozar Rachev and Bessy Athanasopoulos). We describe the supervisory authorities and the laws regulating operations of the Greek Financial Markets. The Basle Committee recommendations and the European Commission's Capital Adequacy Directive are applied to any model developed internally by institutions for statistical measurement of potential market losses. The Value-at-Risk (VaR) models are commonly applied to estimate exposure to market risks. We show superiority of the stable VaR modeling in evaluation of sensitivity to market risks in Greek financial markets.; Paper four: “Stable modeling of credit risk” (with Svetlozar Rachev and Eduardo Schwartz). The paper examines stable distributions in modeling credit risk. It develops a new technique for estimating correlation, constructs a new method for simulating portfolio values, and assesses portfolio risk in various cases of credit instruments' distributions: independent, symmetric dependent, and skewed dependent. It constructs a one-factor model of credit risks, applies stable modeling to extracted credit risk spreads, and estimates portfolio credit risk. The suggested stable modeling can be employed for risk estimation of any financial assets, not only credit instruments, if their distributions are heavy-tailed and/or skewed.