User menu

Accès à distance ? S'identifier sur le proxy UCLouvain

Forecasting Value-at-Risk under Different Distributional Assumptions

Primary tabs

Braione, Manuela [UCL] Scholtes, Nicolas [FUNDP]

Financial asset returns are known to be conditionally heteroskedastic and generally non-normally distributed, fat-tailed and often skewed. These features must be taken into account to produce accurate forecasts of Value-at-Risk (VaR). We provide a comprehensive look at the problem by considering the impact that different distributional assumptions have on the accuracy of both univariate and multivariate GARCH models in out-of-sample VaR prediction. The set of analyzed distributions comprises the normal, Student, Multivariate Exponential Power and their corresponding skewed counterparts. The accuracy of the VaR forecasts is assessed by implementing standard statistical backtesting procedures used to rank the different specifications. The results show the importance of allowing for heavy-tails and skewness in the distributional assumption with the skew-Student outperforming the others across all tests and confidence levels.

Document type Article de périodique (Journal article) – Article de recherche
Access type Accès restreint
Publication date 2016
Language Anglais
Journal information "Econometrics" - Vol. 4, no.3, p. 1-27 (2016)
Peer reviewed yes
Publisher M D P I AG ((Switzerland) Basel)
issn 2225-1146
e-issn 2225-1146
Publication status Publié
Affiliation UCL - SSH/LIDAM - Louvain Institute of Data Analysis and Modeling in economics and statistics
Keywords Value-at-Risk ; forecast accuracy ; distributions ; backtesting
Links
  1. Engle Robert F., Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation, 10.2307/1912773
  2. Bollerslev Tim, Generalized autoregressive conditional heteroskedasticity, 10.1016/0304-4076(86)90063-1
  3. Angelidis Timotheos, Benos Alexandros, Degiannakis Stavros, The use of GARCH models in VaR estimation, 10.1016/j.stamet.2004.08.004
  4. Nelson Daniel B., Conditional Heteroskedasticity in Asset Returns: A New Approach, 10.2307/2938260
  5. Mittnik Stefan, Paolella Marc S., Conditional density and value-at-risk prediction of Asian currency exchange rates, 10.1002/1099-131x(200007)19:4<313::aid-for776>3.0.co;2-e
  6. Ding Zhuanxin, Granger Clive W.J., Engle Robert F., A long memory property of stock market returns and a new model, 10.1016/0927-5398(93)90006-d
  7. Kuester K., Value-at-Risk Prediction: A Comparison of Alternative Strategies, 10.1093/jjfinec/nbj002
  8. Longin François, Solnik Bruno, Is the correlation in international equity returns constant: 1960–1990?, 10.1016/0261-5606(94)00001-h
  9. Brooks, J. Risk, 2, 5 (2000)
  10. Bauwens Luc, Laurent Sébastien, Rombouts Jeroen V. K., Multivariate GARCH models: a survey, 10.1002/jae.842
  11. Giot Pierre, Laurent S�bastien, Value-at-risk for long and short trading positions, 10.1002/jae.710
  12. Santos A. A. P., Nogales F. J., Ruiz E., Comparing Univariate and Multivariate Models to Forecast Portfolio Value-at-Risk, 10.1093/jjfinec/nbs015
  13. Bauwens Luc, Laurent Sébastien, A New Class of Multivariate Skew Densities, With Application to Generalized Autoregressive Conditional Heteroscedasticity Models, 10.1198/073500104000000523
  14. Ley Christophe, Paindaveine Davy, Multivariate skewing mechanisms: A unified perspective based on the transformation approach, 10.1016/j.spl.2010.07.004
  15. Noureldin Diaa, Shephard Neil, Sheppard Kevin, Multivariate rotated ARCH models, 10.1016/j.jeconom.2013.10.003
  16. Krause Jochen, Paolella Marc, A Fast, Accurate Method for Value-at-Risk and Expected Shortfall, 10.3390/econometrics2020098
  17. Christoffersen Peter F., Evaluating Interval Forecasts, 10.2307/2527341
  18. Dumitrescu, Finance, 33, 79 (1995)
  19. Kupiec Paul H, Techniques for Verifying the Accuracy of Risk Measurement Models, 10.3905/jod.1995.407942
  20. Engle Robert F, Manganelli Simone, CAViaR : Conditional Autoregressive Value at Risk by Regression Quantiles, 10.1198/073500104000000370
  21. DeMiguel Victor, Garlappi Lorenzo, Uppal Raman, Optimal Versus Naive Diversification: How Inefficient is the 1/NPortfolio Strategy?, 10.1093/rfs/hhm075
  22. Tu Jun, Zhou Guofu, Markowitz meets Talmud: A combination of sophisticated and naive diversification strategies, 10.1016/j.jfineco.2010.08.013
  23. Brown Stephen J., Hwang Inchang, In Francis Haeuck, Why Optimal Diversification Cannot Outperform Naive Diversification: Evidence from Tail Risk Exposure, 10.2139/ssrn.2242694
  24. Fugazza Carolina, Guidolin Massimo, Nicodano Giovanna, Equally Weighted vs. Long-Run Optimal Portfolios : Equally Weighted vs. Long-Run Optimal Portfolios, 10.1111/eufm.12042
  25. Bauwens (2006)
  26. Engle Robert F., Kroner Kenneth F., Multivariate Simultaneous Generalized ARCH, 10.1017/s0266466600009063
  27. Barndorff-Nielsen O., Exponentially Decreasing Distributions for the Logarithm of Particle Size, 10.1098/rspa.1977.0041
  28. Paolella Marc S., Polak Paweł, COMFORT: A common market factor non-Gaussian returns model, 10.1016/j.jeconom.2015.02.041
  29. Fernández, J. Am. Stat. Assoc., 93, 359 (1998)
  30. Bollerslev Tim, Wooldridge Jeffrey M., Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances, 10.1080/07474939208800229
  31. Fang (1990)
  32. Solaro, Stat. Appl. II, 2, 25 (2004)
  33. Noureldin Diaa, Shephard Neil, Sheppard Kevin, Multivariate high-frequency-based volatility (HEAVY) models, 10.1002/jae.1260
  34. Christoffersen Peter F., Evaluating Interval Forecasts, 10.2307/2527341
  35. Kupiec Paul H, Techniques for Verifying the Accuracy of Risk Measurement Models, 10.3905/jod.1995.407942
  36. Campbell, J. Risk, 9, 1 (2006)
  37. Engle Robert F, Manganelli Simone, CAViaR : Conditional Autoregressive Value at Risk by Regression Quantiles, 10.1198/073500104000000370
  38. Christoffersen P., Backtesting Value-at-Risk: A Duration-Based Approach, 10.1093/jjfinec/nbh004
  39. Pajhede (2015)
Bibliographic reference Braione, Manuela ; Scholtes, Nicolas. Forecasting Value-at-Risk under Different Distributional Assumptions. In: Econometrics, Vol. 4, no.3, p. 1-27 (2016)
Permanent URL http://hdl.handle.net/2078.1/173816