Value at Risk and expected shortfall of firms in the main European Union stock market indexes: A detailed analysis by economic sectors and geographical situation

We have analyzed extreme movements of the main stocks traded in the Eurozone in the 2000–2012 period. Our results can help future very-risk-averse investors to choose their portfolios in the Eurozone for risk management purposes. We find two main results. First, we can clearly classify firms by economic sector according to their different estimated VaR values in five of the seven countries we analyze. Specially, we find sectors in general where companies have very high (telecommunications and banking) and very low (petroleum, utilities, energy and consumption) estimated VaR values. Second, we only find differences according to the geographical situation of where the stocks are traded in two countries: (1) all firms in the Irish stock market (the only financially rescued country we analyze) have very high estimated VaR values in all sectors; while (2) in Spain all firms have very low estimated VaR values included in the banking and the telecommunication sectors. All our results are supported when we also study the expected shortfall of the firms.

[1]  T. V. Ramanathan,et al.  Parametric and non-parametric estimation of value-at-risk , 2009 .

[2]  B. Christensen,et al.  Semiparametric Inference in a GARCH-in-Mean Model , 2008 .

[3]  E. Iglesias,et al.  Extreme movements of the main stocks traded in the Eurozone: an analysis by sectors in the 2000's decade , 2012 .

[4]  Christos S. Savva,et al.  Periodic Dynamic Conditional Correlations between Stock Markets in Europe and the US , 2008 .

[5]  Meryem Duygun Fethi,et al.  Value-at-Risk models and Basel capital charges: Evidence from Emerging and Frontier stock markets , 2012 .

[6]  P. Narayan,et al.  Firm heterogeneity and calendar anomalies , 2011 .

[7]  Suleyman Basak,et al.  Value-at-Risk Based Risk Management: Optimal Policies and Asset Prices , 1999 .

[8]  ESTIMATION OF THE MAXIMAL MOMENT EXPONENT OF A GARCH(1,1) SEQUENCE , 2003, Econometric Theory.

[9]  Kevin Dowd,et al.  A Value at Risk Approach to Risk-Return Analysis , 1999 .

[10]  J. Urbain,et al.  Common stochastic trends in European stock markets , 1993 .

[11]  W. Fuller,et al.  Distribution of the Estimators for Autoregressive Time Series with a Unit Root , 1979 .

[12]  G. Box,et al.  On a measure of lack of fit in time series models , 1978 .

[13]  Mike K. P. So,et al.  Estimation of multiple period expected shortfall and median shortfall for risk management , 2009 .

[14]  T. Ñíguez.  Volatility and VaR forecasting in the Madrid Stock Exchange , 2008 .

[15]  Susan Sunila Sharma,et al.  New evidence on oil price and firm returns , 2011 .

[16]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[17]  Bora Aktan,et al.  Time‐varying volatility modelling of Baltic stock markets , 2010 .

[18]  Haim Levy,et al.  Is There an Intertemporal Relation between Downside Risk and Expected Returns? , 2009, Journal of Financial and Quantitative Analysis.

[19]  Haim Levy,et al.  Value-at-Risk Capital Requirement Regulation, Risk Taking and Asset Allocation: A Mean-Variance Analysis , 2015 .

[20]  Stephen L Taylor,et al.  Modelling Financial Time Series , 1987 .

[21]  Masaaki Kijima,et al.  On the significance of expected shortfall as a coherent risk measure , 2005 .

[22]  O. Linton,et al.  Estimation of Tail Thickness Parameters from GJR-GARCH Models , 2009 .

[23]  Susan Sunila Sharma,et al.  Oil price and stock returns of consumers and producers of crude oil , 2015 .

[24]  P. Phillips.  Testing for a Unit Root in Time Series Regression , 1988 .

[25]  Ross Maller,et al.  Bias and consistency of the maximum Sharpe ratio , 2005 .

[26]  Jonathan B. Hill.  ON TAIL INDEX ESTIMATION FOR DEPENDENT, HETEROGENEOUS DATA , 2010, Econometric Theory.

[27]  Are fundamentals still relevant for European economies in the post-Euro period? , 2009 .

[28]  Adrian Pagan,et al.  Estimating the Density Tail Index for Financial Time Series , 1997, Review of Economics and Statistics.

[29]  P. Narayan,et al.  New evidence on turn-of-the-month effects , 2014 .

[30]  T. Mikosch,et al.  Limit theory for the sample autocorrelations and extremes of a GARCH (1,1) process , 2000 .

[31]  Susan Sunila Sharma,et al.  Firm return volatility and economic gains: The role of oil prices , 2014 .

[32]  Christian Francq,et al.  MIXING PROPERTIES OF A GENERAL CLASS OF GARCH(1,1) MODELS WITHOUT MOMENT ASSUMPTIONS ON THE OBSERVED PROCESS , 2006, Econometric Theory.

[33]  P. Narayan,et al.  Spread determinants and the day-of-the-week effect , 2014 .

[34]  J. Poterba,et al.  What moves stock prices? , 1988 .

[35]  Alexandre M. Baptista,et al.  Portfolio Performance Evaluation Using Value at Risk , 2003 .

[36]  Measuring tail thickness under GARCH and an application to extreme exchange rate changes , 2005 .

[37]  D. Teresienė.  Lithuanian stock market analysis using a set of Garch models , 2009 .

[38]  Dennis W. Jansen,et al.  On the Frequency of Large Stock Returns: Putting Booms and Busts into Perspective , 1989 .

[39]  Peter Christoffersen,et al.  Série Scientifique Scientific Series Estimation Risk in Financial Risk Management , 2022 .

[40]  Her-Jiun Sheu,et al.  Systemic Risk in Taiwan Stock Market , 2012 .

[41]  Lennart F. Hoogerheide,et al.  GARCH Models for Daily Stock Returns: Impact of Estimation Frequency on Value-at-Risk and Expected Shortfall Forecasts , 2014 .

[42]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[43]  B. M. Hill,et al.  A Simple General Approach to Inference About the Tail of a Distribution , 1975 .

[44]  E. Iglesias.  An analysis of extreme movements of exchange rates of the main currencies traded in the Foreign Exchange market , 2012 .

[45]  L. Glosten,et al.  On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks , 1993 .