Browsing by Subject "Value-at-Risk"

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Open Access
Extreme value theory and Value-at-Risk: relative performance in emerging markets
(Elsevier BV, 2004) Gençay, R.; Selçuk, F.
In this paper, we investigate the relative performance of Value-at-Risk (VaR) models with the daily stock market returns of nine different emerging markets. In addition to well-known modeling approaches, such as variance-covariance method and historical simulation, we study the extreme value theory (EVT) to generate VaR estimates and provide the tail forecasts of daily returns at the 0.999 percentile along with 95% confidence intervals for stress testing purposes. The results indicate that EVT-based VaR estimates are more accurate at higher quantiles. According to estimated Generalized Pareto Distribution parameters, certain moments of the return distributions do not exist in some countries. In addition, the daily return distributions have different moment properties at their right and left tails. Therefore, risk and reward are not equally likely in these economies. (C) 2004 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Open Access
Risk-averse multi-class support vector machines
(2018-12) Karagöz, Ayşenur
A classification problem aims to identify the class of new observations based on the previous observations whose classes are known. It has many applications in a variety of disciplines such as medicine, finance and artificial intelligence. However, presence of outliers and noise in previous observations may have significant impact on the classification performance. Support vector machine (SVM) is a classifier introduced to solve binary classification problems under the presence of noise and outliers. In the literature, risk-averse SVM is shown to be more stable to noise and outliers compared to the original SVM formulations. However, we often observe more than two classes in real-life datasets. In this study, we aim to develop riskaverse multi-class SVMs following the idea of risk-averse binary SVM. We use risk measures, VaR and CVaR, to implement risk-aversion to multi-class SVMs. Since VaR constraints are nonconvex in general, SVMs with VaR constraints are more complex than SVMs with CVaR. Therefore, we propose a strong big-M formulation to solve multi-class SVM problems with VaR constraints efficiently. We also provide a computational study on the classification performance of the original multi-class SVM formulations and the proposed risk-averse formulations using artificial and real-life datasets. The results show that multi-class SVMs with VaR are more stable to outliers and noise compared to multi-class SVMs with CVaR, and both of them are more stable than the original formulations.