, such as Multivariate DCC-GARCH and Continuous Wavelet Transform (CWT) to test the research objective. The period of …

The Value-at-Risk (VaR) metric serves as a pivotal tool for quantifying market risk, offering an estimation of …, particularly during crises and heightened uncertainty phases. Phenomena like volatility clustering impinge on the accuracy of these … models. To mitigate such constraints, conditional volatility models are integrated to augment the robustness and adaptability …

portfolios formed of hundreds or thousands of stocks, for the scope of volatility (and therefore risk) forecasting, PCGARCH is …The thesis proposes to assess the risk topic in the context of foreign investment decisions. In identifying two main … risk-related concepts, I have split risks in two categories using a unique criterion: the ratio between the endogenous and …

volatility prediction models is to accurately estimate volatility. In this study, MA, EWMA, GARCH (1,1) and IGARCH models have … other market players aspire to know whether there is volatility in the market and to determine the structure of such … fluctuations in case they exist. In addition to this, the accurate volatility estimation models are required to be able to conduct …

quality of volatility forecast provided by GARCH when compared to any other alternative model, without considering any cost … more advanced and complex models. Ultimately, its scope is to offer support for the rationale behind of an idea: GARCH is … the most appropriate model to use when one has to evaluate the volatility of the returns of groups of stocks with large …

intervention. Based on a GARCH framework and change point detection, we test for a structural break in the effectiveness of … volatility at the turn of the millennium when Japanese foreign exchange intervention started to remain unsterilized. …