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Yayın Failure of an exchange-rate-based stabilization plan in Turkey(M E Sharpe, 2003-02) Gökkent, Giyas; Moslares, Carlos; Amiel-Saenz, RafaelThe Turkish exchange-rate-based stabilization plan adopted in 2000 has been a spectacular failure, lasting a mere fourteen months despite a relatively flexible peg regime and preannounced exit strategy. The final three months of the currency regime were marred by the eruption of a banking sector crisis that quickly developed into a currency crisis, quelled only by external loans and a blanket guarantee by the sovereign of all banking sector liabilities. This was ultimately to no avail as the lira was allowed to float following a full-fledged currency crisis in late February 2001. The usual indicators of crisis did not point to imminent turmoil in November 2000 despite widespread concern about eventual dire developments. To identify the source of the November crisis, one must weigh the factors that led economic agents, and banks in particular, to expect higher interest rates after the fall.Yayın Comparison of evolutionary techniques for Value-at-Risk calculation(Springer-Verlag Berlin, 2007) Uludağ, Gönül; Etaner Uyar, Ayşe Şima; Senel, Kerem; Dağ, HasanThe Value-at-Risk (VaR) approach has been used for measuring and controlling the market risks in financial institutions. Studies show that the t-distribution is more suited to representing the financial asset returns in VaR calculations than the commonly used normal distribution. The frequency of extremely positive or extremely negative financial asset returns is higher than that is suggested by normal distribution. Such a leptokurtic distribution can better be approximated by a t-distribution. The aim of this study is to asses the performance of a real coded Genetic Algorithm (CA) with Evolutionary Strategies (ES) approach for Maximum Likelihood (ML) parameter estimation. Using Monte Carlo (MC) simulations, we compare the test results of VaR simulations using the t-distribution, whose optimal parameters are generated by the Evolutionary Algorithms (EAs), to that of the normal distribution. It turns out that the VaR figures calculated with the assumption of normal distribution significantly understate the VaR figures computed from the actual historical distribution at high confidence levels. On the other hand, for the same confidence levels, the VaR figures calculated with the assumption of t-distribution are very close to the results found using the actual historical distribution. Finally, in order to speed up the MC simulation technique, which is not commonly preferred in financial applications due to its time consuming algorithm, we implement a parallel version of it.
