The role of the information set for forecasting - with applications to risk management
AbstractPredictions are issued on the basis of certain information. If the forecasting mechanisms are correctly specified, a larger amount of available information should lead to better forecasts. For point forecasts, we show how the effect of increasing the information set can be quantified by using strictly consistent scoring functions, where it results in smaller average scores. Further, we show that the classical Diebold-Mariano test, based on strictly consistent scoring functions and asymptotically ideal forecasts, is a consistent test for the effect of an increase in a sequence of information sets on $h$-step point forecasts. For the value at risk (VaR), we show that the average score, which corresponds to the average quantile risk, directly relates to the expected shortfall. Thus, increasing the information set will result in VaR forecasts which lead on average to smaller expected shortfalls. We illustrate our results in simulations and applications to stock returns for unconditional versus conditional risk management as well as univariate modeling of portfolio returns versus multivariate modeling of individual risk factors. The role of the information set for evaluating probabilistic forecasts by using strictly proper scoring rules is also discussed.
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Bibliographic InfoPaper provided by arXiv.org in its series Papers with number 1404.7653.
Date of creation: Apr 2014
Date of revision:
Publication status: Published in Annals of Applied Statistics 2014, Vol. 8, No. 1, 595-621
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Web page: http://arxiv.org/
This paper has been announced in the following NEP Reports:
- NEP-ALL-2014-05-09 (All new papers)
- NEP-ETS-2014-05-09 (Econometric Time Series)
- NEP-FOR-2014-05-09 (Forecasting)
- NEP-RMG-2014-05-09 (Risk Management)
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- Francis X. Diebold, 2012.
"Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests,"
NBER Working Papers
18391, National Bureau of Economic Research, Inc.
- Francis X. Diebold, 2012. "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," PIER Working Paper Archive 12-035, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
- Diebold, Francis X & Mariano, Roberto S, 1995.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 13(3), pages 253-63, July.
- Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
- Tom Doan, . "DMARIANO: RATS procedure to compute Diebold-Mariano Forecast Comparison Test," Statistical Software Components RTS00055, Boston College Department of Economics.
- Jeremy Berkowitz & Peter Christoffersen & Denis Pelletier, 2011.
"Evaluating Value-at-Risk Models with Desk-Level Data,"
INFORMS, vol. 57(12), pages 2213-2227, December.
- Peter Christoffersen & Jeremy Berkowitz & Denis Pelletier, 2008. "Evaluating Value-at-Risk Models with Desk-Level Data," CREATES Research Papers 2009-35, School of Economics and Management, University of Aarhus.
- Jeremy Berkowitz & Peter Christoffersen & Denis Pelletier, 2005. "Evaluating Value-at-Risk models with desk-level data," Working Paper Series 010, North Carolina State University, Department of Economics, revised Dec 2006.
- repec:taf:jnlbes:v:30:y:2012:i:1:p:1-17 is not listed on IDEAS
- Newey, Whitney & West, Kenneth, 2014.
"A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix,"
Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
- Newey, Whitney K & West, Kenneth D, 1987. "A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix," Econometrica, Econometric Society, vol. 55(3), pages 703-08, May.
- Whitney K. Newey & Kenneth D. West, 1986. "A Simple, Positive Semi-Definite, Heteroskedasticity and AutocorrelationConsistent Covariance Matrix," NBER Technical Working Papers 0055, National Bureau of Economic Research, Inc.
- Tsyplakov, Alexander, 2011. "Evaluating density forecasts: a comment," MPRA Paper 31184, University Library of Munich, Germany.
- Giacomini, Raffaella & White, Halbert, 2003.
"Tests of Conditional Predictive Ability,"
University of California at San Diego, Economics Working Paper Series
qt5jk0j5jh, Department of Economics, UC San Diego.
- Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-50, July.
- Acerbi, Carlo & Tasche, Dirk, 2002.
"On the coherence of expected shortfall,"
Journal of Banking & Finance,
Elsevier, vol. 26(7), pages 1487-1503, July.
- Tae-Hwy Lee & Yong Bao & Burak Saltoglu, 2006. "Evaluating predictive performance of value-at-risk models in emerging markets: a reality check," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(2), pages 101-128.
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