This paper examines how the largest stock market of the world, the U.S., and particularly the S&P500 index, reacted during the COVID-19 outbreak (02.01.2020-30.04.2020). Using simple financial and corporate analysis (adopting Constant Growth Model) procedures for our theoretical framework, we...

Even though econometric and technological advances have contributed to the vast risk modelling literature, practitioners in most cases use the simplest and most conventional approaches in order to avoid algorithmic complexity and increased costs. In this study we present a new approach which...

The purpose of this paper is to suggest a new approach that improves the conventional Historical Value-at-Risk (HVaR) estimations' accuracy and can be easily applied by anyone. The main assumption for the newly suggested method is “the more representative to the financial conditions the data...

Accurate Value at Risk (VaR) estimations are crucial for the robustness and stability of a financial system. Even though significant advances have been made in the field of risk modelling, many crises have emerged during the same period, and an explanation for this is that the advanced models...

In this paper, we examine how Value at Risk (VaR) contributes to the financial market's stability. We apply the Guidelines on Risk Measurement and the Calculation of Global Exposure and Counterparty Risk for UCITS of the Committee of European Securities Regulators (CESR (2010)) to the main...

This study highlights some deficiencies of the stock markets’ risk legislation framework, and particularly the CESR (2010) guidelines. We show that the current legislative framework fails to offer incentives to financial management companies to invest in advanced models for more representative...

This study highlights some deficiencies of the stock markets' risk legislation framework, and particularly the CESR (2010) guidelines. We show that the current legislative framework fails to offer incentives to financial management companies to invest in advanced models for more representative...