Quantifying Downside Risk in Euro Area Stock Markets: A Value at Risk Study
Keywords:Euro Area, Downside risk, Stock Return, Financial Risk Management.
The present research paper aims to assess and quantify the downside risk of Euro Area stock markets using the Value at Risk (VaR) methodology over a substantial time frame spanning 26 years. The study employs daily closing price data from the main stock exchanges of Euro Area nations. Using the historical simulation method, VaR estimates are calculated for each country's stock index, providing valuable insights into market performance and risk levels during both normal and crisis periods. The non-parametric nature of the historical simulation approach is favored due to its flexibility in dealing with non-normal distribution data, making it suitable for this analysis. The findings reveal significant variations in downside risk among Euro Area countries. Certain nations consistently exhibit lower VaR estimates, indicating comparatively lower downside volatility and potential losses. These markets may prove attractive to risk-averse investors seeking stability during adverse market conditions. In contrast, some countries consistently demonstrate higher VaR estimates, signaling heightened downside risk, which may offer higher potential returns but may not align with risk-averse investors' preferences. During periods of crisis, certain Euro Area markets display a lower level of downside volatility, showcasing their resilience during turbulent times. This information can guide investors in constructing diversified portfolios that can withstand adverse market conditions. Additionally, policymakers can draw upon these findings to formulate targeted monetary policies to support financial markets during economic uncertainty. Overall, this study contributes valuable insights into downside risk and market performance in Euro Area stock markets, providing investors, policymakers, and financial participants with essential information to make informed decisions and navigate the complexities of global financial markets effectively.
Ammann, M. and Reich, C. (2001) ‘VaR for nonlinear financial instruments — linear approximation or full Monte Carlo?’, Financial Markets and Portfolio Management, 15(3), pp. 363–378. doi: 10.1007/s11408-001-0306-9.
Best, P. (1999) Implementing Value at Risk.
Chen, J. and Chen, D. H. (2004) ‘The Downside Risk and Equity Evaluation: Emerging Market Evidence’, Journal of Emerging Market Finance, 3(1), pp. 77–93. doi: 10.1177/097265270400300105.
Danielsson, J. (2011) Financial Risk Forecasting:The Theory and Practice of Forecasting Market Risk, with Implementation in R and Matlab.
Dowd, K. (1998) ‘Beyond value at risk: the new science of risk management’, p. 274.
Dowd, K. (2002) An Introduction to Market Risk Measurement.
Estrada, J. (2006) ‘Applied corporate finance’.
Jorion, P. (2007) ‘Value_at_Risk,_3rd_Ed.__The_New Benchmark for managing financial risk.’
Linsmeier, T. J. and Pearson, N. D. (1996) ‘Risk Measurement?: An Introduction to Value at Risk’, 6153(July).
Liu, Yuntong et al. (2022) ‘International stock market risk contagion during the COVID-19 pandemic’, Finance Research Letters, 45, p. 102145. doi: 10.1016/J.FRL.2021.102145.
Perignon, C. and Smith, D. R. (2009) ‘The Level and Quality of Value-at-Risk Disclosure by Commercial Banks’, SSRN Electronic Journal. doi: 10.2139/ssrn.952595.
Rizwan, M. S., Ahmad, G. and Ashraf, D. (2020) ‘Systemic risk: The impact of COVID-19’, Finance Research Letters, 36, p. 101682. doi: 10.1016/J.FRL.2020.101682.
Vlaar, P. J. G. (2000) ‘Value at risk models for Dutch bond portfolios’, 24.